From 32fe61839bfc0b74d68f8e2803f4693edc36d22c Mon Sep 17 00:00:00 2001 From: MateusStano Date: Mon, 22 Jun 2026 21:54:06 -0300 Subject: [PATCH 01/22] ENH: first aero refactor draft --- .../center_of_pressure_and_stability.rst | 398 ++++++++++ .../aerodynamics/elliptical_fins.rst | 11 - .../aerodynamics/individual_fins.rst | 8 - .../technical/aerodynamics/roll_equations.rst | 30 - docs/technical/index.rst | 1 + rocketpy/__init__.py | 1 + rocketpy/plots/aero_surface_plots.py | 86 +++ rocketpy/plots/flight_plots.py | 110 ++- rocketpy/plots/rocket_plots.py | 261 ++++++- rocketpy/prints/aero_surface_prints.py | 54 +- rocketpy/prints/flight_prints.py | 21 + rocketpy/prints/rocket_prints.py | 25 +- rocketpy/rocket/__init__.py | 1 + rocketpy/rocket/aero_surface/__init__.py | 3 + .../rocket/aero_surface/_barrowman_surface.py | 120 +++ .../rocket/aero_surface/aero_coefficient.py | 229 ++++++ rocketpy/rocket/aero_surface/aero_surface.py | 187 +---- rocketpy/rocket/aero_surface/air_brakes.py | 107 ++- .../controllable_generic_surface.py | 162 ++++ .../rocket/aero_surface/fins/_base_fin.py | 38 +- rocketpy/rocket/aero_surface/fins/fin.py | 50 ++ rocketpy/rocket/aero_surface/fins/fins.py | 69 +- .../aero_surface/fins/trapezoidal_fin.py | 1 - .../rocket/aero_surface/generic_surface.py | 380 ++++++--- .../aero_surface/linear_generic_surface.py | 270 ++++--- rocketpy/rocket/aero_surface/nose_cone.py | 18 +- rocketpy/rocket/aero_surface/rail_buttons.py | 64 +- rocketpy/rocket/aero_surface/tail.py | 18 +- rocketpy/rocket/rocket.py | 718 +++++++++++++++++- rocketpy/simulation/flight.py | 285 ++++++- .../simulation/helpers/flight_derivatives.py | 168 ++-- 31 files changed, 3142 insertions(+), 752 deletions(-) create mode 100644 docs/technical/aerodynamics/center_of_pressure_and_stability.rst create mode 100644 rocketpy/rocket/aero_surface/_barrowman_surface.py create mode 100644 rocketpy/rocket/aero_surface/aero_coefficient.py create mode 100644 rocketpy/rocket/aero_surface/controllable_generic_surface.py diff --git a/docs/technical/aerodynamics/center_of_pressure_and_stability.rst b/docs/technical/aerodynamics/center_of_pressure_and_stability.rst new file mode 100644 index 000000000..42f281cab --- /dev/null +++ b/docs/technical/aerodynamics/center_of_pressure_and_stability.rst @@ -0,0 +1,398 @@ +.. _aero_cp_stability: + +========================================================== +Aerodynamics: Coefficients, Centers and Stability +========================================================== + +:Author: RocketPy Team +:Date: June 2026 + +Introduction +============ + +This document describes how RocketPy models aerodynamic forces and moments and +how the rocket's stability quantities — the **aerodynamic center**, the +**center of pressure**, the **static** and **stability margins**, and the +**dynamic-stability** parameters — are derived from them. + +The model is built as a strict set of layers, each derived only from the one +below it: + +#. **Surface coefficients** — every aerodynamic surface exposes the six + dimensionless aerodynamic coefficients. +#. **Rocket aggregate** — the surfaces are summed into the rocket's total force + and moment. +#. **Stability references** — the *aerodynamic center* (linear) and the + *center of pressure* (nonlinear). +#. **Margins** — the linear (aerodynamic-center) and realized (center-of- + pressure) stability margins. +#. **Dynamic stability** — the linearized attitude oscillator. + +Since the aerodynamic-surface refactor, :class:`rocketpy.GenericSurface` is the +**root of the aerodynamic-surface hierarchy**: nose cones, fin sets, individual +fins, tails/transitions and air brakes are all described by the same coefficient +set and computed through a single coefficient-based force-and-moment model. The +geometric (Barrowman) surfaces translate their geometry into those same +coefficients (see :ref:`barrowman_mapping`), so the rocket knows the full +aerodynamic coefficient set for every surface. + +.. note:: + The legacy ``AeroSurface`` base class is deprecated. It is retained only as a + compatibility shim: :class:`rocketpy.GenericSurface` is registered as a + virtual subclass, so ``isinstance(surface, AeroSurface)`` still returns + ``True``. + +Layer 0 — The aerodynamic coefficient model +============================================ + +A generic aerodynamic surface is defined by six dimensionless coefficients, +each a function of a set of independent variables: + +- force coefficients: lift :math:`C_L`, side force :math:`C_Q`, drag :math:`C_D`; +- moment coefficients: pitch :math:`C_m`, yaw :math:`C_n`, roll :math:`C_l`. + +The standard independent variables are the angle of attack :math:`\alpha`, the +sideslip angle :math:`\beta`, the Mach number :math:`M`, the Reynolds number +:math:`Re`, and the body angular rates (pitch :math:`q`, yaw :math:`r`, roll +:math:`p`): + +.. math:: + + C_i = C_i(\alpha,\ \beta,\ M,\ Re,\ q,\ r,\ p) + +Subclasses may append extra axes — control deflections for +:class:`rocketpy.ControllableGenericSurface`, or the unsteady terms +:math:`\dot\alpha,\ \dot\beta` when ``unsteady_aero=True``. + +Forces and moments of a surface +------------------------------- + +At each step the surface receives the freestream velocity in the body frame. +Reversing it into the standard aerodynamic frame, the incidence angles are + +.. math:: + + \alpha = \operatorname{atan2}(-v_y,\ -v_z), \qquad + \beta = \operatorname{atan2}(-v_x,\ -v_z) + +With the dynamic pressure times reference area +:math:`\bar q A = \tfrac{1}{2}\rho V^2 A_\text{ref}`, the aerodynamic force +:math:`(Q, -L, -D)` is rotated from the aerodynamic frame into the body frame, +giving :math:`\mathbf{R}=(R_1, R_2, R_3)`, and the moment about the rocket's +center of dry mass is + +.. math:: + :label: moment_transport + + \mathbf{M} = \bar q A L_\text{ref}\,(C_m, C_n, C_l) + + \mathbf{r}_\text{cp} \times \mathbf{R} + +The first term is the couple carried by the moment coefficients; the second +transports the resultant force from its application point +:math:`\mathbf{r}_\text{cp}` to the center of dry mass. This is implemented in +:meth:`rocketpy.GenericSurface.compute_forces_and_moments`. + +Layer 1 — Rocket aggregate +========================== + +The simulation, and every stability quantity below, sums the surfaces into the +rocket's total body-frame force and moment about the center of dry mass. The +nonlinear aggregate at a given state is +:meth:`rocketpy.Rocket._aerodynamic_forces_and_moments`; the dimensionless +totals are exposed by :meth:`rocketpy.Rocket.aerodynamic_coefficients` (total +normal-force coefficient :math:`C_N` and pitch-moment coefficient :math:`C_m`). +The **linear** aggregate — the normal-force-curve slope and the +slope-weighted positions — is built by +:meth:`rocketpy.Rocket.evaluate_center_of_pressure` (see Layer 2). + +Layer 2 — Aerodynamic center vs. center of pressure +=================================================== + +These two are the heart of the model and are frequently confused. They are the +same physics in two regimes. + +Aerodynamic center (linear) +--------------------------- + +The **aerodynamic center** (AC) is the *linearized*, small-incidence +(:math:`\alpha=\beta=0`) location about which the pitching moment is independent +of angle of attack: + +.. math:: + :label: ac + + x_\text{AC}(M) = x_\text{ref} + - \frac{\partial C_m/\partial\alpha}{\partial C_N/\partial\alpha}\,L_\text{ref} + +It is well-conditioned, a function of Mach alone, and is the classical reference +that the static margin is built on. At the rocket level it is the +normal-force-slope-weighted average of the component locations, + +.. math:: + :label: rocket_ac + + x_\text{AC,rocket}(M) = + \frac{\sum_i k_i\, C_{N,\alpha,i}(M)\,\big(p_i - c\, z_{\text{cp},i}(M)\big)} + {\sum_i k_i\, C_{N,\alpha,i}(M)} + +with the area-correction factor :math:`k_i = A_{\text{ref},i}/A_\text{rocket}`, +:math:`p_i` the surface position and :math:`c=\pm 1` the coordinate-system +orientation. Because the weight is the normal-force slope, a zero-lift surface +(e.g. a pure-drag element) drops out cleanly. This is computed by +:meth:`rocketpy.Rocket.evaluate_center_of_pressure` and stored as +``Rocket.aerodynamic_center``. + +.. note:: + ``Rocket.cp_position`` is a **deprecated alias** for + ``Rocket.aerodynamic_center``. The historical "center of pressure" attribute + was always the aerodynamic center; the alias is kept (with a + ``DeprecationWarning``) for backward compatibility. + +Center of pressure (nonlinear) +------------------------------ + +The **center of pressure** (CP) is the point at which the *actual* resultant +aerodynamic force acts with no residual moment, at a finite angle of +attack/sideslip: + +.. math:: + :label: cp + + x_\text{CP}(\alpha,\beta,M,Re) = + x_\text{cdm} + c\,\frac{M_2 R_1 - M_1 R_2}{R_1^2 + R_2^2} + +evaluated from the Layer-1 aggregate (:math:`M = r\times F`). Unlike the AC, the +CP **moves with incidence**. It is a :math:`0/0` limit at zero incidence and +converges to the AC as :math:`\alpha,\beta \to 0`. This is +:meth:`rocketpy.Rocket.center_of_pressure`. + +To stay well-conditioned, ``center_of_pressure`` returns the aerodynamic-center +limit below ~1° of total incidence — blended between the pitch and yaw planes by +the direction of incidence (:meth:`rocketpy.Rocket._aerodynamic_center_limit`) — +so it is continuous and never spikes as the rocket oscillates through zero +incidence. The design-time travel is exposed by +``center_of_pressure_over_alpha`` and ``center_of_pressure_over_beta``. + +Pitch and yaw planes +-------------------- + +Because :class:`rocketpy.GenericSurface` allows **non-axisymmetric** rockets, the +*linear* AC is computed independently for the two planes: + +- pitch (``aerodynamic_center``) from :math:`\partial C_L/\partial\alpha` and + :math:`C_m`; +- yaw (``aerodynamic_center_yaw``) from the side-force slope and :math:`C_n`. + +They coincide for an axisymmetric rocket; ``Rocket.is_axisymmetric`` reports +whether they agree (to caliber tolerance) and +:meth:`rocketpy.Rocket.evaluate_center_of_pressure` warns when they do not, since +the scalar ``static_margin``/``stability_margin`` then describe the pitch plane +only. The **nonlinear** CP needs no such split — evaluated at the actual combined +incidence, a single axial location already captures both planes. + +Layer 3 — Static and stability margins +====================================== + +A margin is the longitudinal center-of-mass-to-stability-reference distance in +calibers (rocket diameters). With the center of mass :math:`z_\text{cm}(t)`, the +rocket radius :math:`R` and the orientation factor :math:`c`, there are **two +co-equal families**: + +**Linear (aerodynamic-center) margins.** Built on the AC; well-conditioned and +never spiking. The conventional design parameters: + +.. math:: + :label: static_margin + + \text{static margin}(t) = c\,\frac{z_\text{cm}(t) - x_\text{AC}(0)}{2R}, + \qquad + \text{stability margin}(M, t) = c\,\frac{z_\text{cm}(t) - x_\text{AC}(M)}{2R} + +The static margin (:meth:`rocketpy.Rocket.evaluate_static_margin`) is the +incompressible (:math:`M=0`) limit, a function of time; the stability margin +(:meth:`rocketpy.Rocket.evaluate_stability_margin`) is a function of Mach and +time. The ``*_yaw`` counterparts use ``aerodynamic_center_yaw``. + +**Realized (center-of-pressure) margin.** Built on the nonlinear CP at the +actual flight incidence, it reflects how the stability reference travels with +:math:`\alpha,\beta` (and combines the planes for a non-axisymmetric rocket). + +At the :class:`rocketpy.Flight` level: + +- ``Flight.stability_margin`` evaluates the **linear** margin along the realized + Mach and time — smooth, conventional, and the source of + ``initial_stability_margin`` / ``out_of_rail_stability_margin`` / + ``min_stability_margin`` / ``max_stability_margin``; +- ``Flight.realized_stability_margin`` evaluates the **nonlinear** CP at the + realized :math:`\alpha,\beta,M,Re`, falling back to the linear margin only at + negligible dynamic pressure (rail, rest, apogee), where the realized incidence + is meaningless. + +A positive margin (stability reference behind the center of mass) is the classic +passive-stability condition. + +Layer 4 — Dynamic stability +=========================== + +A static margin only gives the *sign* of the restoring moment. The actual +attitude response is the linearized pitch (or yaw) oscillator + +.. math:: + + I_L\,\ddot\theta + C_2\,\dot\theta + C_1\,\theta = 0 + +with the **corrective moment coefficient** (restoring moment per radian), + +.. math:: + + C_1 = \bar q\, A_\text{ref}\, C_{N,\alpha}\, (z_\text{cm} - x_\text{AC}), + +the **damping moment coefficient** (aerodynamic plus jet damping), + +.. math:: + + C_2 = \tfrac{1}{2}\rho V A_\text{ref} \sum_i k_i\,C_{N,\alpha,i}\,(x_i - z_\text{cm})^2 + \;+\; \dot m\,(x_\text{nozzle} - z_\text{cm})^2, + +and the lateral moment of inertia about the instantaneous center of mass +:math:`I_L`. From these, + +.. math:: + + \omega_n = \sqrt{C_1/I_L}, \qquad \zeta = \frac{C_2}{2\sqrt{C_1\,I_L}}. + +These are exposed on :class:`rocketpy.Flight` as +``corrective_moment_coefficient``, ``damping_moment_coefficient``, +``pitch_natural_frequency``, ``pitch_damping_ratio`` and the ``yaw_*`` +counterparts. :math:`\zeta < 1` is an underdamped (oscillatory) response; +RocketPy also exposes the empirical FFT ``attitude_frequency_response`` as a +cross-check. + +.. note:: + **Roll has no natural frequency.** A conventional rocket has no aerodynamic + roll-restoring moment, so roll is *neutrally stable* (a first-order system: + fin-cant forcing balanced by roll damping, spinning up to a steady rate). + The roll-pitch/yaw coupling of concern is **roll resonance** ("roll + lock-in"): when the roll rate crosses the pitch/yaw natural frequency, the + spin couples into the attitude oscillation and the amplitude can diverge. + ``Flight.plots.dynamic_stability_data`` therefore overlays the roll rate (as + a frequency) on the natural-frequency plot — the crossings are the points to + watch. + +Quick reference +=============== + +.. list-table:: + :header-rows: 1 + :widths: 32 18 50 + + * - Attribute + - Variables + - Meaning + * - ``Rocket.aerodynamic_center`` (``_yaw``) + - :math:`M` + - Linear (small-incidence) center of pressure; static-margin reference. + ``cp_position`` is a deprecated alias. + * - ``Rocket.center_of_pressure(α, β, M, Re)`` + - :math:`\alpha,\beta,M,Re` + - Nonlinear CP at finite incidence; combines both planes. + * - ``Rocket.center_of_pressure_over_{alpha,beta}`` + - :math:`\alpha` / :math:`\beta` + - CP travel sweep (design time). + * - ``Rocket.aerodynamic_coefficients(α, β, M, Re)`` + - :math:`\alpha,\beta,M,Re` + - Total :math:`C_N`, :math:`C_m` about the center of dry mass. + * - ``Rocket.static_margin`` (``_yaw``) + - :math:`t` + - Linear margin at :math:`M=0` (calibers). + * - ``Rocket.stability_margin`` (``_yaw``) + - :math:`M, t` + - Linear margin vs Mach and time (calibers). + * - ``Rocket.stability_margin_over_{alpha,beta}`` + - :math:`\alpha` / :math:`\beta` + - Nonlinear margin travel sweep (design time). + * - ``Flight.stability_margin`` + - :math:`t` + - Linear margin along the realized Mach(t) — smooth. + * - ``Flight.realized_stability_margin`` + - :math:`t` + - Nonlinear margin at the realized incidence. + * - ``Flight.{pitch,yaw}_natural_frequency`` + - :math:`t` + - Attitude oscillation natural frequency :math:`\omega_n`. + * - ``Flight.{pitch,yaw}_damping_ratio`` + - :math:`t` + - Attitude oscillation damping ratio :math:`\zeta`. + * - ``Flight.{corrective,damping}_moment_coefficient`` + - :math:`t` + - Oscillator coefficients :math:`C_1`, :math:`C_2`. + +Visualizing stability +===================== + +- ``Rocket.plots.stability_margin`` — linear margin vs Mach and time (surface). +- ``Rocket.plots.stability_margin_over_alpha`` / ``_over_beta`` — nonlinear + margin travel with incidence (yaw sweep shown when non-axisymmetric). +- ``Rocket.plots.aerodynamic_coefficients`` — :math:`C_N`, :math:`C_m` vs + :math:`\alpha`; ``drag_curves`` for :math:`C_D` vs Mach. +- ``Flight.plots.stability_and_control_data`` — linear and realized margin vs + time, plus the FFT frequency response. +- ``Flight.plots.dynamic_stability_data`` — natural frequency and damping ratio + vs time (pitch and yaw). + +For non-axisymmetric rockets, ``Rocket.plots.all`` / ``Rocket.all_info`` also +draw both the pitch (``xz``) and yaw (``yz``) planes and the yaw-plane margins. + +.. _barrowman_mapping: + +Mapping Barrowman surfaces to coefficients +========================================== + +The geometric surfaces expose a lift-curve slope :math:`C_{N,\alpha}(M)` +(``clalpha``), a geometric cp :math:`z_\text{cp}` and — for fins — roll +forcing/damping. These are translated into the linear coefficient model: + +.. math:: + + C_{L,\alpha} = C_{N,\alpha}, \qquad + C_{Q,\beta} = -C_{N,\alpha} + +.. math:: + + C_{m,\alpha} = -C_{N,\alpha}\,\frac{z_\text{cp}}{L_\text{ref}}, \qquad + C_{n,\beta} = +C_{N,\alpha}\,\frac{z_\text{cp}}{L_\text{ref}} + +For an **individual fin** at angular position :math:`\phi`, the lift only resists +incidence in its own plane, so its slope is projected onto the two planes — +:math:`\sin^2\phi` to the pitch plane and :math:`\cos^2\phi` to the yaw plane. +An evenly spaced set of :math:`n` fins sums to :math:`n/2` in each plane, +reproducing the axisymmetric fin-set result; a one-plane layout (e.g. canards at +:math:`0^\circ/180^\circ`) makes the pitch- and yaw-plane aerodynamic centers +differ. + +For fin sets, the cant-angle roll forcing and roll damping add + +.. math:: + + C_{l,0} = C_{lf,\delta}(M)\,\delta, \qquad + C_{l,p} = C_{ld,\omega}(M) + +where :math:`\delta` is the cant angle. With this mapping the geometric surfaces +reproduce the Barrowman lift and roll behavior while flowing through the same +generic coefficient path as every other surface. + +.. note:: + The independent :math:`\alpha,\ \beta` decomposition of the linear model + coincides with the classical single-plane Barrowman projection to first + order and diverges only at large combined angle of attack, where the + underlying linear coefficients are themselves no longer valid; the nonlinear + :meth:`rocketpy.Rocket.center_of_pressure` captures that regime. + +References +========== + +The Barrowman method and its coefficients are described in [Barrowman]_ and +[Niskanen]_. The dynamic-stability oscillator (corrective and damping moment +coefficients, natural frequency and damping ratio) follows [Niskanen]_. See also +the :ref:`individual_fins` and roll-moment technical documents for the fin +derivations. diff --git a/docs/technical/aerodynamics/elliptical_fins.rst b/docs/technical/aerodynamics/elliptical_fins.rst index 5ff5c4ee9..3b1cb113d 100644 --- a/docs/technical/aerodynamics/elliptical_fins.rst +++ b/docs/technical/aerodynamics/elliptical_fins.rst @@ -1,14 +1,3 @@ -========================= -Elliptical Fins Equations -========================= - -:Author: Mateus Stano Junqueira, -:Author: Franz Masatoshi Yuri, -:Author: Kaleb Ramos Wanderley Santos, -:Author: Matheus Gonçalvez Doretto, -:Date: February 2022 - - Nomenclature ============ diff --git a/docs/technical/aerodynamics/individual_fins.rst b/docs/technical/aerodynamics/individual_fins.rst index 408352e16..d8cb7a100 100644 --- a/docs/technical/aerodynamics/individual_fins.rst +++ b/docs/technical/aerodynamics/individual_fins.rst @@ -4,9 +4,6 @@ Individual Fin Model ==================== -:Author: Mateus Stano Junqueira -:Date: March 2025 - Introduction ============ @@ -491,8 +488,3 @@ rocket: # Angle of sideslip test_flight.angle_of_sideslip.plot(test_flight.out_of_rail_time, 5) - - - - - diff --git a/docs/technical/aerodynamics/roll_equations.rst b/docs/technical/aerodynamics/roll_equations.rst index 8ad106b60..b35f1de68 100644 --- a/docs/technical/aerodynamics/roll_equations.rst +++ b/docs/technical/aerodynamics/roll_equations.rst @@ -1,11 +1,3 @@ -======================================= -Roll equations for high-powered rockets -======================================= - -:Author: Bruno Abdulklech Sorban, -:Author: Mateus Stano Junqueira -:Date: February 2022 - Nomenclature ============ @@ -236,25 +228,3 @@ For the damping moment lift coefficient derivative: .. math:: (C_{lf\delta})_{K_{f}} = K_{f} \cdot C_{lf\delta} .. math:: (C_{ld\omega})_{K_{d}} = K_{d} \cdot C_{ld\omega} - -Comments -======== - -Roll moment is expected to increase linearly with velocity. This -relationship can be verified in the rotation frequency equilibrium -equation, described by [Niskanen]_ in equation -(3.73), and again stated below: - -.. math:: f_{eq} = \frac{\omega}{2\pi} = \frac{A_{ref}\beta \overline{Y_t} (C_{N\alpha})_1 }{4\pi^2 \sum_{i} c_i \xi^2 \Delta \xi} \, \delta V_0 - -The auxiliary value :math:`\beta` is defined as: -:math:`\beta = \sqrt{|1-M|}`, where M is the speed of the rocket in -Mach. - -.. .. math:: k = 1 + \frac{\frac{\sqrt{s^2-r_{t}^2}\Bigl(2C_{r}r_{t}^2\ln\Bigl(\frac{2s\sqrt{s^2-r_{t}^2}+2s^2}{r_{t}}\Bigr)-2C_{r}r_{t}^2\ln\Bigl(2s\Bigr)\Bigr)+2C_{r}s^3-{\pi}C_{r}r_{t}s^2-2C_{r}r_{t}^2s+{\pi}C_{r}r_{t}^3}{2r_{t}s^3-2r_{t}^3s}}{C_{r}\cdot\Bigl(\dfrac{s^2}{3}+\dfrac{{\pi}r_{t}s}{4}\Bigr)} - -.. .. math:: - -.. k = 1 + \frac{\sqrt{s^2-r_{t}^2}\Bigl(2r_{t}^2\ln\Bigl(\frac{2s\sqrt{s^2-r_{t}^2}+2s^2}{r_{t}}\Bigr)-2r_{t}^2\ln\Bigl(2s\Bigr)\Bigr)+2s^3-{\pi}r_{t}s^2-2r_{t}^2s+{\pi}r_{t}^3} -.. {(2r_{t}s^3-2r_{t}^3s) \cdot\Bigl(\dfrac{s^2}{3}+\dfrac{{\pi}r_{t}s}{4}\Bigr)} - diff --git a/docs/technical/index.rst b/docs/technical/index.rst index bb49ac56c..050c366e0 100644 --- a/docs/technical/index.rst +++ b/docs/technical/index.rst @@ -12,6 +12,7 @@ in their code. Equations of Motion v0 Equations of Motion v1 + Aerodynamics, Center of Pressure and Margins Elliptical Fins Individual Fin Roll Moment diff --git a/rocketpy/__init__.py b/rocketpy/__init__.py index ae602d784..42024fb9d 100644 --- a/rocketpy/__init__.py +++ b/rocketpy/__init__.py @@ -30,6 +30,7 @@ AeroSurface, AirBrakes, Components, + ControllableGenericSurface, EllipticalFin, EllipticalFins, Fin, diff --git a/rocketpy/plots/aero_surface_plots.py b/rocketpy/plots/aero_surface_plots.py index e9b30da45..fe9eec783 100644 --- a/rocketpy/plots/aero_surface_plots.py +++ b/rocketpy/plots/aero_surface_plots.py @@ -40,6 +40,80 @@ class for more information on how this plot is made. """ self.aero_surface.cl() + # Coefficients swept against their most relevant incidence angle: pitch-plane + # coefficients vs. angle of attack, yaw-plane ones vs. sideslip. + _COEFFICIENT_SWEEP = [ + ("cL", "alpha"), + ("cQ", "beta"), + ("cD", "alpha"), + ("cm", "alpha"), + ("cn", "beta"), + ] + + def coefficients(self, *, mach=0.3, angle_range_deg=15.0, filename=None): + """Plot the surface's main aerodynamic coefficients. + + Each available, non-zero coefficient (``cL, cQ, cD, cm, cn``) is swept + against its most relevant incidence angle (pitch-plane coefficients vs. + angle of attack, yaw-plane vs. sideslip) at a representative Mach, + skipping coefficients that are identically zero or flat. Works + uniformly across surface types because every generic, linear and + Barrowman surface now exposes these coefficients as callables over the + standard argument tuple. + + Parameters + ---------- + mach : float, optional + Mach number at which to sample the coefficients. Default 0.3. + angle_range_deg : float, optional + Half-range of the incidence sweep, in degrees. Default 15. + filename : str | None, optional + Path to save the figure; if None the figure is shown. + """ + surface = self.aero_surface + independent_vars = getattr(surface, "independent_vars", None) + if independent_vars is None: + return + index = {name: i for i, name in enumerate(independent_vars)} + if "mach" not in index: + return + n_args = len(independent_vars) + + angles = np.linspace( + np.deg2rad(-angle_range_deg), np.deg2rad(angle_range_deg), 61 + ) + entries = [] + for name, var in self._COEFFICIENT_SWEEP: + coeff = getattr(surface, name, None) + if coeff is None or getattr(coeff, "is_zero", False): + continue + if var not in index: + continue + values = np.empty_like(angles) + for i, angle in enumerate(angles): + args = [0.0] * n_args + args[index["mach"]] = mach + args[index[var]] = angle + values[i] = coeff(*args) + if np.allclose(values, 0.0): + continue + entries.append((name, var, values)) + + if not entries: + return + + fig, axes = plt.subplots( + len(entries), 1, figsize=(7, 2.3 * len(entries)), squeeze=False + ) + for ax, (name, var, values) in zip(axes[:, 0], entries): + ax.plot(np.rad2deg(angles), values) + ax.set_xlabel(f"{var.replace('_', ' ').title()} (°)") + ax.set_ylabel(name) + ax.grid(True) + axes[0, 0].set_title(f"{surface.name} coefficients (Mach {mach})") + plt.tight_layout() + show_or_save_plot(filename) + def all(self): """Plots all aero surface plots. @@ -49,6 +123,7 @@ def all(self): """ self.draw() self.lift() + self.coefficients() class _NoseConePlots(_AeroSurfacePlots): @@ -215,6 +290,7 @@ def all(self, *, filename=None): self.airfoil(filename=filename) self.roll(filename=filename) self.lift(filename=filename) + self.coefficients(filename=filename) class _FinPlots(_AeroSurfacePlots): @@ -295,6 +371,7 @@ def all(self, *, filename=None): self.airfoil(filename=filename) self.roll(filename=filename) self.lift(filename=filename) + self.coefficients(filename=filename) class _TrapezoidalFinsPlots(_FinsPlots): @@ -878,9 +955,18 @@ class _GenericSurfacePlots(_AeroSurfacePlots): def draw(self, *, filename=None): pass + def all(self): + """Plots all generic surface plots (the aerodynamic coefficients).""" + self.coefficients() + class _LinearGenericSurfacePlots(_AeroSurfacePlots): """Class that contains all linear generic surface plots.""" def draw(self, *, filename=None): pass + + def all(self): + """Plots all linear generic surface plots (the aerodynamic + coefficients).""" + self.coefficients() diff --git a/rocketpy/plots/flight_plots.py b/rocketpy/plots/flight_plots.py index e792acc9f..681d08b13 100644 --- a/rocketpy/plots/flight_plots.py +++ b/rocketpy/plots/flight_plots.py @@ -1264,12 +1264,30 @@ def stability_and_control_data(self, *, filename=None): # pylint: disable=too-m plt.figure(figsize=(9, 6)) + asymmetric = not self.flight.rocket.is_axisymmetric ax1 = plt.subplot(211) - ax1.plot(self.flight.stability_margin[:, 0], self.flight.stability_margin[:, 1]) + ax1.plot( + self.flight.stability_margin[:, 0], + self.flight.stability_margin[:, 1], + label="Linear pitch" if asymmetric else "Linear (aerodynamic center)", + ) + if asymmetric: + ax1.plot( + self.flight.stability_margin_yaw[:, 0], + self.flight.stability_margin_yaw[:, 1], + label="Linear yaw", + ) + ax1.plot( + self.flight.realized_stability_margin[:, 0], + self.flight.realized_stability_margin[:, 1], + label="Realized (nonlinear CP)", + linestyle="--", + ) ax1.set_title("Stability Margin") ax1.set_xlabel("Time (s)") ax1.set_ylabel("Stability Margin (c)") ax1.set_xlim(0, self.first_parachute_event_time) + ax1.legend() ax1.grid() self._add_event_markers_dropline(ax1, labels={"Burnout"}) @@ -1313,6 +1331,76 @@ def stability_and_control_data(self, *, filename=None): # pylint: disable=too-m plt.subplots_adjust(hspace=0.5) show_or_save_plot(filename) + def dynamic_stability_data(self, *, filename=None): + """Plots the rocket's dynamic-stability quantities over the flight: the + pitch (and, for non-axisymmetric rockets, yaw) natural frequency and + damping ratio of the linearized attitude oscillation. + + The roll rate is overlaid on the natural-frequency plot (as a frequency). + Roll is neutrally stable -- it has no restoring moment and therefore no + natural frequency of its own -- but **roll resonance** ("roll lock-in") + occurs where the roll rate crosses the pitch/yaw natural frequency, the + roll-pitch/yaw coupling driving the attitude oscillation. Those crossings + are the points to watch. + + Parameters + ---------- + filename : str | None, optional + The path the plot should be saved to. By default None, in which case + the plot will be shown instead of saved. Supported file endings are: + eps, jpg, jpeg, pdf, pgf, png, ps, raw, rgba, svg, svgz, tif, tiff + and webp (these are the formats supported by matplotlib). + + Returns + ------- + None + """ + asymmetric = not self.flight.rocket.is_axisymmetric + upper = self.first_parachute_event_time + + plt.figure(figsize=(9, 6)) + + ax1 = plt.subplot(211) + freq = self.flight.pitch_natural_frequency + ax1.plot(freq[:, 0], freq[:, 1] / (2 * np.pi), label="Pitch natural freq.") + if asymmetric: + yaw_freq = self.flight.yaw_natural_frequency + ax1.plot( + yaw_freq[:, 0], yaw_freq[:, 1] / (2 * np.pi), "--", + label="Yaw natural freq.", + ) + # Roll rate as a frequency: where it crosses the natural frequency the + # rocket is in roll resonance (roll-pitch/yaw coupling). + roll_rate = self.flight.w3 + ax1.plot( + roll_rate[:, 0], np.abs(roll_rate[:, 1]) / (2 * np.pi), ":", + color="tab:red", label="Roll rate (resonance if crossing)", + ) + ax1.set_title("Natural Frequency & Roll Rate") + ax1.set_xlabel("Time (s)") + ax1.set_ylabel("Frequency (Hz)") + ax1.set_xlim(0, upper) + ax1.legend() + ax1.grid() + self._add_event_markers_dropline(ax1, labels={"Burnout"}) + + ax2 = plt.subplot(212) + ratio = self.flight.pitch_damping_ratio + ax2.plot(ratio[:, 0], ratio[:, 1], label="Pitch") + if asymmetric: + yaw_ratio = self.flight.yaw_damping_ratio + ax2.plot(yaw_ratio[:, 0], yaw_ratio[:, 1], "--", label="Yaw") + ax2.axhline(1.0, color="gray", linestyle=":", label="Critical (ζ=1)") + ax2.set_title("Damping Ratio") + ax2.set_xlabel("Time (s)") + ax2.set_ylabel("Damping Ratio (ζ)") + ax2.set_xlim(0, upper) + ax2.legend() + ax2.grid() + + plt.subplots_adjust(hspace=0.5) + show_or_save_plot(filename) + def pressure_rocket_altitude(self, *, filename=None): """Plots out pressure at rocket's altitude. @@ -1662,6 +1750,19 @@ def _ylim_in_range(arr): top = float(np.percentile(vals, 95)) * 1.5 return max(top, 1.0) + def _ylim_signed(arr): + # Symmetric y-limits for signed quantities (partial angle of attack, + # sideslip): these are arctan2-based and routinely go negative, so a + # 0 lower bound would clip half the signal. Scale by the 95th + # percentile of the magnitude to ignore the runaway rise near apogee. + mask = (arr[:, 0] >= t_lower) & (arr[:, 0] <= t_upper) + vals = arr[mask, 1] + if len(vals) == 0: + return (-10.0, 10.0) + top = float(np.percentile(np.abs(vals), 95)) * 1.5 + top = max(top, 1.0) + return (-top, top) + plt.figure(figsize=(9, 9)) ax1 = plt.subplot(311) @@ -1679,7 +1780,8 @@ def _ylim_in_range(arr): self.flight.partial_angle_of_attack[:, 1], ) ax2.set_xlim(t_lower, t_upper) - ax2.set_ylim(0, _ylim_in_range(self.flight.partial_angle_of_attack[:, :])) + ax2.set_ylim(*_ylim_signed(self.flight.partial_angle_of_attack[:, :])) + ax2.axhline(0, color="0.6", linewidth=0.8) ax2.set_title("Partial Angle of Attack") ax2.set_xlabel("Time (s)") ax2.set_ylabel("Partial Angle of Attack (°)") @@ -1690,7 +1792,8 @@ def _ylim_in_range(arr): self.flight.angle_of_sideslip[:, 0], self.flight.angle_of_sideslip[:, 1] ) ax3.set_xlim(t_lower, t_upper) - ax3.set_ylim(0, _ylim_in_range(self.flight.angle_of_sideslip[:, :])) + ax3.set_ylim(*_ylim_signed(self.flight.angle_of_sideslip[:, :])) + ax3.axhline(0, color="0.6", linewidth=0.8) ax3.set_title("Angle of Sideslip") ax3.set_xlabel("Time (s)") ax3.set_ylabel("Angle of Sideslip (°)") @@ -1751,6 +1854,7 @@ def all(self): # pylint: disable=too-many-statements print("\n\nTrajectory Stability and Control Plots\n") self.stability_and_control_data() + self.dynamic_stability_data() if self.flight.sensors: print("\n\nSensor Data Plots\n") diff --git a/rocketpy/plots/rocket_plots.py b/rocketpy/plots/rocket_plots.py index 47da8a78b..561732534 100644 --- a/rocketpy/plots/rocket_plots.py +++ b/rocketpy/plots/rocket_plots.py @@ -1,3 +1,5 @@ +import os + import matplotlib.pyplot as plt import numpy as np @@ -87,6 +89,114 @@ def stability_margin(self): alpha=1, ) + def static_margin_yaw(self, *, filename=None): + """Plots the yaw-plane static margin of the rocket as a function of + time. Only meaningful for non-axisymmetric rockets; for an axisymmetric + rocket it is identical to :meth:`static_margin`. + + Parameters + ---------- + filename : str | None, optional + The path the plot should be saved to. By default None, in which case + the plot will be shown instead of saved. Supported file endings are: + eps, jpg, jpeg, pdf, pgf, png, ps, raw, rgba, svg, svgz, tif, tiff + and webp (these are the formats supported by matplotlib). + + Returns + ------- + None + """ + self.rocket.static_margin_yaw(filename=filename) + + def stability_margin_yaw(self): + """Plots the yaw-plane stability margin of the rocket as a function of + Mach number and time. Only meaningful for non-axisymmetric rockets; for + an axisymmetric rocket it is identical to :meth:`stability_margin`. + + Returns + ------- + None + """ + self.rocket.stability_margin_yaw.plot_2d( + lower=0, + upper=[2, self.rocket.motor.burn_out_time], # Mach 2 and burnout + samples=[20, 20], + disp_type="surface", + alpha=1, + ) + + def stability_margin_over_alpha(self, *, filename=None): + """Plots the stability margin in calibers as a function of angle of + attack, for a range of Mach numbers. Built on the nonlinear center of + pressure, it shows how the margin changes with incidence -- the + angle-of-attack analogue of the static margin, which a Barrowman + (Mach-only) estimate cannot capture. Evaluated at the loaded center of + mass (``time = 0``). + + Parameters + ---------- + filename : str | None, optional + The path the plot should be saved to. By default None, in which case + the plot will be shown instead of saved. Supported file endings are: + eps, jpg, jpeg, pdf, pgf, png, ps, raw, rgba, svg, svgz, tif, tiff + and webp (these are the formats supported by matplotlib). + + Returns + ------- + None + """ + alphas_deg = np.linspace(0, 15, 50) + alphas_rad = np.radians(alphas_deg) + + _, ax = plt.subplots() + for mach in (0.1, 0.5, 0.8, 1.2, 2.0): + margin = self.rocket.stability_margin_over_alpha(mach=mach) + margin_values = [margin.get_value_opt(a) for a in alphas_rad] + ax.plot(alphas_deg, margin_values, label=f"Mach {mach}") + + ax.set_title("Stability Margin vs Angle of Attack (loaded)") + ax.set_xlabel("Angle of Attack (deg)") + ax.set_ylabel("Stability Margin (c)") + ax.legend(loc="best", shadow=True) + plt.grid(True) + show_or_save_plot(filename) + + def stability_margin_over_beta(self, *, filename=None): + """Plots the stability margin in calibers as a function of sideslip + angle, for a range of Mach numbers -- the yaw-plane companion to + :meth:`stability_margin_over_alpha`. Most informative for + non-axisymmetric rockets, whose yaw-plane center of pressure differs + from the pitch-plane one. Evaluated at the loaded center of mass + (``time = 0``). + + Parameters + ---------- + filename : str | None, optional + The path the plot should be saved to. By default None, in which case + the plot will be shown instead of saved. Supported file endings are: + eps, jpg, jpeg, pdf, pgf, png, ps, raw, rgba, svg, svgz, tif, tiff + and webp (these are the formats supported by matplotlib). + + Returns + ------- + None + """ + betas_deg = np.linspace(0, 15, 50) + betas_rad = np.radians(betas_deg) + + _, ax = plt.subplots() + for mach in (0.1, 0.5, 0.8, 1.2, 2.0): + margin = self.rocket.stability_margin_over_beta(mach=mach) + margin_values = [margin.get_value_opt(b) for b in betas_rad] + ax.plot(betas_deg, margin_values, label=f"Mach {mach}") + + ax.set_title("Stability Margin vs Sideslip Angle (loaded)") + ax.set_xlabel("Sideslip Angle (deg)") + ax.set_ylabel("Stability Margin (c)") + ax.legend(loc="best", shadow=True) + plt.grid(True) + show_or_save_plot(filename) + # pylint: disable=too-many-statements def drag_curves(self, *, filename=None): """Plots power off and on drag curves of the rocket as a function of time. @@ -141,6 +251,52 @@ def drag_curves(self, *, filename=None): plt.grid(True) show_or_save_plot(filename) + def aerodynamic_coefficients(self, *, filename=None): + """Plots the rocket's total aerodynamic coefficients -- normal force + ``C_N`` and pitch moment ``C_m`` (about the center of dry mass) -- versus + angle of attack, for a range of Mach numbers. The drag coefficient versus + Mach is shown by :meth:`drag_curves`. + + Parameters + ---------- + filename : str | None, optional + The path the plot should be saved to. By default None, in which case + the plot will be shown instead of saved. Supported file endings are: + eps, jpg, jpeg, pdf, pgf, png, ps, raw, rgba, svg, svgz, tif, tiff + and webp (these are the formats supported by matplotlib). + + Returns + ------- + None + """ + alphas_deg = np.linspace(0, 15, 40) + alphas_rad = np.radians(alphas_deg) + + _, (ax1, ax2) = plt.subplots(1, 2, figsize=(11, 4.5)) + for mach in (0.1, 0.5, 0.8, 1.2, 2.0): + coeffs = [ + self.rocket.aerodynamic_coefficients(a, 0.0, mach) + for a in alphas_rad + ] + ax1.plot( + alphas_deg, [c["normal_force"] for c in coeffs], label=f"Mach {mach}" + ) + ax2.plot( + alphas_deg, [c["pitch_moment"] for c in coeffs], label=f"Mach {mach}" + ) + + ax1.set_title("Normal Force Coefficient") + ax1.set_xlabel("Angle of Attack (deg)") + ax1.set_ylabel(r"$C_N$") + ax1.legend(loc="best", shadow=True) + ax1.grid(True) + ax2.set_title("Pitch Moment Coefficient (about CDM)") + ax2.set_xlabel("Angle of Attack (deg)") + ax2.set_ylabel(r"$C_m$") + ax2.grid(True) + plt.tight_layout() + show_or_save_plot(filename) + def thrust_to_weight(self): """ Plots the motor thrust force divided by rocket weight as a function of time. @@ -198,7 +354,34 @@ def draw(self, vis_args=None, plane="xz", *, filename=None): "line_width": 1.0, } - _, ax = plt.subplots(figsize=(8, 6), facecolor=vis_args["background"]) + # A non-axisymmetric rocket looks different in the xz and yz planes + # (e.g. canards or fins present in one plane only), so draw both for + # comparison; an axisymmetric rocket looks the same in either plane. + planes = [plane] if self.rocket.is_axisymmetric else ["xz", "yz"] + + # Each plane is drawn in its own figure so the projections can be read + # and saved independently. When saving multiple planes, the plane name + # is appended to the filename (e.g. ``rocket_xz.png``). + for draw_plane in planes: + _, ax = plt.subplots( + figsize=(8, 6), + facecolor=vis_args["background"], + ) + self._draw_on_plane(ax, vis_args, draw_plane) + plt.tight_layout() + show_or_save_plot(self.__plane_filename(filename, draw_plane, planes)) + + @staticmethod + def __plane_filename(filename, plane, planes): + """Inserts the plane name before the extension when more than one plane + is drawn so each figure is saved to a distinct file.""" + if filename is None or len(planes) == 1: + return filename + root, ext = os.path.splitext(filename) + return f"{root}_{plane}{ext}" + + def _draw_on_plane(self, ax, vis_args, plane): + """Draws the rocket onto a single axis for the given projection plane.""" ax.set_aspect("equal") ax.grid(True, linestyle="--", linewidth=0.5) @@ -210,17 +393,17 @@ def draw(self, vis_args=None, plane="xz", *, filename=None): last_radius, last_x = self._draw_tubes(ax, drawn_surfaces, vis_args) self._draw_motor(last_radius, last_x, ax, vis_args) self._draw_rail_buttons(ax, vis_args) - self._draw_center_of_mass_and_pressure(ax) + self._draw_center_of_mass_and_pressure(ax, plane) self._draw_sensors(ax, self.rocket.sensors, plane) - plt.title("Rocket Representation") - plt.xlim() - plt.ylim([-self.rocket.radius * 4, self.rocket.radius * 6]) - plt.xlabel("Position (m)") - plt.ylabel("Radius (m)") - plt.legend(bbox_to_anchor=(1.05, 1), loc="upper left") - plt.tight_layout() - show_or_save_plot(filename) + title = "Rocket Representation" + if not self.rocket.is_axisymmetric: + title += f" ({plane} plane)" + ax.set_title(title) + ax.set_ylim([-self.rocket.radius * 4, self.rocket.radius * 6]) + ax.set_xlabel("Position (m)") + ax.set_ylabel("Radius (m)") + ax.legend(bbox_to_anchor=(1.05, 1), loc="upper left") def __validate_aerodynamic_surfaces(self, plane): if not self.rocket.aerodynamic_surfaces: @@ -632,17 +815,59 @@ def _draw_rail_buttons(self, ax, vis_args): except IndexError: pass - def _draw_center_of_mass_and_pressure(self, ax): - """Draws the center of mass and center of pressure of the rocket.""" + def _draw_center_of_mass_and_pressure(self, ax, plane="xz"): + """Draws the center of mass and center of pressure of the rocket. + + The red dot is the (linear) aerodynamic center, conventionally labeled + the center of pressure. A translucent red band through it shows the + range over which the *nonlinear* center of pressure travels as the + incidence angle grows (angle of attack in the xz plane, sideslip in the + yz plane). + """ # Draw center of mass and center of pressure cm = self.rocket.center_of_mass(0) ax.scatter(cm, 0, color="#1565c0", label="Center of Mass", s=10) - cp = self.rocket.cp_position(0) + cp = self.rocket.aerodynamic_center(0) + + # Center of pressure travel band: sweep the nonlinear center of + # pressure over the relevant incidence angle and shade its min-max span. + cp_min, cp_max = self._center_of_pressure_range(plane) + if cp_max > cp_min: + ax.plot( + [cp_min, cp_max], + [0, 0], + color="red", + alpha=0.3, + linewidth=4, + solid_capstyle="butt", + zorder=9, + label="Center of Pressure Range", + ) + ax.scatter( - cp, 0, label="Static Center of Pressure", color="red", s=10, zorder=10 + cp, 0, label="Center of Pressure", color="red", s=10, zorder=10 ) + def _center_of_pressure_range(self, plane, max_angle=np.deg2rad(15), samples=31): + """Min and max center-of-pressure position over an incidence sweep. + + Sweeps the angle of attack (xz plane) or sideslip (yz plane) from 0 to + ``max_angle`` using :meth:`Rocket.center_of_pressure_over_alpha` / + :meth:`Rocket.center_of_pressure_over_beta` and returns the extent of + the resulting center-of-pressure travel. + """ + if plane == "yz": + cp_travel = self.rocket.center_of_pressure_over_beta() + else: + cp_travel = self.rocket.center_of_pressure_over_alpha() + angles = np.linspace(0, max_angle, samples) + positions = np.array([cp_travel.get_value_opt(a) for a in angles]) + positions = positions[np.isfinite(positions)] + if len(positions) == 0: + return (0.0, 0.0) + return (float(positions.min()), float(positions.max())) + def _draw_sensors(self, ax, sensors, plane): """Draw the sensor as a small thick line at the position of the sensor, with a vector pointing in the direction normal of the sensor. Get the @@ -724,12 +949,20 @@ def all(self): print("Drag Plots") print("-" * 20) # Separator for Drag Plots self.drag_curves() + self.aerodynamic_coefficients() # Stability Plots print("\nStability Plots") print("-" * 20) # Separator for Stability Plots self.static_margin() self.stability_margin() + self.stability_margin_over_alpha() + # Non-axisymmetric rockets: the above describe the pitch plane only, so + # also show the yaw-plane margins (including the sideslip sweep). + if not self.rocket.is_axisymmetric: + self.static_margin_yaw() + self.stability_margin_yaw() + self.stability_margin_over_beta() # Thrust-to-Weight Plot print("\nThrust-to-Weight Plot") diff --git a/rocketpy/prints/aero_surface_prints.py b/rocketpy/prints/aero_surface_prints.py index cc36f1b01..d85b6e243 100644 --- a/rocketpy/prints/aero_surface_prints.py +++ b/rocketpy/prints/aero_surface_prints.py @@ -1,11 +1,52 @@ from abc import ABC, abstractmethod +import numpy as np + # TODO: the rocketpy/prints/aero_surface_prints.py file could be separated into different, smaller files. class _AeroSurfacePrints(ABC): def __init__(self, aero_surface): self.aero_surface = aero_surface + def coefficients(self): + """Prints a summary of the surface's main aerodynamic coefficients. + + For every non-zero coefficient (``cL, cQ, cD, cm, cn``) reports its + value at a reference condition (5° angle of attack and sideslip, Mach + 0.3) and, when available, the variables it depends on. Works across all + surface types that expose the uniform coefficient accessors. + """ + surface = self.aero_surface + independent_vars = getattr(surface, "independent_vars", None) + if independent_vars is None: + return + index = {name: i for i, name in enumerate(independent_vars)} + if "mach" not in index: + return + n_args = len(independent_vars) + + print("Aerodynamic coefficients (AoA 5°, sideslip 5°, Mach 0.3):") + print("---------------------------------------------------------") + args = [0.0] * n_args + args[index["mach"]] = 0.3 + if "alpha" in index: + args[index["alpha"]] = np.deg2rad(5) + if "beta" in index: + args[index["beta"]] = np.deg2rad(5) + printed = False + for name in ("cL", "cQ", "cD", "cm", "cn"): + coeff = getattr(surface, name, None) + if coeff is None or getattr(coeff, "is_zero", False): + continue + value = coeff(*args) + depends = getattr(coeff, "depends_on", None) + suffix = f" [depends on {', '.join(depends)}]" if depends else "" + print(f" {name} = {value:.4f}{suffix}") + printed = True + if not printed: + print(" (all zero)") + print() + def identity(self): """Prints the identity of the aero surface. @@ -51,6 +92,7 @@ def all(self): self.identity() self.geometry() self.lift() + self.coefficients() class _NoseConePrints(_AeroSurfacePrints): @@ -343,8 +385,8 @@ class _GenericSurfacePrints(_AeroSurfacePrints): def geometry(self): print("Geometric information of the Surface:") print("----------------------------------") - print(f"Reference Area: {self.generic_surface.reference_area:.3f} m") - print(f"Reference length: {2 * self.generic_surface.rocket_radius:.3f} m") + print(f"Reference Area: {self.aero_surface.reference_area:.3f} m^2") + print(f"Reference length: {self.aero_surface.reference_length:.3f} m\n") def all(self): """Prints all information of the generic surface. @@ -355,7 +397,7 @@ def all(self): """ self.identity() self.geometry() - self.lift() + self.coefficients() class _LinearGenericSurfacePrints(_AeroSurfacePrints): @@ -364,8 +406,8 @@ class _LinearGenericSurfacePrints(_AeroSurfacePrints): def geometry(self): print("Geometric information of the Surface:") print("----------------------------------") - print(f"Reference Area: {self.generic_surface.reference_area:.3f} m") - print(f"Reference length: {2 * self.generic_surface.rocket_radius:.3f} m") + print(f"Reference Area: {self.aero_surface.reference_area:.3f} m^2") + print(f"Reference length: {self.aero_surface.reference_length:.3f} m\n") def all(self): """Prints all information of the linear generic surface. @@ -376,4 +418,4 @@ def all(self): """ self.identity() self.geometry() - self.lift() + self.coefficients() diff --git a/rocketpy/prints/flight_prints.py b/rocketpy/prints/flight_prints.py index e28ed2a12..5ead46adc 100644 --- a/rocketpy/prints/flight_prints.py +++ b/rocketpy/prints/flight_prints.py @@ -631,6 +631,27 @@ def stability_margin(self): f"at {self.flight.min_stability_margin_time:.2f} s" ) + out_of_rail_time = self.flight.out_of_rail_time + # The margins above describe the pitch plane. For a non-axisymmetric + # rocket, also report the yaw-plane margin at rail departure. + if not self.flight.rocket.is_axisymmetric: + print( + "Out of Rail Stability Margin - yaw: " + f"{self.flight.stability_margin_yaw.get_value_opt(out_of_rail_time):.3f} c" + ) + + # Dynamic stability at rail departure (representative powered condition). + two_pi = 6.283185307179586 + natural_frequency = self.flight.pitch_natural_frequency.get_value_opt( + out_of_rail_time + ) + damping_ratio = self.flight.pitch_damping_ratio.get_value_opt(out_of_rail_time) + print( + f"Pitch Natural Frequency (out of rail): " + f"{natural_frequency / two_pi:.2f} Hz" + ) + print(f"Pitch Damping Ratio (out of rail): {damping_ratio:.3f}") + def all(self): """Prints out all data available about the Flight. This method invokes all other print methods in the class. diff --git a/rocketpy/prints/rocket_prints.py b/rocketpy/prints/rocket_prints.py index 7b768ea2f..72a2daf8e 100644 --- a/rocketpy/prints/rocket_prints.py +++ b/rocketpy/prints/rocket_prints.py @@ -126,7 +126,8 @@ def rocket_aerodynamics_quantities(self): f"Center of Mass position (time=0): {self.rocket.center_of_mass(0):.3f} m" ) print( - f"Center of Pressure position (time=0): {self.rocket.cp_position(0):.3f} m" + f"Aerodynamic Center position (Mach=0): " + f"{self.rocket.aerodynamic_center(0):.3f} m" ) print( f"Initial Static Margin (mach=0, time=0): " @@ -137,10 +138,28 @@ def rocket_aerodynamics_quantities(self): f"{self.rocket.static_margin(self.rocket.motor.burn_out_time):.3f} c" ) print( - f"Rocket Center of Mass (time=0) - Center of Pressure (mach=0): " - f"{abs(self.rocket.center_of_mass(0) - self.rocket.cp_position(0)):.3f} m\n" + f"Rocket Center of Mass (time=0) - Aerodynamic Center (Mach=0): " + f"{abs(self.rocket.center_of_mass(0) - self.rocket.aerodynamic_center(0)):.3f} m\n" ) + if not self.rocket.is_axisymmetric: + print( + "The rocket is NOT axisymmetric: the values above describe the " + "PITCH plane. Yaw plane:\n" + ) + print( + f"Aerodynamic Center position - yaw (Mach=0): " + f"{self.rocket.aerodynamic_center_yaw(0):.3f} m" + ) + print( + f"Initial Static Margin - yaw (mach=0, time=0): " + f"{self.rocket.static_margin_yaw(0):.3f} c" + ) + print( + f"Final Static Margin - yaw (mach=0, time=burn_out): " + f"{self.rocket.static_margin_yaw(self.rocket.motor.burn_out_time):.3f} c\n" + ) + def parachute_data(self): """Print parachute data. diff --git a/rocketpy/rocket/__init__.py b/rocketpy/rocket/__init__.py index afb7f0bb6..94ad5e8ee 100644 --- a/rocketpy/rocket/__init__.py +++ b/rocketpy/rocket/__init__.py @@ -2,6 +2,7 @@ from rocketpy.rocket.aero_surface import ( AeroSurface, AirBrakes, + ControllableGenericSurface, EllipticalFin, EllipticalFins, Fin, diff --git a/rocketpy/rocket/aero_surface/__init__.py b/rocketpy/rocket/aero_surface/__init__.py index 7634d3500..542435781 100644 --- a/rocketpy/rocket/aero_surface/__init__.py +++ b/rocketpy/rocket/aero_surface/__init__.py @@ -10,6 +10,9 @@ TrapezoidalFin, TrapezoidalFins, ) +from rocketpy.rocket.aero_surface.controllable_generic_surface import ( + ControllableGenericSurface, +) from rocketpy.rocket.aero_surface.generic_surface import GenericSurface from rocketpy.rocket.aero_surface.linear_generic_surface import LinearGenericSurface from rocketpy.rocket.aero_surface.nose_cone import NoseCone diff --git a/rocketpy/rocket/aero_surface/_barrowman_surface.py b/rocketpy/rocket/aero_surface/_barrowman_surface.py new file mode 100644 index 000000000..9a9843193 --- /dev/null +++ b/rocketpy/rocket/aero_surface/_barrowman_surface.py @@ -0,0 +1,120 @@ +import numpy as np + +from rocketpy.mathutils.vector_matrix import Vector +from rocketpy.rocket.aero_surface.aero_coefficient import AeroCoefficient +from rocketpy.rocket.aero_surface.linear_generic_surface import LinearGenericSurface + + +class _BarrowmanSurface(LinearGenericSurface): + """Intermediate base for geometry-defined (Barrowman) aerodynamic surfaces + such as nose cones, tails/transitions and fin sets. + + These surfaces historically expose a lift-curve slope ``clalpha`` (a + ``Function`` of Mach), a geometric center of pressure ``cpz`` and, for fins, + a pair of roll forcing/damping coefficients. This class translates that + Barrowman description into the linear generic-surface coefficient model so + the forces and moments are computed by the single, shared + :meth:`GenericSurface.compute_forces_and_moments`: + + - normal-force slope -> ``cL_alpha`` (pitch plane) and ``cQ_beta`` (yaw plane); + - center-of-pressure offset -> ``cm_alpha`` / ``cn_beta`` (the moment is + carried by the coefficients, with the force applied at the surface origin); + - fin roll -> ``cl_0`` (cant forcing) and ``cl_p`` (roll damping). + + Subclasses must compute ``self.clalpha`` (Function of Mach) and the geometric + center of pressure before calling ``super().__init__`` (which passes the + geometric cp through ``center_of_pressure``), and, for fins, set + ``self.roll_parameters = [clf_delta, cld_omega, cant_angle_rad]``. + """ + + @staticmethod + def _beta(mach): + """Prandtl-Glauert compressibility factor used to correct subsonic + force coefficients of the nose cone, fins and tails/transitions, as in + Barrowman. + + Parameters + ---------- + mach : int, float + Mach number. + + Returns + ------- + beta : float + Compressibility factor based on the Mach number. + + References + ---------- + [1] Barrowman, James S. https://arc.aiaa.org/doi/10.2514/6.1979-504 + """ + if mach < 0.8: + return np.sqrt(1 - mach**2) + elif mach < 1.1: + return np.sqrt(1 - 0.8**2) + else: + return np.sqrt(mach**2 - 1) + + @property + def force_application_point(self): + """Barrowman surfaces apply the resultant force at the surface origin; + the whole center-of-pressure offset is carried by the ``cm``/``cn`` + moment coefficients (avoiding a double count with the ``cp ^ force`` + transport). The geometric center of pressure remains available through + ``self.cp``/``self.cpz`` for display and through + ``center_of_pressure_z`` as a mach-dependent diagnostic. + """ + return Vector([0, 0, 0]) + + def evaluate_coefficients(self): + """Populate the linear generic-surface coefficient derivatives from the + surface geometry. Called by ``GenericSurface.__init__`` and again + whenever the geometry changes. + """ + clalpha = self.clalpha # Function of Mach + cpz = self.cpz # geometric center of pressure (set from center_of_pressure) + reference_length = self.reference_length + + # Axisymmetric Barrowman lift: equal-magnitude slopes in the pitch and + # yaw planes. The yaw-plane (side-force) slope is opposite in sign due to + # the aerodynamic-to-body frame convention used by the shared compute. + self.cL_alpha = self._mach_coefficient( + lambda mach: clalpha.get_value_opt(mach), "cL_alpha" + ) + self.cQ_beta = self._mach_coefficient( + lambda mach: -clalpha.get_value_opt(mach), "cQ_beta" + ) + + # Center-of-pressure offset expressed as moment coefficients (the local + # cp ^ force couple, with the force applied at the origin). + self.cm_alpha = self._mach_coefficient( + lambda mach: -clalpha.get_value_opt(mach) * cpz / reference_length, + "cm_alpha", + ) + self.cn_beta = self._mach_coefficient( + lambda mach: clalpha.get_value_opt(mach) * cpz / reference_length, + "cn_beta", + ) + + # Fin roll forcing (cant) and damping, when present. + roll_parameters = getattr(self, "roll_parameters", None) + if roll_parameters is not None: + clf_delta, cld_omega, cant_angle_rad = roll_parameters + self.cl_0 = self._mach_coefficient( + lambda mach: clf_delta.get_value_opt(mach) * cant_angle_rad, "cl_0" + ) + self.cl_p = self._mach_coefficient( + lambda mach: cld_omega.get_value_opt(mach), "cl_p" + ) + + def _mach_coefficient(self, func_of_mach, name="coefficient"): + """Wrap a Mach-only callable into an :class:`AeroCoefficient` that + depends only on Mach but is callable over the full coefficient argument + tuple. Storing it at one dimension keeps the Mach table un-smeared and + evaluates with a single argument in the hot loop. + """ + return AeroCoefficient( + func_of_mach, + depends_on=("mach",), + independent_vars=self.independent_vars, + name=name, + ) diff --git a/rocketpy/rocket/aero_surface/aero_coefficient.py b/rocketpy/rocket/aero_surface/aero_coefficient.py new file mode 100644 index 000000000..e65aa3a9a --- /dev/null +++ b/rocketpy/rocket/aero_surface/aero_coefficient.py @@ -0,0 +1,229 @@ +"""Minimal-dimension aerodynamic coefficient storage. + +A :class:`AeroCoefficient` stores a single aerodynamic coefficient at its +*intrinsic* dimensionality - a constant, or a :class:`Function` over only the +variables the coefficient actually depends on (its ``depends_on``) - and maps +the full coefficient argument tuple (in ``independent_vars`` order) down to that +subset on every call. + +This avoids forcing a Mach-only (or constant) coefficient into a full seven +dimensional :class:`Function`: interpolation happens at the right dimension (so +a Mach-only table is not smeared across a 7-D domain) and evaluation passes only +the arguments that matter. It generalizes the per-call ``dict(zip(...))`` subset +selection that the CSV loader used to do inline. +""" + +import inspect + +from rocketpy.mathutils import Function + + +class AeroCoefficient: + """A single aerodynamic coefficient stored at minimal dimensionality. + + Parameters + ---------- + source : int, float, callable, or Function + The coefficient value. A number is stored as a constant; a callable or + :class:`Function` is stored over ``depends_on``. + depends_on : sequence of str + The independent variables the coefficient depends on, a (possibly + empty) subset of ``independent_vars``. The order is normalized to the + order of ``independent_vars``. + independent_vars : sequence of str + The full, ordered list of independent variables of the owning surface + (e.g. ``alpha, beta, mach, reynolds, pitch_rate, yaw_rate, roll_rate`` + plus any control or unsteady axes). Defines the argument order accepted + by :meth:`__call__`. + name : str, optional + Name of the coefficient, used for the underlying ``Function`` output. + """ + + def __init__(self, source, depends_on, independent_vars, name="coefficient"): + self.name = name + self.independent_vars = tuple(independent_vars) + # ``depends_on`` is kept in the given order because it matches the + # positional argument order of the stored source (callable parameters, + # CSV columns, …). ``_indices`` therefore maps the full argument tuple + # to the source's own argument order. + self.depends_on = tuple(depends_on) + unknown = [var for var in self.depends_on if var not in self.independent_vars] + if unknown: + raise ValueError( + f"{name} depends on unknown variable(s) {unknown}; " + f"valid variables are {list(self.independent_vars)}." + ) + self._indices = tuple( + self.independent_vars.index(var) for var in self.depends_on + ) + + self.is_zero = False + self._constant = None + if isinstance(source, Function): + self.function = source + elif callable(source): + self.function = Function( + source, + list(self.depends_on) or ["x"], + [name], + interpolation="linear", + extrapolation="natural", + ) + else: + # Scalar constant. + self._constant = float(source) + self.is_zero = self._constant == 0.0 + self.function = Function(self._constant) + + self._evaluate = self.function.get_value_opt + + @classmethod + def from_input(cls, input_data, name, independent_vars, csv_loader=None): + """Build an :class:`AeroCoefficient` from a user coefficient input. + + Mirrors the accepted coefficient inputs of + :class:`GenericSurface`: a number, a callable, a :class:`Function`, or a + path to a CSV file, inferring ``depends_on`` from each. + + Parameters + ---------- + input_data : int, float, str, callable, or Function + The coefficient value (number, CSV path, callable, or Function). + name : str + Coefficient name, used for error messages and the Function output. + independent_vars : sequence of str + The owning surface's ordered independent variables. + csv_loader : callable, optional + Callable ``(file_path, name) -> (function, depends_on)`` used to + load a CSV coefficient at minimal dimension. Required when + ``input_data`` is a string path. + + Returns + ------- + AeroCoefficient + """ + independent_vars = list(independent_vars) + n_vars = len(independent_vars) + vars_repr = ", ".join(independent_vars) + + if isinstance(input_data, AeroCoefficient): + # Already an AeroCoefficient (e.g. a to_dict/from_dict round trip): + # re-key it to the requested independent-variable order. + return cls( + input_data._constant + if input_data._constant is not None + else input_data.function, + input_data.depends_on, + independent_vars, + name, + ) + + if isinstance(input_data, str): + if csv_loader is None: # pragma: no cover - defensive + raise ValueError("A csv_loader is required for CSV coefficients.") + function, depends_on = csv_loader(input_data, name) + return cls(function, depends_on, independent_vars, name) + + if isinstance(input_data, Function): + dom_dim = input_data.__dom_dim__ + if dom_dim == n_vars: + depends_on = independent_vars + elif dom_dim == 1: + # A 1-D Function is taken to depend on the first independent + # variable (alpha) unless its input name matches one of them. + depends_on = [cls._infer_single_var(input_data, independent_vars)] + else: + raise ValueError( + f"{name} Function must have {n_vars} input arguments " + f"({vars_repr}) or be one-dimensional." + ) + return cls(input_data, depends_on, independent_vars, name) + + if callable(input_data): + depends_on = cls._infer_callable_depends_on( + input_data, independent_vars, name + ) + return cls(input_data, depends_on, independent_vars, name) + + # Anything else must be a scalar number. + try: + float(input_data) + except (TypeError, ValueError) as exc: + raise TypeError( + f"Invalid input for {name}: must be a number, a CSV file path, " + "a callable, or a Function." + ) from exc + return cls(input_data, (), independent_vars, name) + + @staticmethod + def _infer_single_var(function, independent_vars): + """Best-effort name of the variable a 1-D Function depends on.""" + try: + label = function.__inputs__[0] + except (AttributeError, IndexError, TypeError): + return independent_vars[0] + label_lower = str(label).lower() + for var in independent_vars: + if var in label_lower: + return var + return independent_vars[0] + + @staticmethod + def _infer_callable_depends_on(func, independent_vars, name): + """Infer ``depends_on`` for a plain callable. + + Two conventions are accepted, checked in order: + + 1. *Named subset* - every parameter name is an independent variable, so + the parameters themselves name the dependency subset (e.g. + ``lambda alpha, mach: ...``). + 2. *Positional full-arity* - the parameter count equals the number of + independent variables, so the callable depends on all of them + regardless of how its parameters are named (e.g. + ``lambda a, b, m, r, p, q, rr: ...``). + """ + n_vars = len(independent_vars) + try: + params = list(inspect.signature(func).parameters.values()) + except (TypeError, ValueError): # pragma: no cover - builtins + params = [] + names = [p.name for p in params] + + if names and set(names) <= set(independent_vars): + return names + if len(names) == n_vars: + return list(independent_vars) + raise ValueError( + f"{name} callable must accept {n_vars} positional arguments " + f"({', '.join(independent_vars)}) or name its parameters after the " + "independent variables it depends on." + ) + + @property + def is_zero_coefficient(self): + """Back-compat alias used by the linear model's hot-loop term skipping.""" + return self.is_zero + + @property + def __dom_dim__(self): + """Number of full independent variables (the call arity).""" + return len(self.independent_vars) + + def get_value_opt(self, *args): + """Fast, unvalidated evaluation (mirrors :meth:`Function.get_value_opt`). + + Maps the full ``independent_vars`` argument tuple down to the source's + own ``depends_on`` arguments before evaluating; a constant short-circuits. + """ + if self._constant is not None: + return self._constant + return self._evaluate(*(args[i] for i in self._indices)) + + # Calling the coefficient is the same as the fast evaluator; the linear + # model grabs ``get_value_opt`` directly for the hot loop. + __call__ = get_value_opt + + def __repr__(self): + if self._constant is not None: + return f"AeroCoefficient({self.name}={self._constant})" + return f"AeroCoefficient({self.name}, depends_on={self.depends_on})" diff --git a/rocketpy/rocket/aero_surface/aero_surface.py b/rocketpy/rocket/aero_surface/aero_surface.py index 6727476c6..f2a561885 100644 --- a/rocketpy/rocket/aero_surface/aero_surface.py +++ b/rocketpy/rocket/aero_surface/aero_surface.py @@ -1,159 +1,46 @@ -from abc import ABC, abstractmethod +import warnings +from abc import ABC -import numpy as np +from rocketpy.rocket.aero_surface.generic_surface import GenericSurface -from rocketpy.mathutils.vector_matrix import Matrix +_AEROSURFACE_DEPRECATION_MESSAGE = ( + "`AeroSurface` is deprecated and will be removed in a future major " + "release. RocketPy's aerodynamic surfaces now derive from `GenericSurface`; " + "use `GenericSurface` (or a concrete surface class such as `NoseCone`, " + "`TrapezoidalFins`, `Tail`, ...) instead. Note that `isinstance(surface, " + "AeroSurface)` still returns True for all surfaces." +) class AeroSurface(ABC): - """Abstract class used to define aerodynamic surfaces.""" - - def __init__(self, name, reference_area, reference_length): - self.reference_area = reference_area - self.reference_length = reference_length - self.name = name - - self.cpx = 0 - self.cpy = 0 - self.cpz = 0 - - self._rotation_surface_to_body = Matrix( - [ - [-1, 0, 0], - [0, 1, 0], - [0, 0, -1], - ] + """Deprecated base class for aerodynamic surfaces. + + .. deprecated:: + ``AeroSurface`` is no longer the base of RocketPy's aerodynamic + surfaces, which now all derive from :class:`GenericSurface`. It is kept + only as a deprecated compatibility shim and will be removed in a future + major release. Importing, instantiating or subclassing it emits a + ``DeprecationWarning``. + + For backward compatibility, :class:`GenericSurface` (and therefore every + concrete surface) is registered as a *virtual subclass*, so existing + ``isinstance(surface, AeroSurface)`` and + ``issubclass(type(surface), AeroSurface)`` checks keep working. + """ + + def __init_subclass__(cls, **kwargs): + super().__init_subclass__(**kwargs) + warnings.warn( + _AEROSURFACE_DEPRECATION_MESSAGE, DeprecationWarning, stacklevel=2 ) - @staticmethod - def _beta(mach): - """Defines a parameter that is often used in aerodynamic - equations. It is commonly used in the Prandtl factor which - corrects subsonic force coefficients for compressible flow. - This is applied to the lift coefficient of the nose cone, - fins and tails/transitions as in [1]. - - Parameters - ---------- - mach : int, float - Number of mach. - - Returns - ------- - beta : int, float - Value that characterizes flow speed based on the mach number. - - References - ---------- - [1] Barrowman, James S. https://arc.aiaa.org/doi/10.2514/6.1979-504 - """ - - if mach < 0.8: - return np.sqrt(1 - mach**2) - elif mach < 1.1: - return np.sqrt(1 - 0.8**2) - else: - return np.sqrt(mach**2 - 1) - - @abstractmethod - def evaluate_center_of_pressure(self): - """Evaluates the center of pressure of the aerodynamic surface in local - coordinates. - - Returns - ------- - None - """ - - @abstractmethod - def evaluate_lift_coefficient(self): - """Evaluates the lift coefficient curve of the aerodynamic surface. - - Returns - ------- - None - """ - - @abstractmethod - def evaluate_geometrical_parameters(self): - """Evaluates the geometrical parameters of the aerodynamic surface. - - Returns - ------- - None - """ - - @abstractmethod - def info(self): - """Prints and plots summarized information of the aerodynamic surface. - - Returns - ------- - None - """ - - @abstractmethod - def all_info(self): - """Prints and plots all the available information of the aero surface. - - Returns - ------- - None - """ - - def compute_forces_and_moments( - self, - stream_velocity, - stream_speed, - stream_mach, - rho, - cp, - *args, - ): # pylint: disable=unused-argument - """Computes the forces and moments acting on the aerodynamic surface. - Used in each time step of the simulation. This method is valid for - the barrowman aerodynamic models. + def __init__(self, *args, **kwargs): # pylint: disable=unused-argument + warnings.warn( + _AEROSURFACE_DEPRECATION_MESSAGE, DeprecationWarning, stacklevel=2 + ) - Parameters - ---------- - stream_velocity : tuple - Tuple containing the stream velocity components in the body frame. - stream_speed : int, float - Speed of the stream in m/s. - stream_mach : int, float - Mach number of the stream. - rho : int, float - Density of the stream in kg/m^3. - cp : Vector - Center of pressure coordinates in the body frame. - args : tuple - Additional arguments. - kwargs : dict - Additional keyword arguments. - Returns - ------- - tuple of float - The aerodynamic forces (lift, side_force, drag) and moments - (pitch, yaw, roll) in the body frame. - """ - R1, R2, R3, M1, M2, M3 = 0, 0, 0, 0, 0, 0 - cpz = cp[2] - stream_vx, stream_vy, stream_vz = stream_velocity - if stream_vx**2 + stream_vy**2 != 0: - # Normalize component stream velocity in body frame - stream_vzn = stream_vz / stream_speed - if -1 * stream_vzn < 1: - attack_angle = np.arccos(-stream_vzn) - c_lift = self.cl.get_value_opt(attack_angle, stream_mach) - # Component lift force magnitude - lift = 0.5 * rho * (stream_speed**2) * self.reference_area * c_lift - # Component lift force components - lift_dir_norm = (stream_vx**2 + stream_vy**2) ** 0.5 - lift_xb = lift * (stream_vx / lift_dir_norm) - lift_yb = lift * (stream_vy / lift_dir_norm) - # Total lift force - R1, R2, R3 = lift_xb, lift_yb, 0 - # Total moment - M1, M2, M3 = -cpz * lift_yb, cpz * lift_xb, 0 - return R1, R2, R3, M1, M2, M3 +# Register GenericSurface (and thus all concrete surfaces) as a virtual +# subclass so that ``isinstance(surface, AeroSurface)`` remains True during the +# deprecation period. Virtual registration does not trigger ``__init_subclass__``. +AeroSurface.register(GenericSurface) diff --git a/rocketpy/rocket/aero_surface/air_brakes.py b/rocketpy/rocket/aero_surface/air_brakes.py index e7417aaa9..3c4958255 100644 --- a/rocketpy/rocket/aero_surface/air_brakes.py +++ b/rocketpy/rocket/aero_surface/air_brakes.py @@ -6,12 +6,14 @@ from rocketpy.plots.aero_surface_plots import _AirBrakesPlots from rocketpy.prints.aero_surface_prints import _AirBrakesPrints -from .aero_surface import AeroSurface +from .controllable_generic_surface import ControllableGenericSurface # TODO: review airbrakes implementation to make it more in line with events -class AirBrakes(AeroSurface): - """AirBrakes class. Inherits from AeroSurface. +class AirBrakes(ControllableGenericSurface): + """AirBrakes class. Inherits from :class:`ControllableGenericSurface`, using + ``deployment_level`` as its single control variable and a multivariable drag + coefficient. Attributes ---------- @@ -100,45 +102,66 @@ def __init__( ------- None """ - super().__init__(name, reference_area, None) + self.clamp = clamp + self.override_rocket_drag = override_rocket_drag + self.initial_deployment_level = deployment_level self.drag_coefficient_curve = drag_coefficient_curve - # TODO: this drag coefficient needs to be a function of more parameters - # just like generic surface coefficients + # Back-compatible 2-input (deployment level, Mach) drag curve, kept for + # display/serialization and as the source of the multivariable drag + # coefficient below. self.drag_coefficient = Function( drag_coefficient_curve, inputs=["Deployment Level", "Mach"], outputs="Drag Coefficient", interpolation="linear", ) - self.clamp = clamp - self.override_rocket_drag = override_rocket_drag - self.initial_deployment_level = deployment_level + + # Multivariable drag coefficient over the generic-surface inputs plus the + # ``deployment_level`` control axis. The deployment-0 ⇒ Cd 0 rule applies + # only when the air brakes add to (rather than override) the rocket drag. + def drag_coefficient_function( + alpha, beta, mach, reynolds, pitch_rate, yaw_rate, roll_rate, deployment_level + ): # pylint: disable=unused-argument + if deployment_level == 0 and not self.override_rocket_drag: + return 0.0 + return self.drag_coefficient.get_value_opt(deployment_level, mach) + + super().__init__( + reference_area=reference_area, + reference_length=2 * (reference_area / np.pi) ** 0.5, + coefficients={"cD": drag_coefficient_function}, + center_of_pressure=(0, 0, 0), + name=name, + controls=("deployment_level",), + ) + self.deployment_level = deployment_level self.prints = _AirBrakesPrints(self) self.plots = _AirBrakesPlots(self) - @property - def deployment_level(self): - """Returns the deployment level of the air brakes.""" - return self._deployment_level - - @deployment_level.setter - def deployment_level(self, value): - # Check if deployment level is within bounds and warn user if not - if value < 0 or value > 1: - # Clamp deployment level if clamp is True + def _clamp_control(self, name, value): + """Clamp ``deployment_level`` to ``[0, 1]`` (or warn if ``clamp`` is + False), preserving the historical AirBrakes behavior.""" + if name == "deployment_level" and (value < 0 or value > 1): if self.clamp: - # Make sure deployment level is between 0 and 1 - value = np.clip(value, 0, 1) + value = float(np.clip(value, 0, 1)) else: - # Raise warning if clamp is False warnings.warn( f"Deployment level of {self.name} is smaller than 0 or " + "larger than 1. Extrapolation for the drag coefficient " + "curve will be used.", UserWarning, ) - self._deployment_level = value + return value + + @property + def deployment_level(self): + """Returns the deployment level of the air brakes.""" + return self.control_state["deployment_level"] + + @deployment_level.setter + def deployment_level(self, value): + self.set_control("deployment_level", value) def _reset(self): """Resets the air brakes to their initial state. This is ran at the @@ -146,44 +169,6 @@ def _reset(self): state.""" self.deployment_level = self.initial_deployment_level - def evaluate_center_of_pressure(self): - """Evaluates the center of pressure of the aerodynamic surface in local - coordinates. - - For air brakes, all components of the center of pressure position are - 0. - - Returns - ------- - None - """ - self.cpx = 0 - self.cpy = 0 - self.cpz = 0 - self.cp = (self.cpx, self.cpy, self.cpz) - - def evaluate_lift_coefficient(self): - """Evaluates the lift coefficient curve of the aerodynamic surface. - - For air brakes, the current model assumes no lift is generated. - Therefore, the lift coefficient (C_L) and its derivative relative to the - angle of attack (C_L_alpha), is 0. - - Returns - ------- - None - """ - self.clalpha = Function( - lambda mach: 0, - "Mach", - f"Lift coefficient derivative for {self.name}", - ) - self.cl = Function( - lambda alpha, mach: 0, - ["Alpha (rad)", "Mach"], - "Lift Coefficient", - ) - def evaluate_geometrical_parameters(self): """Evaluates the geometrical parameters of the aerodynamic surface. diff --git a/rocketpy/rocket/aero_surface/controllable_generic_surface.py b/rocketpy/rocket/aero_surface/controllable_generic_surface.py new file mode 100644 index 000000000..42e473eb9 --- /dev/null +++ b/rocketpy/rocket/aero_surface/controllable_generic_surface.py @@ -0,0 +1,162 @@ +from rocketpy.plots.aero_surface_plots import _GenericSurfacePlots +from rocketpy.prints.aero_surface_prints import _GenericSurfacePrints +from rocketpy.rocket.aero_surface.generic_surface import ( + BASE_INDEPENDENT_VARS, + GenericSurface, +) + + +class ControllableGenericSurface(GenericSurface): + """A generic aerodynamic surface whose coefficients additionally depend on + one or more **control-deflection** variables (canards, grid fins, elevons, + air-brake deployment, …) sourced at runtime from a controller. + + On top of the seven standard independent variables of + :class:`GenericSurface` (``alpha``, ``beta``, ``mach``, ``reynolds``, + ``pitch_rate``, ``yaw_rate``, ``roll_rate``), the coefficient functions take + one extra argument per entry of ``controls`` (appended in order). The + current control values are held in :attr:`control_state` and mutated each + simulation step by a controller (see ``Rocket.add_controllable_surface``); + :meth:`_coefficient_arguments` appends them to every coefficient evaluation. + + Attributes + ---------- + ControllableGenericSurface.control_variables : list of str + Names of the control-deflection axes, in coefficient-argument order. + ControllableGenericSurface.control_state : dict + Current value of each control variable (defaults to 0). + """ + + def __init__( + self, + reference_area, + reference_length, + coefficients, + center_of_pressure=(0, 0, 0), + name="Controllable Generic Surface", + controls=("deflection",), + ): + """Create a controllable generic aerodynamic surface. + + Parameters + ---------- + reference_area : int, float + Reference area of the surface, in squared meters. + reference_length : int, float + Reference length of the surface, in meters. + coefficients : dict + Aerodynamic coefficients (``cL``, ``cQ``, ``cD``, ``cm``, ``cn``, + ``cl``), each a callable/CSV/Function of the seven base variables + **plus** the control variables listed in ``controls`` (appended in + order). Omitted coefficients default to 0. + center_of_pressure : tuple, list, optional + Application point of the aerodynamic forces and moments in the local + surface frame. Default ``(0, 0, 0)``. + name : str, optional + Name of the surface. Default ``"Controllable Generic Surface"``. + controls : iterable of str, optional + Names of the control-deflection axes. Default ``("deflection",)``. + Each name becomes an extra coefficient argument and a key in + :attr:`control_state`. + """ + # These must be set before ``super().__init__`` so coefficient + # processing (arity, CSV validation) and the derived-cp accessors see + # the extended variable list and the current control values. + self.control_variables = list(controls) + self.independent_vars = BASE_INDEPENDENT_VARS + self.control_variables + self.control_state = {name: 0.0 for name in self.control_variables} + + super().__init__( + reference_area=reference_area, + reference_length=reference_length, + coefficients=coefficients, + center_of_pressure=center_of_pressure, + name=name, + ) + + self.prints = _GenericSurfacePrints(self) + self.plots = _GenericSurfacePlots(self) + + def _coefficient_arguments( + self, + alpha, + beta, + mach, + reynolds, + pitch_rate, + yaw_rate, + roll_rate, + alpha_dot=0.0, + beta_dot=0.0, + ): + """Append the current control-variable values (in + ``self.control_variables`` order) to the standard inputs (which may + already include the unsteady ``alpha_dot``/``beta_dot`` axes).""" + base = super()._coefficient_arguments( + alpha, + beta, + mach, + reynolds, + pitch_rate, + yaw_rate, + roll_rate, + alpha_dot, + beta_dot, + ) + controls = tuple(self.control_state[name] for name in self.control_variables) + return base + controls + + def _clamp_control(self, name, value): # pylint: disable=unused-argument + """Hook to constrain a control value before it is stored. The base class + applies no clamping; subclasses (e.g. ``AirBrakes``) may override.""" + return value + + def set_control(self, name, value): + """Set the current value of a control variable (applying any clamping). + + Parameters + ---------- + name : str + Name of the control variable; must be one of + :attr:`control_variables`. + value : float + New control value. + """ + if name not in self.control_state: + raise KeyError( + f"Unknown control variable '{name}'. " + f"Valid controls are: {self.control_variables}." + ) + self.control_state[name] = self._clamp_control(name, value) + + def get_control(self, name): + """Return the current value of a control variable.""" + return self.control_state[name] + + def to_dict(self, include_outputs=False): # pylint: disable=unused-argument + return { + "reference_area": self.reference_area, + "reference_length": self.reference_length, + "coefficients": { + "cL": self.cL, + "cQ": self.cQ, + "cD": self.cD, + "cm": self.cm, + "cn": self.cn, + "cl": self.cl, + }, + "center_of_pressure": self.center_of_pressure, + "name": self.name, + "controls": self.control_variables, + } + + @classmethod + def from_dict(cls, data): + return cls( + reference_area=data["reference_area"], + reference_length=data["reference_length"], + coefficients=data["coefficients"], + center_of_pressure=data.get("center_of_pressure", (0, 0, 0)), + name=data.get("name", "Controllable Generic Surface"), + controls=data.get("controls", ("deflection",)), + ) diff --git a/rocketpy/rocket/aero_surface/fins/_base_fin.py b/rocketpy/rocket/aero_surface/fins/_base_fin.py index f6b09f797..332326aea 100644 --- a/rocketpy/rocket/aero_surface/fins/_base_fin.py +++ b/rocketpy/rocket/aero_surface/fins/_base_fin.py @@ -5,13 +5,15 @@ from rocketpy.mathutils.function import Function -from ..aero_surface import AeroSurface +from .._barrowman_surface import _BarrowmanSurface +from ..linear_generic_surface import LinearGenericSurface -class _BaseFin(AeroSurface): +class _BaseFin(_BarrowmanSurface): """ Base class for fins, shared by both Fin and Fins classes. - Inherits from AeroSurface. + Inherits from :class:`_BarrowmanSurface`, translating the fin geometry into + the linear generic-surface coefficient model. Handles shared initialization logic and common properties. """ @@ -47,8 +49,13 @@ def __init__( self.geometry = None self.reference_area = np.pi * rocket_radius**2 + self.reference_length = self.rocket_diameter - super().__init__(name, self.reference_area, self.rocket_diameter) + # The linear generic-surface machinery is initialized lazily by + # ``_finalize_barrowman`` once the concrete subclass has set up its + # geometry strategy and the first ``_update_geometry_chain`` has + # produced ``clalpha``, ``cpz`` and ``roll_parameters``. + self._barrowman_initialized = False def _update_reference_quantities(self): """Update quantities that depend on rocket radius.""" @@ -56,11 +63,32 @@ def _update_reference_quantities(self): self.reference_length = self.rocket_diameter def _update_geometry_chain(self): - """Update geometry-dependent quantities in dependency order.""" + """Update geometry-dependent quantities in dependency order, then + (re)build the generic-surface coefficients from the new geometry.""" self.evaluate_geometrical_parameters() self.evaluate_center_of_pressure() self.evaluate_lift_coefficient() self.evaluate_roll_parameters() + if self._barrowman_initialized: + # Geometry changed after construction: refresh the coefficients. + self.evaluate_coefficients() + self.compute_all_coefficients() + self._evaluate_derived_coefficients() + else: + self._finalize_barrowman() + + def _finalize_barrowman(self): + """Initialize the linear generic-surface machinery from the geometry + computed by the first ``_update_geometry_chain`` call.""" + LinearGenericSurface.__init__( + self, + reference_area=self.reference_area, + reference_length=self.reference_length, + coefficients={}, + center_of_pressure=(self.cpx, self.cpy, self.cpz), + name=self.name, + ) + self._barrowman_initialized = True @property def rocket_radius(self): diff --git a/rocketpy/rocket/aero_surface/fins/fin.py b/rocketpy/rocket/aero_surface/fins/fin.py index 5fec8a099..ac49d92e7 100644 --- a/rocketpy/rocket/aero_surface/fins/fin.py +++ b/rocketpy/rocket/aero_surface/fins/fin.py @@ -148,6 +148,21 @@ def __init__( self._angular_position = angular_position self._angular_position_rad = math.radians(angular_position) + def _update_geometry_chain(self): + """Run the base geometry/coefficient chain, then (re)build the body<->fin + rotation matrices. + + The rotation matrices must be set **after** the chain: the chain's first + call initializes the generic-surface machinery, which resets + ``_rotation_surface_to_body`` to the identity. Doing it here (rather than + in each concrete fin's ``__init__``) ensures every individual-fin + subclass -- trapezoidal, elliptical and free-form -- gets correct, + angular-position-aware rotation matrices on construction and whenever the + geometry changes. + """ + super()._update_geometry_chain() + self.evaluate_rotation_matrix() + @property def cant_angle(self): return self._cant_angle @@ -318,6 +333,41 @@ def evaluate_rotation_matrix(self): self._rotation_fin_to_body = R_body_to_fin.transpose self._rotation_surface_to_body = self._rotation_fin_to_body + @property + def force_application_point(self): + """A single (off-axis) fin keeps its bespoke force computation and + transports the moment geometrically through its center of pressure, + so the force application point is the fin's actual cp rather than the + surface origin used by axisymmetric Barrowman surfaces. + """ + return Vector([self.cpx, self.cpy, self.cpz]) + + def evaluate_coefficients(self): + """A single fin transports its moment geometrically (via ``cp ^ force`` + in its own ``compute_forces_and_moments``), so only the normal-force + slopes are exposed for the stability-margin diagnostic; the moment + coefficients stay zero to avoid double-counting the cp offset. + + A fin's lift only resists incidence in its own plane, so its slope is + projected onto the pitch and yaw planes by its angular position + ``phi``: ``sin(phi)**2`` to the pitch plane (``cL_alpha``) and + ``cos(phi)**2`` to the yaw plane (``cQ_beta``). A fin at ``phi = 0`` + (lying in the yaw plane) thus feeds the yaw plane only, which is what + makes a non-axisymmetric individual-fin layout report different pitch- + and yaw-plane centers of pressure. An evenly spaced set of ``n`` fins + sums to ``n / 2`` in each plane, reproducing the axisymmetric ``Fins`` + set (see :meth:`Fins.fin_num_correction`). + """ + clalpha = self.clalpha + sin_sq = math.sin(self.angular_position_rad) ** 2 + cos_sq = math.cos(self.angular_position_rad) ** 2 + self.cL_alpha = self._mach_coefficient( + lambda mach: clalpha.get_value_opt(mach) * sin_sq + ) + self.cQ_beta = self._mach_coefficient( + lambda mach: -clalpha.get_value_opt(mach) * cos_sq + ) + def compute_forces_and_moments( self, stream_velocity, diff --git a/rocketpy/rocket/aero_surface/fins/fins.py b/rocketpy/rocket/aero_surface/fins/fins.py index b61bfcc19..9913b3f5b 100644 --- a/rocketpy/rocket/aero_surface/fins/fins.py +++ b/rocketpy/rocket/aero_surface/fins/fins.py @@ -200,11 +200,18 @@ def evaluate_roll_parameters(self): """ clf_delta = ( self.roll_forcing_interference_factor - * self.fin_num_correction(self.n) + * self.n * (self.Yma + self.rocket_radius) * self.clalpha_single_fin / self.reference_length ) # Function of mach number + # NOTE: roll forcing scales with the full fin count ``n`` -- every + # identically-canted fin contributes the same roll moment, with no + # cancellation. This differs from the normal-force slope, which uses the + # ``fin_num_correction(n)`` (~n/2) multiple-fin factor because fins at + # different roll angles partially cancel in pitch/yaw. Using + # ``fin_num_correction(n)`` here previously halved the roll forcing (and + # the roll rate) of a fin set relative to the equivalent individual fins. clf_delta.set_inputs("Mach") clf_delta.set_outputs("Roll moment forcing coefficient derivative") clf_delta.set_title( @@ -251,66 +258,6 @@ def fin_num_correction(n): else: return n / 2 - def compute_forces_and_moments( - self, - stream_velocity, - stream_speed, - stream_mach, - rho, - cp, - omega, - *args, - ): # pylint: disable=arguments-differ - """Computes the forces and moments acting on the aerodynamic surface. - - Parameters - ---------- - stream_velocity : tuple of float - The velocity of the airflow relative to the surface. - stream_speed : float - The magnitude of the airflow speed. - stream_mach : float - The Mach number of the airflow. - rho : float - Air density. - cp : Vector - Center of pressure coordinates in the body frame. - omega: tuple[float, float, float] - Tuple containing angular velocities around the x, y, z axes. - - Returns - ------- - tuple of float - The aerodynamic forces (lift, side_force, drag) and moments - (pitch, yaw, roll) in the body frame. - """ - - R1, R2, R3, M1, M2, _ = super().compute_forces_and_moments( - stream_velocity, - stream_speed, - stream_mach, - rho, - cp, - ) - clf_delta, cld_omega, cant_angle_rad = self.roll_parameters - M3_forcing = ( - (1 / 2 * rho * stream_speed**2) - * self.reference_area - * self.reference_length - * clf_delta.get_value_opt(stream_mach) - * cant_angle_rad - ) - M3_damping = ( - (1 / 2 * rho * stream_speed) - * self.reference_area - * (self.reference_length) ** 2 - * cld_omega.get_value_opt(stream_mach) - * omega[2] - / 2 - ) - M3 = M3_forcing + M3_damping - return R1, R2, R3, M1, M2, M3 - def to_dict(self, **kwargs): if self.airfoil: if kwargs.get("discretize", False): diff --git a/rocketpy/rocket/aero_surface/fins/trapezoidal_fin.py b/rocketpy/rocket/aero_surface/fins/trapezoidal_fin.py index c58055945..f6bf1a7cd 100644 --- a/rocketpy/rocket/aero_surface/fins/trapezoidal_fin.py +++ b/rocketpy/rocket/aero_surface/fins/trapezoidal_fin.py @@ -163,7 +163,6 @@ def __init__( ) self._update_geometry_chain() self.evaluate_shape() - self.evaluate_rotation_matrix() self.prints = _TrapezoidalFinPrints(self) self.plots = _TrapezoidalFinPlots(self) diff --git a/rocketpy/rocket/aero_surface/generic_surface.py b/rocketpy/rocket/aero_surface/generic_surface.py index 4b83e0e4f..90c0a3537 100644 --- a/rocketpy/rocket/aero_surface/generic_surface.py +++ b/rocketpy/rocket/aero_surface/generic_surface.py @@ -6,6 +6,20 @@ from rocketpy.mathutils import Function from rocketpy.mathutils.vector_matrix import Matrix, Vector +from rocketpy.rocket.aero_surface.aero_coefficient import AeroCoefficient + +# Single source of truth for the coefficient independent variables. Subclasses +# (e.g. ControllableGenericSurface, or the alpha_dot/beta_dot extension) append +# extra axes to this base via ``self.independent_vars``. +BASE_INDEPENDENT_VARS = [ + "alpha", + "beta", + "mach", + "reynolds", + "pitch_rate", + "yaw_rate", + "roll_rate", +] class GenericSurface: @@ -21,6 +35,7 @@ def __init__( coefficients, center_of_pressure=(0, 0, 0), name="Generic Surface", + unsteady_aero=False, ): """Create a generic aerodynamic surface, defined by its aerodynamic coefficients. This surface is used to model any aerodynamic surface @@ -74,8 +89,27 @@ def __init__( aerodynamic surface. The default value is (0, 0, 0). name : str, optional Name of the aerodynamic surface. Default is 'GenericSurface'. + unsteady_aero : bool, optional + If True, the coefficients additionally depend on the time + derivatives of the flow angles, and ``alpha_dot`` and ``beta_dot`` + are appended (in that order) to the independent variables. CSV files + may then include "alpha_dot"/"beta_dot" columns, and callables must + accept the two extra trailing arguments. The simulation supplies 0 + for these unless it computes them, so existing coefficient tables are + unaffected. Default is False. """ + # Independent variables the coefficients depend on. Subclasses may set + # this (with extra axes appended) before calling ``super().__init__``. + # When ``unsteady_aero`` is enabled, the time-derivatives of the flow + # angles (``alpha_dot``, ``beta_dot``) are appended as extra axes + # (defaulting to 0 at runtime, so existing tables are unaffected). + self._unsteady_aero = unsteady_aero + if not hasattr(self, "independent_vars"): + self.independent_vars = list(BASE_INDEPENDENT_VARS) + if unsteady_aero: + self.independent_vars += ["alpha_dot", "beta_dot"] + self.reference_area = reference_area self.reference_length = reference_length self.center_of_pressure = center_of_pressure @@ -94,6 +128,144 @@ def __init__( value = self._process_input(coeff_value, coeff) setattr(self, coeff, value) + self.evaluate_coefficients() + self._evaluate_derived_coefficients() + + @property + def force_application_point(self): + """Local point (surface frame) at which the resultant force is applied + when transporting its moment to the rocket's center of dry mass. For a + plain generic surface this is simply the center of pressure ``self.cp``; + the residual couple is carried by the ``cm``/``cn``/``cl`` coefficients. + Barrowman subclasses override this to the origin, because they fold the + whole cp offset into the moment coefficients instead. + """ + return Vector([self.cpx, self.cpy, self.cpz]) + + def evaluate_coefficients(self): + """Hook for subclasses to (re)populate the aerodynamic coefficient + ``Function``s from their geometry. The base class builds coefficients + directly from the user-provided dictionary, so this is a no-op here. + Subclasses that derive coefficients from geometry (e.g. the Barrowman + surfaces) override this and call it again whenever their geometry + changes. + + Returns + ------- + None + """ + + def _evaluate_derived_coefficients(self): + """Build the mach-only diagnostic accessors used by the rocket's + center-of-pressure / stability-margin computation, for both the pitch + and the yaw plane. + + These reconstruct, at the linearization point ``alpha = beta = 0`` with + zero rates, each plane's force-curve slope and the location of its + center of pressure. The center of pressure combines the surface's + declared local ``cpz`` with the offset implied by its moment + coefficient (the two representations are interchangeable; + ``cpz_eff = cpz - (dc_moment/dangle)/(dc_force/dangle) * L_ref``): + + - pitch plane: ``lift_coefficient_derivative`` (``dcL/dalpha``) and + ``center_of_pressure_z`` (from ``cm``); + - yaw plane: ``side_coefficient_derivative`` and + ``center_of_pressure_z_yaw`` (from ``cn``). + + Returns + ------- + None + """ + cL_alpha = self._partial_slope(self.cL, axis="alpha") + cm_alpha = self._partial_slope(self.cm, axis="alpha") + cQ_beta = self._partial_slope(self.cQ, axis="beta") + cn_beta = self._partial_slope(self.cn, axis="beta") + self._set_derived_cp_accessors(cL_alpha, cm_alpha, cQ_beta, cn_beta) + + def _set_derived_cp_accessors(self, cL_alpha, cm_alpha, cQ_beta, cn_beta): + """Store the pitch- and yaw-plane diagnostic accessors as mach-only + ``Function``s, guarding the moment/force division for zero-force + surfaces (which then drop out of the force-weighted cp average). + + Parameters + ---------- + cL_alpha : Function + Pitch-plane normal-force slope ``dcL/dalpha`` vs. mach. + cm_alpha : Function + Pitch-moment slope ``dcm/dalpha`` vs. mach. + cQ_beta : Function + Yaw-plane side-force slope ``dcQ/dbeta`` vs. mach. + cn_beta : Function + Yaw-moment slope ``dcn/dbeta`` vs. mach. + """ + reference_length = self.reference_length + local_cpz = self.force_application_point[2] + + def _cp_z(force_slope, moment_slope): + def cp_z(mach): + slope = force_slope.get_value_opt(mach) + if slope == 0: + return local_cpz + return ( + local_cpz + - moment_slope.get_value_opt(mach) / slope * reference_length + ) + + return Function(cp_z, "Mach", "Center of pressure to local origin (m)") + + # Pitch plane. + self.lift_coefficient_derivative = cL_alpha + self.center_of_pressure_z = _cp_z(cL_alpha, cm_alpha) + + # Yaw plane. The side-force slope is sign-adjusted (``-cQ_beta``) so that + # an axisymmetric surface yields the same signed weight as the pitch + # plane, making the two planes' margins coincide when symmetric. + self.side_coefficient_derivative = -cQ_beta + self.center_of_pressure_z_yaw = _cp_z(cQ_beta, cn_beta) + + def _partial_slope(self, coefficient, axis): + """Partial derivative ``d(coefficient)/d(axis)`` at ``alpha = beta = 0`` + and zero rates, returned as a mach-only ``Function``. + + Reuses :meth:`Function.differentiate` on a single-variable slice of the + coefficient taken along ``axis`` (``"alpha"`` or ``"beta"``) with all + other base inputs frozen at zero. Extra axes (control deflections) are + frozen at their current value via :meth:`_coefficient_arguments`. + + Parameters + ---------- + coefficient : Function + A coefficient ``Function`` over ``self.independent_vars``. + axis : str + Either ``"alpha"`` or ``"beta"``. + + Returns + ------- + Function + ``d(coefficient)/d(axis)`` evaluated at the zero point, vs. mach. + """ + + def slope(mach): + if axis == "alpha": + sliced = Function( + lambda alpha: coefficient( + *self._coefficient_arguments( + alpha, 0.0, mach, 0.0, 0.0, 0.0, 0.0 + ) + ) + ) + else: + sliced = Function( + lambda beta: coefficient( + *self._coefficient_arguments( + 0.0, beta, mach, 0.0, 0.0, 0.0, 0.0 + ) + ) + ) + return sliced.differentiate(0) + + return Function(slope, "Mach", "Coefficient derivative") + def _get_default_coefficients(self): """Returns default coefficients @@ -173,6 +345,8 @@ def _compute_from_coefficients( pitch_rate, yaw_rate, roll_rate, + alpha_dot=0.0, + beta_dot=0.0, ): """Compute the aerodynamic forces and moments from the aerodynamic coefficients. @@ -208,30 +382,56 @@ def _compute_from_coefficients( dyn_pressure_area = 0.5 * rho * stream_speed**2 * self.reference_area dyn_pressure_area_length = dyn_pressure_area * self.reference_length - # Compute aerodynamic forces - lift = dyn_pressure_area * self.cL( - alpha, beta, mach, reynolds, pitch_rate, yaw_rate, roll_rate - ) - side = dyn_pressure_area * self.cQ( - alpha, beta, mach, reynolds, pitch_rate, yaw_rate, roll_rate - ) - drag = dyn_pressure_area * self.cD( - alpha, beta, mach, reynolds, pitch_rate, yaw_rate, roll_rate + # Coefficient arguments (base 7 vars, plus any extra axes appended by + # subclasses such as control deflections or the unsteady alpha_dot/ + # beta_dot terms). + args = self._coefficient_arguments( + alpha, + beta, + mach, + reynolds, + pitch_rate, + yaw_rate, + roll_rate, + alpha_dot, + beta_dot, ) + # Compute aerodynamic forces + lift = dyn_pressure_area * self.cL(*args) + side = dyn_pressure_area * self.cQ(*args) + drag = dyn_pressure_area * self.cD(*args) + # Compute aerodynamic moments - pitch = dyn_pressure_area_length * self.cm( - alpha, beta, mach, reynolds, pitch_rate, yaw_rate, roll_rate - ) - yaw = dyn_pressure_area_length * self.cn( - alpha, beta, mach, reynolds, pitch_rate, yaw_rate, roll_rate - ) - roll = dyn_pressure_area_length * self.cl( - alpha, beta, mach, reynolds, pitch_rate, yaw_rate, roll_rate - ) + pitch = dyn_pressure_area_length * self.cm(*args) + yaw = dyn_pressure_area_length * self.cn(*args) + roll = dyn_pressure_area_length * self.cl(*args) return lift, side, drag, pitch, yaw, roll + def _coefficient_arguments( + self, + alpha, + beta, + mach, + reynolds, + pitch_rate, + yaw_rate, + roll_rate, + alpha_dot=0.0, + beta_dot=0.0, + ): + """Returns the argument tuple passed to every coefficient ``Function``, + in ``self.independent_vars`` order. The base class provides the seven + standard inputs, plus ``alpha_dot``/``beta_dot`` when ``unsteady_aero`` + is enabled. Subclasses (e.g. :class:`ControllableGenericSurface`) + override this to append further axes such as control deflections. + """ + base = (alpha, beta, mach, reynolds, pitch_rate, yaw_rate, roll_rate) + if self._unsteady_aero: + return base + (alpha_dot, beta_dot) + return base + def compute_forces_and_moments( self, stream_velocity, @@ -243,6 +443,8 @@ def compute_forces_and_moments( density, dynamic_viscosity, z, + alpha_dot=0.0, + beta_dot=0.0, ): """Computes the forces and moments acting on the aerodynamic surface. Used in each time step of the simulation. This method is valid for @@ -306,20 +508,29 @@ def compute_forces_and_moments( omega[0], # q omega[1], # r omega[2], # p + alpha_dot, + beta_dot, ) - # Conversion from aerodynamic frame to body frame + # Conversion from the aerodynamic frame to the body frame. This is the + # direction cosine matrix (DCM) that expresses the aerodynamic-frame + # force components in the body frame, i.e. rotations by ``-alpha`` about + # x and ``+beta`` about y. Using the opposite-sign "vector rotation" + # matrices is incorrect: it leaves the result effectively in the + # aerodynamic frame, flipping the transverse components of any force that + # has a drag part (see RocketPy issue #932). Surfaces with no drag (the + # Barrowman lift/side surfaces) differ only in the small axial term. rotation_matrix = Matrix( [ [1, 0, 0], - [0, math.cos(alpha), -math.sin(alpha)], - [0, math.sin(alpha), math.cos(alpha)], + [0, math.cos(alpha), math.sin(alpha)], + [0, -math.sin(alpha), math.cos(alpha)], ] ) @ Matrix( [ - [math.cos(beta), 0, -math.sin(beta)], + [math.cos(beta), 0, math.sin(beta)], [0, 1, 0], - [math.sin(beta), 0, math.cos(beta)], + [-math.sin(beta), 0, math.cos(beta)], ] ) R1, R2, R3 = rotation_matrix @ Vector([side, -lift, -drag]) @@ -330,91 +541,48 @@ def compute_forces_and_moments( return R1, R2, R3, M1, M2, M3 def _process_input(self, input_data, coeff_name): - """Process the input data, either as a CSV file or a callable function. + """Process a coefficient input into an :class:`AeroCoefficient`. + + Accepts a number, a callable, a :class:`Function`, or a path to a CSV + file, storing the coefficient at its intrinsic dimensionality (its + ``depends_on``) rather than forcing it into a full + ``len(self.independent_vars)``-D ``Function``. See + :class:`AeroCoefficient`. Parameters ---------- - input_data : str or callable - Input data to be processed, either a path to a CSV or a callable. + input_data : int, float, str, callable, or Function + Input data to be processed. coeff_name : str Name of the coefficient being processed for error reporting. Returns ------- - Function - Function object with 7 input arguments (alpha, beta, mach, reynolds, - pitch_rate, yaw_rate, roll_rate). + AeroCoefficient + Callable over the full ``self.independent_vars`` argument tuple. """ - if isinstance(input_data, str): - # Input is assumed to be a file path to a CSV - return self.__load_generic_surface_csv(input_data, coeff_name) - elif isinstance(input_data, Function): - if input_data.__dom_dim__ != 7: - raise ValueError( - f"{coeff_name} function must have 7 input arguments" - " (alpha, beta, mach, reynolds, pitch_rate, yaw_rate, roll_rate)." - ) - return input_data - elif callable(input_data): - # Check if callable has 7 inputs (alpha, beta, mach, reynolds, pitch_rate, yaw_rate, roll_rate) - if input_data.__code__.co_argcount != 7: - raise ValueError( - f"{coeff_name} function must have 7 input arguments" - " (alpha, beta, mach, reynolds, pitch_rate, yaw_rate, roll_rate)." - ) - return Function( - input_data, - [ - "alpha", - "beta", - "mach", - "reynolds", - "pitch_rate", - "yaw_rate", - "roll_rate", - ], - [coeff_name], - interpolation="linear", - extrapolation="natural", - ) - elif input_data == 0: - return Function( - lambda alpha, beta, mach, reynolds, pitch_rate, yaw_rate, roll_rate: 0, - [ - "alpha", - "beta", - "mach", - "reynolds", - "pitch_rate", - "yaw_rate", - "roll_rate", - ], - [coeff_name], - interpolation="linear", - extrapolation="natural", - ) - else: - raise TypeError( - f"Invalid input for {coeff_name}: must be a CSV file path" - " or a callable." - ) + return AeroCoefficient.from_input( + input_data, + coeff_name, + self.independent_vars, + csv_loader=self.__load_generic_surface_csv, + ) def __load_generic_surface_csv(self, file_path, coeff_name): # pylint: disable=too-many-statements,import-outside-toplevel - """Load GenericSurface coefficient CSV into a 7D Function. + """Load a GenericSurface coefficient CSV at minimal dimension. This loader expects header-based CSV data with one or more independent - variables among: alpha, beta, mach, reynolds, pitch_rate, yaw_rate, - roll_rate. + variables among ``self.independent_vars`` (the seven base variables, + plus any extra axes added by subclasses such as control deflections). + + Returns + ------- + tuple + ``(function, depends_on)`` where ``function`` is a low-dimensional + ``Function`` over the present columns and ``depends_on`` lists those + columns. Consumed by :meth:`AeroCoefficient.from_input`. """ - independent_vars = [ - "alpha", - "beta", - "mach", - "reynolds", - "pitch_rate", - "yaw_rate", - "roll_rate", - ] + independent_vars = list(self.independent_vars) try: with open(file_path, mode="r") as file: @@ -464,23 +632,7 @@ def __load_generic_surface_csv(self, file_path, coeff_name): # pylint: disable= extrapolation="natural", ) - def wrapper(alpha, beta, mach, reynolds, pitch_rate, yaw_rate, roll_rate): - args_by_name = { - "alpha": alpha, - "beta": beta, - "mach": mach, - "reynolds": reynolds, - "pitch_rate": pitch_rate, - "yaw_rate": yaw_rate, - "roll_rate": roll_rate, - } - selected_args = [args_by_name[col] for col in ordered_present_columns] - return csv_func(*selected_args) - - return Function( - wrapper, - independent_vars, - [coeff_name], - interpolation="linear", - extrapolation="natural", - ) + # The CSV columns may appear in any order; AeroCoefficient maps the full + # argument tuple to ``ordered_present_columns`` order, so the stored + # Function is queried directly at its own (minimal) dimensionality. + return csv_func, ordered_present_columns diff --git a/rocketpy/rocket/aero_surface/linear_generic_surface.py b/rocketpy/rocket/aero_surface/linear_generic_surface.py index 3e7ed9a55..fa085252c 100644 --- a/rocketpy/rocket/aero_surface/linear_generic_surface.py +++ b/rocketpy/rocket/aero_surface/linear_generic_surface.py @@ -171,6 +171,29 @@ def __init__( self.prints = _LinearGenericSurfacePrints(self) self.plots = _LinearGenericSurfacePlots(self) + def _evaluate_derived_coefficients(self): + """Exact override of the diagnostic cp accessors. The linear model + already exposes the forcing derivatives ``cL_alpha``/``cm_alpha`` (pitch) + and ``cQ_beta``/``cn_beta`` (yaw), so the slopes are read directly + (frozen at zero alpha/beta/rates) instead of being recovered by + numerical differentiation. Damping derivatives (``_p/_q/_r``) are + intentionally excluded from the stability cp. + """ + + def _at_zero(coefficient, name): + return Function( + lambda mach: coefficient(0.0, 0.0, mach, 0.0, 0.0, 0.0, 0.0), + "Mach", + name, + ) + + self._set_derived_cp_accessors( + _at_zero(self.cL_alpha, "cL_alpha"), + _at_zero(self.cm_alpha, "cm_alpha"), + _at_zero(self.cQ_beta, "cQ_beta"), + _at_zero(self.cn_beta, "cn_beta"), + ) + def _get_default_coefficients(self): """Returns default coefficients @@ -220,64 +243,119 @@ def _get_default_coefficients(self): } return default_coefficients - def compute_forcing_coefficient(self, c_0, c_alpha, c_beta): - """Compute the forcing coefficient from the derivatives of the - aerodynamic coefficients.""" - - def total_coefficient( - alpha, beta, mach, reynolds, pitch_rate, yaw_rate, roll_rate - ): - return ( - c_0(alpha, beta, mach, reynolds, pitch_rate, yaw_rate, roll_rate) - + c_alpha(alpha, beta, mach, reynolds, pitch_rate, yaw_rate, roll_rate) - * alpha - + c_beta(alpha, beta, mach, reynolds, pitch_rate, yaw_rate, roll_rate) - * beta - ) + _COEFFICIENT_INPUTS = [ + "alpha", + "beta", + "mach", + "reynolds", + "pitch_rate", + "yaw_rate", + "roll_rate", + ] - return Function( - total_coefficient, - [ - "alpha", - "beta", - "mach", - "reynolds", - "pitch_rate", - "yaw_rate", - "roll_rate", - ], - ["coefficient"], - ) + def compute_forcing_coefficient(self, c_0, c_alpha, c_beta): + """Compose the forcing coefficient ``c_0 + c_alpha*alpha + c_beta*beta``, + evaluating only the non-zero terms. + + Two hot-loop optimizations: ``get_value_opt`` is the unvalidated fast + evaluator (for callable-source coefficients it is the raw source), and + terms that are identically zero are skipped entirely. For a Barrowman + surface each forcing coefficient has at most one non-zero derivative, so + this typically collapses to a single source call (or to a constant 0). + """ + has_0 = not getattr(c_0, "is_zero_coefficient", False) + has_alpha = not getattr(c_alpha, "is_zero_coefficient", False) + has_beta = not getattr(c_beta, "is_zero_coefficient", False) + c_0_opt = c_0.get_value_opt + c_alpha_opt = c_alpha.get_value_opt + c_beta_opt = c_beta.get_value_opt + + if not (has_0 or has_alpha or has_beta): + + def total_coefficient( # pylint: disable=unused-argument + alpha, beta, mach, reynolds, pitch_rate, yaw_rate, roll_rate + ): + return 0.0 + + else: + + def total_coefficient( + alpha, beta, mach, reynolds, pitch_rate, yaw_rate, roll_rate + ): + value = 0.0 + if has_0: + value += c_0_opt( + alpha, beta, mach, reynolds, pitch_rate, yaw_rate, roll_rate + ) + if has_alpha: + value += ( + c_alpha_opt( + alpha, beta, mach, reynolds, pitch_rate, yaw_rate, roll_rate + ) + * alpha + ) + if has_beta: + value += ( + c_beta_opt( + alpha, beta, mach, reynolds, pitch_rate, yaw_rate, roll_rate + ) + * beta + ) + return value + + return Function(total_coefficient, self._COEFFICIENT_INPUTS, ["coefficient"]) def compute_damping_coefficient(self, c_p, c_q, c_r): - """Compute the damping coefficient from the derivatives of the - aerodynamic coefficients.""" - - def total_coefficient( - alpha, beta, mach, reynolds, pitch_rate, yaw_rate, roll_rate - ): - return ( - c_p(alpha, beta, mach, reynolds, pitch_rate, yaw_rate, roll_rate) - * roll_rate - + c_q(alpha, beta, mach, reynolds, pitch_rate, yaw_rate, roll_rate) - * pitch_rate - + c_r(alpha, beta, mach, reynolds, pitch_rate, yaw_rate, roll_rate) - * yaw_rate - ) - - return Function( - total_coefficient, - [ - "alpha", - "beta", - "mach", - "reynolds", - "pitch_rate", - "yaw_rate", - "roll_rate", - ], - ["coefficient"], - ) + """Compose the damping coefficient + ``c_p*roll_rate + c_q*pitch_rate + c_r*yaw_rate``, evaluating only the + non-zero terms (see :meth:`compute_forcing_coefficient`). For a Barrowman + surface only ``cl_p`` (roll damping) is non-zero, so most damping + coefficients collapse to a constant 0. + """ + has_p = not getattr(c_p, "is_zero_coefficient", False) + has_q = not getattr(c_q, "is_zero_coefficient", False) + has_r = not getattr(c_r, "is_zero_coefficient", False) + c_p_opt = c_p.get_value_opt + c_q_opt = c_q.get_value_opt + c_r_opt = c_r.get_value_opt + + if not (has_p or has_q or has_r): + + def total_coefficient( # pylint: disable=unused-argument + alpha, beta, mach, reynolds, pitch_rate, yaw_rate, roll_rate + ): + return 0.0 + + else: + + def total_coefficient( + alpha, beta, mach, reynolds, pitch_rate, yaw_rate, roll_rate + ): + value = 0.0 + if has_p: + value += ( + c_p_opt( + alpha, beta, mach, reynolds, pitch_rate, yaw_rate, roll_rate + ) + * roll_rate + ) + if has_q: + value += ( + c_q_opt( + alpha, beta, mach, reynolds, pitch_rate, yaw_rate, roll_rate + ) + * pitch_rate + ) + if has_r: + value += ( + c_r_opt( + alpha, beta, mach, reynolds, pitch_rate, yaw_rate, roll_rate + ) + * yaw_rate + ) + return value + + return Function(total_coefficient, self._COEFFICIENT_INPUTS, ["coefficient"]) def compute_all_coefficients(self): """Compute all the aerodynamic coefficients from the derivatives.""" @@ -312,6 +390,29 @@ def compute_all_coefficients(self): ) self.cld = self.compute_damping_coefficient(self.cl_p, self.cl_q, self.cl_r) + self._expose_uniform_coefficients() + + def _expose_uniform_coefficients(self): + """Expose the main force/moment coefficients (``cL, cQ, cD, cm, cn``) as + the composed *forcing* coefficients, so every surface - including + Barrowman ones whose coefficients are derived from geometry - has + uniform, callable accessors over the standard argument tuple. + + The forcing coefficient is the static, flow-state part of the model + (``c_0 + c_alpha*alpha + c_beta*beta``); the rate-damping parts + (``cLd``, …) are dimensionally tied to the reduced rate and remain + separate. The roll coefficient is intentionally **not** exposed as + ``cl`` here: geometry-defined subclasses (nose cones, tails, individual + fins) use the legacy ``cl`` name for their *lift* coefficient. The + composed roll forcing/damping remain available as ``clf``/``cld``. + """ + # pylint: disable=invalid-name + self.cL = self.cLf + self.cQ = self.cQf + self.cD = self.cDf + self.cm = self.cmf + self.cn = self.cnf + def _compute_from_coefficients( self, rho, @@ -323,10 +424,15 @@ def _compute_from_coefficients( pitch_rate, yaw_rate, roll_rate, + alpha_dot=0.0, # pylint: disable=unused-argument + beta_dot=0.0, # pylint: disable=unused-argument ): """Compute the aerodynamic forces and moments from the aerodynamic coefficients. + The linear (Barrowman) model does not use the unsteady ``alpha_dot`` / + ``beta_dot`` terms; they are accepted for signature compatibility. + Parameters ---------- rho : float @@ -369,42 +475,36 @@ def _compute_from_coefficients( / 2 ) + # Evaluate the composed coefficients through the fast, unvalidated + # ``get_value_opt`` path (the composed coefficients are callable-source + # Functions, so this calls the closure directly, skipping the per-call + # ``__call__``/``get_value`` argument validation in the hot loop). + args = (alpha, beta, mach, reynolds, pitch_rate, yaw_rate, roll_rate) + # Compute aerodynamic forces - lift = dyn_pressure_area * self.cLf( - alpha, beta, mach, reynolds, pitch_rate, yaw_rate, roll_rate - ) + dyn_pressure_area_damping * self.cLd( - alpha, beta, mach, reynolds, pitch_rate, yaw_rate, roll_rate - ) + lift = dyn_pressure_area * self.cLf.get_value_opt( + *args + ) + dyn_pressure_area_damping * self.cLd.get_value_opt(*args) - side = dyn_pressure_area * self.cQf( - alpha, beta, mach, reynolds, pitch_rate, yaw_rate, roll_rate - ) + dyn_pressure_area_damping * self.cQd( - alpha, beta, mach, reynolds, pitch_rate, yaw_rate, roll_rate - ) + side = dyn_pressure_area * self.cQf.get_value_opt( + *args + ) + dyn_pressure_area_damping * self.cQd.get_value_opt(*args) - drag = dyn_pressure_area * self.cDf( - alpha, beta, mach, reynolds, pitch_rate, yaw_rate, roll_rate - ) + dyn_pressure_area_damping * self.cDd( - alpha, beta, mach, reynolds, pitch_rate, yaw_rate, roll_rate - ) + drag = dyn_pressure_area * self.cDf.get_value_opt( + *args + ) + dyn_pressure_area_damping * self.cDd.get_value_opt(*args) # Compute aerodynamic moments - pitch = dyn_pressure_area_length * self.cmf( - alpha, beta, mach, reynolds, pitch_rate, yaw_rate, roll_rate - ) + dyn_pressure_area_length_damping * self.cmd( - alpha, beta, mach, reynolds, pitch_rate, yaw_rate, roll_rate - ) + pitch = dyn_pressure_area_length * self.cmf.get_value_opt( + *args + ) + dyn_pressure_area_length_damping * self.cmd.get_value_opt(*args) - yaw = dyn_pressure_area_length * self.cnf( - alpha, beta, mach, reynolds, pitch_rate, yaw_rate, roll_rate - ) + dyn_pressure_area_length_damping * self.cnd( - alpha, beta, mach, reynolds, pitch_rate, yaw_rate, roll_rate - ) + yaw = dyn_pressure_area_length * self.cnf.get_value_opt( + *args + ) + dyn_pressure_area_length_damping * self.cnd.get_value_opt(*args) - roll = dyn_pressure_area_length * self.clf( - alpha, beta, mach, reynolds, pitch_rate, yaw_rate, roll_rate - ) + dyn_pressure_area_length_damping * self.cld( - alpha, beta, mach, reynolds, pitch_rate, yaw_rate, roll_rate - ) + roll = dyn_pressure_area_length * self.clf.get_value_opt( + *args + ) + dyn_pressure_area_length_damping * self.cld.get_value_opt(*args) return lift, side, drag, pitch, yaw, roll diff --git a/rocketpy/rocket/aero_surface/nose_cone.py b/rocketpy/rocket/aero_surface/nose_cone.py index 240a61a5c..a0c0507e7 100644 --- a/rocketpy/rocket/aero_surface/nose_cone.py +++ b/rocketpy/rocket/aero_surface/nose_cone.py @@ -7,10 +7,10 @@ from rocketpy.plots.aero_surface_plots import _NoseConePlots from rocketpy.prints.aero_surface_prints import _NoseConePrints -from .aero_surface import AeroSurface +from ._barrowman_surface import _BarrowmanSurface -class NoseCone(AeroSurface): +class NoseCone(_BarrowmanSurface): """Keeps nose cone information. Note @@ -129,7 +129,9 @@ def __init__( # pylint: disable=too-many-statements None """ rocket_radius = rocket_radius or base_radius - super().__init__(name, np.pi * rocket_radius**2, 2 * rocket_radius) + self.name = name + self.reference_area = np.pi * rocket_radius**2 + self.reference_length = 2 * rocket_radius self._rocket_radius = rocket_radius self._base_radius = base_radius @@ -163,6 +165,16 @@ def __init__( # pylint: disable=too-many-statements self.evaluate_lift_coefficient() self.evaluate_center_of_pressure() + # Translate the Barrowman geometry (clalpha, cpz) into the linear + # generic-surface coefficient model and build the shared compute path. + super().__init__( + reference_area=self.reference_area, + reference_length=self.reference_length, + coefficients={}, + center_of_pressure=(self.cpx, self.cpy, self.cpz), + name=name, + ) + self.plots = _NoseConePlots(self) self.prints = _NoseConePrints(self) diff --git a/rocketpy/rocket/aero_surface/rail_buttons.py b/rocketpy/rocket/aero_surface/rail_buttons.py index 7d3a9bd30..19ad16f32 100644 --- a/rocketpy/rocket/aero_surface/rail_buttons.py +++ b/rocketpy/rocket/aero_surface/rail_buttons.py @@ -1,12 +1,11 @@ import numpy as np -from rocketpy.mathutils.function import Function from rocketpy.prints.aero_surface_prints import _RailButtonsPrints -from .aero_surface import AeroSurface +from .generic_surface import GenericSurface -class RailButtons(AeroSurface): +class RailButtons(GenericSurface): """Class that defines a rail button pair or group. Attributes @@ -53,14 +52,24 @@ def __init__( If not provided, it will be calculated when the RailButtons object is added to a Rocket object. """ - super().__init__(name, None, None) self.buttons_distance = buttons_distance self.angular_position = angular_position self.button_height = button_height - self.name = name self.rocket_radius = rocket_radius - self.evaluate_lift_coefficient() - self.evaluate_center_of_pressure() + + # Rail buttons produce no aerodynamic force; they are modeled as a + # generic surface with all-zero coefficients. The reference area/length + # are placeholders (never used, since rail buttons are not part of the + # rocket's aerodynamic_surfaces) computed from the rocket radius when + # available. + reference_radius = rocket_radius or 1.0 + super().__init__( + reference_area=np.pi * reference_radius**2, + reference_length=2 * reference_radius, + coefficients={}, + center_of_pressure=(0, 0, 0), + name=name, + ) self.prints = _RailButtonsPrints(self) @@ -68,47 +77,6 @@ def __init__( def angular_position_rad(self): return np.radians(self.angular_position) - def evaluate_center_of_pressure(self): - """Evaluates the center of pressure of the rail buttons. Rail buttons - do not contribute to the center of pressure of the rocket. - - Returns - ------- - None - """ - self.cpx = 0 - self.cpy = 0 - self.cpz = 0 - self.cp = (self.cpx, self.cpy, self.cpz) - - def evaluate_lift_coefficient(self): - """Evaluates the lift coefficient curve of the rail buttons. Rail - buttons do not contribute to the lift coefficient of the rocket. - - Returns - ------- - None - """ - self.clalpha = Function( - lambda mach: 0, - "Mach", - f"Lift coefficient derivative for {self.name}", - ) - self.cl = Function( - lambda alpha, mach: 0, - ["Alpha (rad)", "Mach"], - "Cl", - ) - - def evaluate_geometrical_parameters(self): - """Evaluates the geometrical parameters of the rail buttons. Rail - buttons do not contribute to the geometrical parameters of the rocket. - - Returns - ------- - None - """ - def to_dict(self, **kwargs): # pylint: disable=unused-argument return { "buttons_distance": self.buttons_distance, diff --git a/rocketpy/rocket/aero_surface/tail.py b/rocketpy/rocket/aero_surface/tail.py index 3e738f99c..0066bcf86 100644 --- a/rocketpy/rocket/aero_surface/tail.py +++ b/rocketpy/rocket/aero_surface/tail.py @@ -4,10 +4,10 @@ from rocketpy.plots.aero_surface_plots import _TailPlots from rocketpy.prints.aero_surface_prints import _TailPrints -from .aero_surface import AeroSurface +from ._barrowman_surface import _BarrowmanSurface -class Tail(AeroSurface): +class Tail(_BarrowmanSurface): """Class that defines a tail. Currently only accepts conical tails. Note @@ -76,7 +76,9 @@ def __init__(self, top_radius, bottom_radius, length, rocket_radius, name="Tail" ------- None """ - super().__init__(name, np.pi * rocket_radius**2, 2 * rocket_radius) + self.name = name + self.reference_area = np.pi * rocket_radius**2 + self.reference_length = 2 * rocket_radius self._top_radius = top_radius self._bottom_radius = bottom_radius @@ -87,6 +89,16 @@ def __init__(self, top_radius, bottom_radius, length, rocket_radius, name="Tail" self.evaluate_lift_coefficient() self.evaluate_center_of_pressure() + # Translate the Barrowman geometry into the linear generic-surface + # coefficient model and build the shared compute path. + super().__init__( + reference_area=self.reference_area, + reference_length=self.reference_length, + coefficients={}, + center_of_pressure=(self.cpx, self.cpy, self.cpz), + name=name, + ) + self.plots = _TailPlots(self) self.prints = _TailPrints(self) diff --git a/rocketpy/rocket/rocket.py b/rocketpy/rocket/rocket.py index c16c799ce..2092e89e6 100644 --- a/rocketpy/rocket/rocket.py +++ b/rocketpy/rocket/rocket.py @@ -133,9 +133,12 @@ class Rocket: Collection of air brakes of the rocket. Rocket._controllers : list Collection of controllers of the rocket. - Rocket.cp_position : Function - Function of Mach number expressing the rocket's center of pressure - position relative to user defined rocket reference system. + Rocket.aerodynamic_center : Function + Function of Mach number expressing the rocket's aerodynamic center + (the linearized, small-incidence center of pressure) position relative + to the user defined rocket reference system. The nonlinear center of + pressure at a finite angle of attack is :meth:`Rocket.center_of_pressure`. + ``Rocket.cp_position`` is a deprecated alias for this attribute. See :doc:`Positions and Coordinate Systems ` for more information. Rocket.stability_margin : Function @@ -345,10 +348,10 @@ def __init__( # pylint: disable=too-many-statements self.surfaces_cp_to_cdm = {} self.rail_buttons = Components() - self.cp_position = Function( + self.aerodynamic_center = Function( lambda mach: 0, inputs="Mach Number", - outputs="Center of Pressure Position (m)", + outputs="Aerodynamic Center Position (m)", ) self.total_lift_coeff_der = Function( lambda mach: 0, @@ -363,6 +366,27 @@ def __init__( # pylint: disable=too-many-statements inputs=["Mach", "Time (s)"], outputs="Stability Margin (c)", ) + # Yaw-plane counterparts. The pitch-plane attributes above remain the + # primary (default) margin; these expose the yaw plane for + # non-axisymmetric rockets (see ``evaluate_center_of_pressure``). + self.aerodynamic_center_yaw = Function( + lambda mach: 0, + inputs="Mach Number", + outputs="Aerodynamic Center Position - Yaw (m)", + ) + self.total_side_coeff_der = Function( + lambda mach: 0, + inputs="Mach Number", + outputs="Total Side Coefficient Derivative", + ) + self.static_margin_yaw = Function( + lambda time: 0, inputs="Time (s)", outputs="Static Margin - Yaw (c)" + ) + self.stability_margin_yaw = Function( + lambda mach, time: 0, + inputs=["Mach", "Time (s)"], + outputs="Stability Margin - Yaw (c)", + ) # Define aerodynamic drag coefficients # Coefficients used during flight simulation @@ -610,42 +634,491 @@ def evaluate_thrust_to_weight(self): self.thrust_to_weight.set_title("Thrust to Weight ratio") def evaluate_center_of_pressure(self): - """Evaluates rocket center of pressure position relative to user defined - rocket reference system. It can be called as many times as needed, as it - will update the center of pressure function every time it is called. The - code will iterate through all aerodynamic surfaces and consider each of - their center of pressure position and derivative of the coefficient of - lift as a function of Mach number. + """Evaluates the rocket's **aerodynamic center** as a function of Mach + number, relative to the user-defined rocket reference system. + + The aerodynamic center is the linearized (small-incidence, + :math:`\\alpha=\\beta=0`) center of pressure: the normal-force-slope- + weighted average of every aerodynamic surface's location. It is the + well-conditioned reference used by the static and stability margins and + is distinct from :meth:`center_of_pressure`, which is the *nonlinear* + center of pressure at a finite angle of attack/sideslip. + + It is computed independently for the **pitch** plane + (``aerodynamic_center``, from the normal-force/pitch-moment slopes) and + the **yaw** plane (``aerodynamic_center_yaw``, from the + side-force/yaw-moment slopes). For an axisymmetric rocket the two + coincide; when they differ (a non-axisymmetric configuration, only + expressible through ``GenericSurface``), a warning is raised because the + scalar ``static_margin``/``stability_margin`` attributes describe the + pitch plane only. Returns ------- - self.cp_position : Function - Function of Mach number expressing the rocket's center of pressure - position relative to user defined rocket reference system. - See :doc:`Positions and Coordinate Systems ` - for more information. + self.aerodynamic_center : Function + Function of Mach number expressing the rocket's pitch-plane + aerodynamic center position relative to the user-defined rocket + reference system. See :doc:`Positions and Coordinate Systems + ` for more information. """ - # Re-Initialize total lift coefficient derivative and center of pressure position + # Re-Initialize total force coefficient derivatives and AC positions self.total_lift_coeff_der.set_source(lambda mach: 0) - self.cp_position.set_source(lambda mach: 0) + self.aerodynamic_center.set_source(lambda mach: 0) + self.total_side_coeff_der.set_source(lambda mach: 0) + self.aerodynamic_center_yaw.set_source(lambda mach: 0) - # Calculate total lift coefficient derivative and center of pressure + # Calculate total force coefficient derivatives and aerodynamic center if len(self.aerodynamic_surfaces) > 0: for aero_surface, position in self.aerodynamic_surfaces: - if isinstance(aero_surface, GenericSurface): - continue - # ref_factor corrects lift for different reference areas - ref_factor = (aero_surface.rocket_radius / self.radius) ** 2 - self.total_lift_coeff_der += ref_factor * aero_surface.clalpha - self.cp_position += ( - ref_factor - * aero_surface.clalpha - * (position.z - self._csys * aero_surface.cpz) + lift_coeff_der = aero_surface.lift_coefficient_derivative + cp_z = aero_surface.center_of_pressure_z + # ref_factor corrects force for different reference areas + ref_factor = aero_surface.reference_area / self.area + self.total_lift_coeff_der += ref_factor * lift_coeff_der + self.aerodynamic_center += ( + ref_factor * lift_coeff_der * (position.z - self._csys * cp_z) ) - # Avoid errors when only generic surfaces are added + + # Yaw plane. + side_coeff_der = aero_surface.side_coefficient_derivative + cp_z_yaw = aero_surface.center_of_pressure_z_yaw + self.total_side_coeff_der += ref_factor * side_coeff_der + self.aerodynamic_center_yaw += ( + ref_factor * side_coeff_der * (position.z - self._csys * cp_z_yaw) + ) + # Avoid errors when only zero-lift surfaces are added if self.total_lift_coeff_der.get_value(0) != 0: - self.cp_position /= self.total_lift_coeff_der - return self.cp_position + self.aerodynamic_center /= self.total_lift_coeff_der + if self.total_side_coeff_der.get_value(0) != 0: + self.aerodynamic_center_yaw /= self.total_side_coeff_der + + self._warn_if_asymmetric_cp() + return self.aerodynamic_center + + def _cp_plane_max_difference(self): + """Largest pitch- vs yaw-plane aerodynamic center difference, in meters, + over a few sample Mach numbers.""" + sample_machs = (0.0, 0.5, 1.0) + return max( + abs( + self.aerodynamic_center.get_value_opt(mach) + - self.aerodynamic_center_yaw.get_value_opt(mach) + ) + for mach in sample_machs + ) + + @property + def is_axisymmetric(self): + """``True`` when the rocket's pitch- and yaw-plane aerodynamic centers + coincide (to caliber-scale tolerance). When ``False`` the rocket is not + axisymmetric: ``aerodynamic_center``, ``static_margin`` and + ``stability_margin`` describe the PITCH plane only and differ from their + ``*_yaw`` counterparts (``aerodynamic_center_yaw``, ``static_margin_yaw``, + ``stability_margin_yaw``).""" + # Tolerance relative to the rocket diameter (caliber-scale). + return self._cp_plane_max_difference() <= 1e-6 * (2 * self.radius) + + @property + def cp_position(self): + """Deprecated alias for :attr:`aerodynamic_center` (the linearized, + Mach-dependent center of pressure / aerodynamic center).""" + warnings.warn( + "'cp_position' is deprecated and will be removed in a future " + "release; use 'aerodynamic_center' (the linearized center of " + "pressure) instead. For the nonlinear center of pressure at a given " + "angle of attack use 'center_of_pressure(alpha, beta, mach)'.", + DeprecationWarning, + stacklevel=2, + ) + return self.aerodynamic_center + + @property + def cp_position_yaw(self): + """Deprecated alias for :attr:`aerodynamic_center_yaw`.""" + warnings.warn( + "'cp_position_yaw' is deprecated and will be removed in a future " + "release; use 'aerodynamic_center_yaw' instead.", + DeprecationWarning, + stacklevel=2, + ) + return self.aerodynamic_center_yaw + + def _warn_if_asymmetric_cp(self): + """Warn when the pitch- and yaw-plane aerodynamic centers disagree, i.e. + the rocket is not axisymmetric. The ``static_margin``/ + ``stability_margin`` attributes then describe the pitch plane only; the + yaw-plane counterparts are ``*_yaw``.""" + if not self.is_axisymmetric: + max_diff = self._cp_plane_max_difference() + warnings.warn( + "Pitch- and yaw-plane aerodynamic centers differ " + f"(max difference ~{max_diff:.4g} m): the rocket is not " + "axisymmetric. 'aerodynamic_center', 'static_margin' and " + "'stability_margin' describe the PITCH plane; use " + "'aerodynamic_center_yaw', 'static_margin_yaw' and " + "'stability_margin_yaw' for the yaw plane.", + stacklevel=2, + ) + + def _aerodynamic_center_limit(self, alpha, beta, mach): + """Small-incidence limit of :meth:`center_of_pressure`: the linearized + aerodynamic center, blended between the pitch and yaw planes by the + squared normal-force contribution of each, so the nonlinear center of + pressure stays continuous in the ``(alpha, beta)`` direction as the + incidence goes to zero (pure pitch -> pitch AC, pure sideslip -> yaw AC). + """ + weight_pitch = (self.total_lift_coeff_der.get_value_opt(mach) * alpha) ** 2 + weight_yaw = (self.total_side_coeff_der.get_value_opt(mach) * beta) ** 2 + pitch = self.aerodynamic_center.get_value_opt(mach) + if weight_pitch + weight_yaw == 0: + return pitch + yaw = self.aerodynamic_center_yaw.get_value_opt(mach) + return (pitch * weight_pitch + yaw * weight_yaw) / (weight_pitch + weight_yaw) + + def center_of_pressure(self, alpha, beta, mach, reynolds=0.0): + """Nonlinear center of pressure axial position, as a function of the + aerodynamic state. + + Unlike :attr:`aerodynamic_center` (the linearized center of pressure, a + function of Mach alone, valid only near zero incidence), this aggregates + the *actual* force and moment of every aerodynamic surface at the + requested angle of attack ``alpha``, sideslip ``beta`` and Mach number, + then locates the axial point about which the resultant transverse + aerodynamic force produces no moment: + ``z_cp = (M2*R1 - M1*R2) / (R1**2 + R2**2)`` (about the center of dry + mass, from ``M = r x F``). + + Because the resultant force is evaluated at the actual *combined* + incidence, a single axial location captures both the pitch and the yaw + plane -- the ``aerodynamic_center``/``aerodynamic_center_yaw`` split is + only needed for the linearized slopes, which must pick a perturbation + axis. As ``alpha, beta -> 0`` the normal force vanishes and the location + becomes a ``0/0`` limit; there the linearized aerodynamic center + (:attr:`aerodynamic_center`) is returned, which the nonlinear value + converges to. + + Parameters + ---------- + alpha : float + Angle of attack, in radians (pitch plane, body aerodynamic frame). + beta : float + Sideslip angle, in radians (yaw plane, body aerodynamic frame). + mach : float + Free-stream Mach number. + reynolds : float, optional + Rocket-level Reynolds number (based on the rocket diameter). Each + surface's Reynolds number is scaled to its own reference length. + Defaults to ``0`` (vanishing-Reynolds limit, matching the linearized + ``aerodynamic_center`` convention). + + Returns + ------- + float + Center of pressure position along the rocket axis, in the rocket + coordinate system (m). See :doc:`Positions and Coordinate Systems + `. + """ + # Below ~1 deg total incidence the normal force is too small for the + # moment/normal-force ratio to be well conditioned (a 0/0 limit at the + # origin, finite-precision noise just above it). Return the linearized + # aerodynamic center, which the nonlinear value converges to, so the + # result is continuous and never spikes as the rocket oscillates through + # zero incidence. + if alpha**2 + beta**2 < math.radians(1.0) ** 2 or ( + len(self.aerodynamic_surfaces) == 0 + ): + return self._aerodynamic_center_limit(alpha, beta, mach) + + total_x, total_y, _, moment_x, moment_y, _, _ = ( + self._aerodynamic_forces_and_moments(alpha, beta, mach, reynolds) + ) + + normal_force_sq = total_x**2 + total_y**2 + if normal_force_sq == 0: + return self._aerodynamic_center_limit(alpha, beta, mach) + # Axial offset (body frame) from the center of dry mass to the line of + # action of the resultant transverse force. + cp_offset = (moment_y * total_x - moment_x * total_y) / normal_force_sq + return self.center_of_dry_mass_position + self._csys * cp_offset + + def _aerodynamic_forces_and_moments(self, alpha, beta, mach, reynolds=0.0): + """Total body-frame aerodynamic force ``(R1, R2, R3)`` and moment + ``(M1, M2, M3)`` about the center of dry mass, summed over every + aerodynamic surface at a static state (zero rates), plus the + ``stream_speed`` used. + + Computed at unit air density, so the forces equal the dimensionless + coefficients times ``0.5 * stream_speed**2 * reference_area``; dynamic + pressure therefore cancels from any coefficient or center-of-pressure + ratio. The viscosity is chosen so each surface's Reynolds number (built + on its own reference length) is consistent with the requested + rocket-level Reynolds number (built on the diameter): + ``Re_surface = reynolds * reference_length / (2 * radius)``; a + non-positive Reynolds collapses to the vanishing-Reynolds limit. + """ + # Body-frame stream velocity reproducing (alpha, beta). + # ``compute_forces_and_moments`` negates it internally and recovers + # ``alpha = atan2(sv_y, sv_z)`` and ``beta = atan2(sv_x, sv_z)``. + stream_velocity = Vector([-math.tan(beta), -math.tan(alpha), -1.0]) + stream_speed = abs(stream_velocity) + omega = Vector([0, 0, 0]) + density = Function(1.0) + if reynolds > 0: + dynamic_viscosity = Function(stream_speed * 2 * self.radius / reynolds) + else: + dynamic_viscosity = Function(1e30) + + totals = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0] + for surface, _ in self.aerodynamic_surfaces: + cp = self.surfaces_cp_to_cdm[surface] + forces = surface.compute_forces_and_moments( + stream_velocity, stream_speed, mach, 1.0, cp, omega, + density, dynamic_viscosity, 0.0, + ) + totals = [acc + value for acc, value in zip(totals, forces)] + return (*totals, stream_speed) + + def aerodynamic_coefficients(self, alpha, beta, mach, reynolds=0.0): + """Total rocket aerodynamic coefficients at a given state, referenced to + the rocket cross-section area and diameter and taken about the center of + dry mass. + + Parameters + ---------- + alpha, beta : float + Angle of attack and sideslip, in radians. + mach : float + Free-stream Mach number. + reynolds : float, optional + Rocket-level Reynolds number. Default 0. + + Returns + ------- + dict + ``{"normal_force": C_N, "pitch_moment": C_m}`` -- the total + normal-force and pitch-moment (about the center of dry mass) + coefficient magnitudes. + + Notes + ----- + The rocket's axial (drag) coefficient is **not** included: the geometric + (Barrowman) surfaces carry no drag coefficient, the rocket drag being + supplied separately by ``power_off_drag``/``power_on_drag``. See + :meth:`Rocket.plots.drag_curves`. + """ + r1, r2, _, m1, m2, _, stream_speed = self._aerodynamic_forces_and_moments( + alpha, beta, mach, reynolds + ) + dynamic_pressure_area = 0.5 * stream_speed**2 * self.area + if dynamic_pressure_area == 0: + return {"normal_force": 0.0, "pitch_moment": 0.0} + reference_length = 2 * self.radius + return { + "normal_force": (r1**2 + r2**2) ** 0.5 / dynamic_pressure_area, + "pitch_moment": (m1**2 + m2**2) ** 0.5 + / (dynamic_pressure_area * reference_length), + } + + def aerodynamic_coefficients_full(self, alpha, beta, mach, reynolds=0.0): + """All six signed rocket-level aerodynamic coefficients at a state. + + Aggregates every aerodynamic surface into the vehicle's force and moment + coefficients, referenced to the rocket cross-section area and diameter + and taken about the center of dry mass, in the body aerodynamic frame: + + - ``cL`` (lift), ``cQ`` (side force), ``cD`` (drag); + - ``cm`` (pitch), ``cn`` (yaw), ``cl`` (roll). + + Unlike :meth:`aerodynamic_coefficients` (which returns unsigned + normal-force and pitch-moment magnitudes) these are signed and complete. + The drag coefficient ``cD`` is taken from the vehicle drag curve + (``power_off_drag``), since the geometric surfaces carry no drag + coefficient; this unifies the per-surface lift/moment model with the + separately supplied drag curve into a single coefficient set. + + Parameters + ---------- + alpha, beta : float + Angle of attack and sideslip, in radians. + mach : float + Free-stream Mach number. + reynolds : float, optional + Rocket-level Reynolds number. Default 0. + + Returns + ------- + dict + ``{"cL", "cQ", "cD", "cm", "cn", "cl"}``. + """ + r1, r2, r3, m1, m2, m3, stream_speed = self._aerodynamic_forces_and_moments( + alpha, beta, mach, reynolds + ) + dynamic_pressure_area = 0.5 * stream_speed**2 * self.area + if dynamic_pressure_area == 0: + return {c: 0.0 for c in ("cL", "cQ", "cD", "cm", "cn", "cl")} + reference_length = 2 * self.radius + dynamic_pressure_area_length = dynamic_pressure_area * reference_length + # Body-frame force/moment components map to the aerodynamic-frame + # coefficients (see GenericSurface.compute_forces_and_moments, which + # builds the body force from Vector([side, -lift, -drag])). + return { + "cL": -r2 / dynamic_pressure_area, + "cQ": r1 / dynamic_pressure_area, + "cD": self.power_off_drag_by_mach.get_value_opt(mach), + "cm": m1 / dynamic_pressure_area_length, + "cn": m2 / dynamic_pressure_area_length, + "cl": m3 / dynamic_pressure_area_length, + } + + def center_of_pressure_over_alpha(self, mach=0.0, beta=0.0, reynolds=0.0): + """Center of pressure position as a Function of angle of attack. + + Convenience wrapper around :meth:`center_of_pressure` that fixes the + Mach number, sideslip and Reynolds number and exposes the center of + pressure travel with angle of attack as a plottable :class:`Function`. + + Parameters + ---------- + mach : float, optional + Free-stream Mach number. Default 0. + beta : float, optional + Sideslip angle, in radians. Default 0. + reynolds : float, optional + Rocket-level Reynolds number. Default 0. + + Returns + ------- + Function + Center of pressure position (m) versus angle of attack (rad). + """ + return Function( + lambda alpha: self.center_of_pressure(alpha, beta, mach, reynolds), + inputs="Angle of Attack (rad)", + outputs="Center of Pressure Position (m)", + title="Center of Pressure vs Angle of Attack", + ) + + def stability_margin_over_alpha( + self, mach=0.0, beta=0.0, reynolds=0.0, time=0.0 + ): + """Stability margin in calibers as a Function of angle of attack. + + The center-of-gravity-to-center-of-pressure distance divided by the + rocket diameter, using the nonlinear :meth:`center_of_pressure` so the + margin reflects how the center of pressure moves with incidence. This is + the angle-of-attack analogue of :attr:`static_margin` (which is the + ``alpha = 0`` value as a function of time). + + Parameters + ---------- + mach : float, optional + Free-stream Mach number. Default 0. + beta : float, optional + Sideslip angle, in radians. Default 0. + reynolds : float, optional + Rocket-level Reynolds number. Default 0. + time : float, optional + Time at which the center of mass is evaluated, in seconds. Default 0 + (the fully loaded condition). + + Returns + ------- + Function + Stability margin (calibers) versus angle of attack (rad). + """ + center_of_pressure = self.center_of_pressure_over_alpha(mach, beta, reynolds) + center_of_mass = self.center_of_mass.get_value_opt(time) + diameter = 2 * self.radius + return Function( + lambda alpha: ( + (center_of_mass - center_of_pressure.get_value_opt(alpha)) + / diameter + * self._csys + ), + inputs="Angle of Attack (rad)", + outputs="Stability Margin (c)", + title="Stability Margin vs Angle of Attack", + ) + + def center_of_pressure_over_beta(self, mach=0.0, alpha=0.0, reynolds=0.0): + """Center of pressure position as a Function of sideslip angle. + + Yaw-plane companion to :meth:`center_of_pressure_over_alpha`: fixes the + Mach number, angle of attack and Reynolds number and exposes the center + of pressure travel with sideslip as a plottable :class:`Function`. + + Parameters + ---------- + mach : float, optional + Free-stream Mach number. Default 0. + alpha : float, optional + Angle of attack, in radians. Default 0. + reynolds : float, optional + Rocket-level Reynolds number. Default 0. + + Returns + ------- + Function + Center of pressure position (m) versus sideslip angle (rad). + """ + def _cp(beta): + # At the exact origin the CP is a 0/0 limit; the general + # center_of_pressure resolves it to the PITCH-plane aerodynamic + # center (consistent with static_margin). For a pure-sideslip sweep + # the correct limit is instead the YAW-plane aerodynamic center, + # which the nonlinear value converges to as beta grows -- use it at + # beta = 0 so the sweep stays continuous. + if alpha == 0.0 and beta == 0.0: + return self.aerodynamic_center_yaw.get_value_opt(mach) + return self.center_of_pressure(alpha, beta, mach, reynolds) + + return Function( + _cp, + inputs="Sideslip Angle (rad)", + outputs="Center of Pressure Position (m)", + title="Center of Pressure vs Sideslip Angle", + ) + + def stability_margin_over_beta( + self, mach=0.0, alpha=0.0, reynolds=0.0, time=0.0 + ): + """Stability margin in calibers as a Function of sideslip angle. + + Yaw-plane companion to :meth:`stability_margin_over_alpha`: the + center-of-gravity-to-center-of-pressure distance divided by the rocket + diameter, using the nonlinear :meth:`center_of_pressure` so the margin + reflects how the center of pressure moves with sideslip. + + Parameters + ---------- + mach : float, optional + Free-stream Mach number. Default 0. + alpha : float, optional + Angle of attack, in radians. Default 0. + reynolds : float, optional + Rocket-level Reynolds number. Default 0. + time : float, optional + Time at which the center of mass is evaluated, in seconds. Default 0 + (the fully loaded condition). + + Returns + ------- + Function + Stability margin (calibers) versus sideslip angle (rad). + """ + center_of_pressure = self.center_of_pressure_over_beta(mach, alpha, reynolds) + center_of_mass = self.center_of_mass.get_value_opt(time) + diameter = 2 * self.radius + return Function( + lambda beta: ( + (center_of_mass - center_of_pressure.get_value_opt(beta)) + / diameter + * self._csys + ), + inputs="Sideslip Angle (rad)", + outputs="Stability Margin (c)", + title="Stability Margin vs Sideslip Angle", + ) def evaluate_surfaces_cp_to_cdm(self): """Calculates the relative position of each aerodynamic surface center @@ -674,11 +1147,17 @@ def __evaluate_single_surface_cp_to_cdm(self, surface, position): (position.z - self.center_of_dry_mass_position) * self._csys, ] ) - # position of the center of pressure in body frame + # position of the force application point in body frame. Surfaces that + # carry their center-of-pressure offset in the moment coefficients + # (Barrowman surfaces) apply the force at the origin; surfaces that + # transport the moment geometrically use their center of pressure. + application_point = getattr( + surface, + "force_application_point", + Vector([surface.cpx, surface.cpy, surface.cpz]), + ) pos = ( - surface._rotation_surface_to_body - @ Vector([surface.cpx, surface.cpy, surface.cpz]) - + pos_origin + surface._rotation_surface_to_body @ application_point + pos_origin ) # TODO: this should be recomputed whenever cant angle changes for fin self.surfaces_cp_to_cdm[surface] = pos @@ -699,7 +1178,20 @@ def evaluate_stability_margin(self): ( ( self.center_of_mass.get_value_opt(time) - - self.cp_position.get_value_opt(mach) + - self.aerodynamic_center.get_value_opt(mach) + ) + / (2 * self.radius) + ) + * self._csys + ) + ) + # Yaw-plane stability margin (equal to the pitch plane when axisymmetric) + self.stability_margin_yaw.set_source( + lambda mach, time: ( + ( + ( + self.center_of_mass.get_value_opt(time) + - self.aerodynamic_center_yaw.get_value_opt(mach) ) / (2 * self.radius) ) @@ -723,7 +1215,7 @@ def evaluate_static_margin(self): lambda time: ( ( self.center_of_mass.get_value_opt(time) - - self.cp_position.get_value_opt(0) + - self.aerodynamic_center.get_value_opt(0) ) / (2 * self.radius) ) @@ -736,6 +1228,24 @@ def evaluate_static_margin(self): self.static_margin.set_discrete( lower=0, upper=self.motor.burn_out_time, samples=200 ) + + # Yaw-plane static margin (equal to the pitch plane when axisymmetric) + self.static_margin_yaw.set_source( + lambda time: ( + ( + self.center_of_mass.get_value_opt(time) + - self.aerodynamic_center_yaw.get_value_opt(0) + ) + / (2 * self.radius) + ) + ) + self.static_margin_yaw *= self._csys + self.static_margin_yaw.set_inputs("Time (s)") + self.static_margin_yaw.set_outputs("Static Margin - Yaw (c)") + self.static_margin_yaw.set_title("Static Margin - Yaw") + self.static_margin_yaw.set_discrete( + lower=0, upper=self.motor.burn_out_time, samples=200 + ) return self.static_margin def evaluate_dry_inertias(self): @@ -1152,6 +1662,65 @@ def add_surfaces(self, surfaces, positions): self.evaluate_stability_margin() self.evaluate_static_margin() + def add_vehicle_aerodynamic_surface( + self, coefficients, reference_position=None, name="Vehicle Aerodynamics" + ): + """Define the whole vehicle from a supplied set of aerodynamic + coefficients (a "rocket-as-:class:`GenericSurface`" model). + + Instead of (or in addition to) modeling each surface, this lets a user + fly the 6-DOF directly from a full-vehicle coefficient set, e.g. exported + from CFD, a wind tunnel, or OpenRocket. The coefficients are wrapped in a + single :class:`GenericSurface` referenced to the rocket cross-section + area and diameter and added through the standard aerodynamic-surface + path, so the equations of motion sum it like any other surface. + + Because it is just another aerodynamic surface, a vehicle coefficient set + can be **mixed** with modeled add-on surfaces (e.g. a measured body plus + modeled canards): they simply add. + + Parameters + ---------- + coefficients : dict + Aerodynamic coefficients ``cL, cQ, cD, cm, cn, cl`` (omitted ones + default to 0), each a number, callable, :class:`Function` or CSV + path of ``(alpha, beta, mach, reynolds, pitch_rate, yaw_rate, + roll_rate)`` -- the same input forms accepted by + :class:`GenericSurface`. + reference_position : int, float, optional + Axial station (in the user coordinate system) about which the + supplied moment coefficients are defined and where the resultant + force is applied. Defaults to the center of dry mass position. The + supplied moment coefficients are taken about this fixed station. + name : str, optional + Name of the surface. Default ``"Vehicle Aerodynamics"``. + + Returns + ------- + GenericSurface + The created vehicle aerodynamic surface (also added to the rocket). + + Notes + ----- + A single vehicle coefficient set necessarily drops per-surface locals + (the ``omega x r`` velocity at each surface, per-surface Reynolds, rail + buttons and individual-fin roll). For controllable vehicle coefficients + (deflection axes), build a + :class:`ControllableGenericSurface` and add it with + :meth:`add_surfaces` / ``add_controllable_surface`` instead. + """ + if reference_position is None: + reference_position = self.center_of_dry_mass_position + + surface = GenericSurface( + reference_area=self.area, + reference_length=2 * self.radius, + coefficients=coefficients, + name=name, + ) + self.add_surfaces(surface, reference_position) + return surface + def _add_controllers(self, controllers): """Adds a controller to the rocket. @@ -1987,6 +2556,73 @@ def controller_wrapper(**kwargs): else: return air_brakes + def add_controllable_surface( + self, + surface, + position, + controller_function, + sampling_rate, + controlled_object_name="controllable_surface", + context=None, + name="Controller", + controller_needs=None, + return_controller=False, + ): + """Add a controllable aerodynamic surface and the controller that drives + its deflection during flight. + + The surface is added like any other aerodynamic surface (so it flows + through the standard per-surface force/moment computation), and a + controller is registered to mutate the surface's control variables each + sample. The controller function should set the surface's deflection via + ``surface.set_control(name, value)``. + + Parameters + ---------- + surface : ControllableGenericSurface + The controllable surface to add. + position : int, float, tuple, list, Vector + Position of the surface, in the same convention as + :meth:`add_surfaces`. + controller_function : callable + Control logic, ``controller_function(**kwargs) -> dict or None``. + See :class:`rocketpy.control.controller._Controller` for the + available ``kwargs``. The controlled surface is exposed under + ``controlled_object_name``. + sampling_rate : float + Controller sampling rate in hertz. + controlled_object_name : str, optional + Friendly name under which the surface is exposed in the controller + ``kwargs``. Default ``"controllable_surface"``. + context : dict, optional + Initial persistent controller context. Default ``None``. + name : str, optional + Controller name. Default ``"Controller"``. + controller_needs : list or frozenset of str or None, optional + Expensive simulation values the controller accesses. + return_controller : bool, optional + If True, also return the created controller. Default False. + + Returns + ------- + ControllableGenericSurface or tuple + The surface, or ``(surface, controller)`` if ``return_controller``. + """ + self.add_surfaces(surface, position) + controller = _Controller( + controller_function=controller_function, + controlled_objects=surface, + controlled_objects_name=controlled_object_name, + sampling_rate=sampling_rate, + context=context if context is not None else {}, + name=name, + controller_needs=controller_needs, + ) + self._add_controllers(controller) + if return_controller: + return surface, controller + return surface + def set_rail_buttons( self, upper_button_position, @@ -2230,7 +2866,7 @@ def to_dict(self, **kwargs): if kwargs.get("include_outputs", False): thrust_to_weight = self.thrust_to_weight - cp_position = self.cp_position + aerodynamic_center = self.aerodynamic_center stability_margin = self.stability_margin center_of_mass = self.center_of_mass motor_center_of_mass_position = self.motor_center_of_mass_position @@ -2243,7 +2879,9 @@ def to_dict(self, **kwargs): thrust_to_weight = thrust_to_weight.set_discrete_based_on_model( self.motor.thrust, mutate_self=False ) - cp_position = cp_position.set_discrete(0, 4, 25, mutate_self=False) + aerodynamic_center = aerodynamic_center.set_discrete( + 0, 4, 25, mutate_self=False + ) stability_margin = stability_margin.set_discrete( (0, self.motor.burn_time[0]), (2, self.motor.burn_time[1]), @@ -2293,7 +2931,7 @@ def to_dict(self, **kwargs): rocket_dict["cp_eccentricity_y"] = self.cp_eccentricity_y rocket_dict["thrust_eccentricity_x"] = self.thrust_eccentricity_x rocket_dict["thrust_eccentricity_y"] = self.thrust_eccentricity_y - rocket_dict["cp_position"] = cp_position + rocket_dict["aerodynamic_center"] = aerodynamic_center rocket_dict["stability_margin"] = stability_margin rocket_dict["static_margin"] = self.static_margin rocket_dict["nozzle_position"] = self.nozzle_position diff --git a/rocketpy/simulation/flight.py b/rocketpy/simulation/flight.py index baff48a38..11eb76477 100644 --- a/rocketpy/simulation/flight.py +++ b/rocketpy/simulation/flight.py @@ -2305,24 +2305,289 @@ def static_margin(self): @funcify_method("Time (s)", "Stability Margin (c)", "linear", "zero") def stability_margin(self): - """Stability margin of the rocket along the flight, it considers the - variation of the center of pressure position according to the mach - number, as well as the variation of the center of gravity position - according to the propellant mass evolution. + """Linear stability margin along the flight, in calibers. - Parameters - ---------- - None + This is the classical (aerodynamic-center) margin: it evaluates the + rocket's linearized stability margin + (:meth:`Rocket.stability_margin`) at the realized flight Mach and time at + each instant, capturing the Mach variation of the aerodynamic center + together with the center-of-mass shift as propellant burns. It is + well-conditioned and never spikes. For the nonlinear margin that follows + the center of pressure at the actual angle of attack/sideslip, see + :meth:`realized_stability_margin`. Returns ------- stability : rocketpy.Function - Stability margin as a rocketpy.Function of time. The stability margin - is defined as the distance between the center of pressure and the - center of gravity, divided by the rocket diameter. + Stability margin in calibers as a function of time. A positive + margin (aerodynamic center behind the center of mass) is the classic + passive-stability condition. """ return [(t, self.rocket.stability_margin(m, t)) for t, m in self.mach_number] + @funcify_method("Time (s)", "Stability Margin - Yaw (c)", "linear", "zero") + def stability_margin_yaw(self): + """Linear yaw-plane stability margin along the flight, in calibers. + + Yaw-plane counterpart of :meth:`stability_margin`, using the rocket's + yaw-plane aerodynamic center (:meth:`Rocket.stability_margin_yaw`). + Equals :meth:`stability_margin` for an axisymmetric rocket; for a + non-axisymmetric rocket (e.g. single-plane canards) it differs, since the + pitch and yaw aerodynamic centers no longer coincide. + + Returns + ------- + stability : rocketpy.Function + Yaw-plane stability margin in calibers as a function of time. + """ + return [ + (t, self.rocket.stability_margin_yaw(m, t)) for t, m in self.mach_number + ] + + @funcify_method("Time (s)", "Realized Stability Margin (c)", "linear", "zero") + def realized_stability_margin(self): + """Nonlinear (realized) stability margin along the flight, in calibers. + + Co-equal companion to :meth:`stability_margin`: instead of the + aerodynamic center it uses the rocket's *nonlinear* center of pressure + (:meth:`Rocket.center_of_pressure`) at the realized flight state -- the + actual angle of attack, sideslip, Mach and Reynolds at each time step -- + so it reveals how the center of pressure travels with incidence (and, + for non-axisymmetric rockets, combines the pitch and yaw planes at the + actual combined incidence). + + For a non-axisymmetric rocket the margin is **direction-dependent** (the + pitch and yaw planes differ), and during most of the flight the rocket + flies at near-zero incidence, where the *direction* of the residual + incidence vector is numerical noise (slight coning). Reporting the + directional center of pressure there would make the margin swing between + the pitch- and yaw-plane values. To avoid that, the realized value is + blended into the linear margin by how much real incidence there is: at + negligible incidence the result is the linear :meth:`stability_margin`, + and only a genuine disturbance (a few degrees of incidence) reveals the + nonlinear travel. The value also falls back to the linear margin where + the dynamic pressure is negligible (rail, rest, apogee). + + Returns + ------- + stability : rocketpy.Function + Realized stability margin in calibers as a function of time. + """ + csys = self.rocket._csys + diameter = 2 * self.rocket.radius + time = self.time + + alpha = np.array( + [self.partial_angle_of_attack.get_value_opt(t) for t in time] + ) + beta = np.array([self.angle_of_sideslip.get_value_opt(t) for t in time]) + + center_of_pressure = np.array( + [ + self.rocket.center_of_pressure( + np.deg2rad(a), + np.deg2rad(b), + self.mach_number.get_value_opt(t), + self.reynolds_number.get_value_opt(t), + ) + for a, b, t in zip(alpha, beta, time) + ] + ) + center_of_mass = np.array( + [self.rocket.center_of_mass.get_value_opt(t) for t in time] + ) + margin_realized = (center_of_mass - center_of_pressure) / diameter * csys + + margin_model = np.array( + [ + self.rocket.stability_margin.get_value_opt( + self.mach_number.get_value_opt(t), t + ) + for t in time + ] + ) + + # Weight the (direction-dependent) realized value by how much real + # incidence there is, with a smoothstep ramp up to ~2 deg, so the + # near-zero-incidence direction noise collapses to the linear margin. + incidence = np.hypot(alpha, beta) + weight = np.clip(incidence / 2.0, 0.0, 1.0) + weight = weight**2 * (3.0 - 2.0 * weight) + margin_blended = (1.0 - weight) * margin_model + weight * margin_realized + + # Fall back fully to the linear margin where the rocket is barely moving + # (dynamic pressure below 1% of its flight-wide peak: rail, rest, apogee). + dynamic_pressure = np.array( + [self.dynamic_pressure.get_value_opt(t) for t in time] + ) + meaningful = dynamic_pressure > 0.01 * dynamic_pressure.max() + margin = np.where(meaningful, margin_blended, margin_model) + + return np.column_stack((time, margin)) + + # Dynamic stability + def _lateral_inertia(self, dry_lateral_inertia, motor_lateral_inertia): + """Lateral moment of inertia about the instantaneous center of mass, as + an array over ``self.time``. Uses the reduced-mass formulation of the + equations of motion: ``I_L = I_dry + I_motor(t) + mu(t) b^2`` with + ``mu`` the dry/propellant reduced mass and ``b`` the (initial) + dry-mass-to-propellant distance.""" + dry_mass = self.rocket.dry_mass + b = ( + -( + self.rocket.center_of_propellant_position.get_value_opt(0) + - self.rocket.center_of_dry_mass_position + ) + * self.rocket._csys + ) + inertia = np.empty(len(self.time)) + for i, t in enumerate(self.time): + propellant_mass = self.rocket.motor.propellant_mass.get_value_opt(t) + total = propellant_mass + dry_mass + mu = (propellant_mass * dry_mass / total) if total > 0 else 0.0 + inertia[i] = ( + dry_lateral_inertia + + motor_lateral_inertia.get_value_opt(t) + + mu * b**2 + ) + return inertia + + def _dynamic_stability(self, lift_slope, stability_margin, lateral_inertia): + """Linearized oscillator coefficients for one plane, as arrays over + ``self.time``: corrective moment coefficient ``C1`` (restoring moment per + radian), damping moment coefficient ``C2`` (aerodynamic + jet), undamped + natural frequency ``omega_n`` and damping ratio ``zeta``. + + ``lift_slope`` is the rocket's total normal-force-curve slope for the + plane (``total_lift_coeff_der`` for pitch, ``total_side_coeff_der`` for + yaw); ``stability_margin`` is the matching linear margin + ``Function(mach, time)``; ``lateral_inertia`` is the array from + :meth:`_lateral_inertia`. + """ + area = self.rocket.area + diameter = 2 * self.rocket.radius + csys = self.rocket._csys + nozzle_position = self.rocket.nozzle_position + mass_flow_rate = self.rocket.motor.total_mass_flow_rate + + corrective = np.empty(len(self.time)) + damping = np.empty(len(self.time)) + for i, t in enumerate(self.time): + mach = self.mach_number.get_value_opt(t) + dynamic_pressure = self.dynamic_pressure.get_value_opt(t) + speed = self.speed.get_value_opt(t) + density = self.density.get_value_opt(t) + center_of_mass = self.rocket.center_of_mass.get_value_opt(t) + + # Corrective moment per radian: q A C_Nalpha (x_cm - x_ac). + margin = stability_margin.get_value_opt(mach, t) # calibers + corrective[i] = ( + dynamic_pressure + * area + * lift_slope.get_value_opt(mach) + * margin + * diameter + ) + + # Aerodynamic damping: 0.5 rho V A sum_i (A_i/A) C_Nalpha_i arm_i^2. + damping_aero = 0.0 + for surface, position in self.rocket.aerodynamic_surfaces: + slope = surface.lift_coefficient_derivative.get_value_opt(mach) + cp_position = ( + position.z + - csys * surface.center_of_pressure_z.get_value_opt(mach) + ) + arm = cp_position - center_of_mass + ref_factor = surface.reference_area / area + damping_aero += ref_factor * slope * arm**2 + damping_aero *= 0.5 * density * speed * area + + # Jet (propulsive) damping: mdot (x_nozzle - x_cm)^2. + damping_jet = abs(mass_flow_rate.get_value_opt(t)) * ( + nozzle_position - center_of_mass + ) ** 2 + damping[i] = damping_aero + damping_jet + + positive_corrective = np.clip(corrective, 0.0, None) + with np.errstate(divide="ignore", invalid="ignore"): + natural_frequency = np.sqrt(positive_corrective / lateral_inertia) + denominator = 2.0 * np.sqrt(positive_corrective * lateral_inertia) + damping_ratio = np.divide( + damping, + denominator, + out=np.zeros_like(damping), + where=denominator > 0, + ) + return corrective, damping, natural_frequency, damping_ratio + + @funcify_method("Time (s)", "Corrective Moment Coefficient (N m/rad)", "linear") + def corrective_moment_coefficient(self): + """Pitch-plane corrective (restoring) moment coefficient ``C1`` as a + function of time -- the aerodynamic restoring moment per radian of angle + of attack. Positive for a statically stable rocket.""" + inertia = self._lateral_inertia(self.rocket.dry_I_11, self.rocket.motor.I_11) + corrective, _, _, _ = self._dynamic_stability( + self.rocket.total_lift_coeff_der, self.rocket.stability_margin, inertia + ) + return np.column_stack((self.time, corrective)) + + @funcify_method("Time (s)", "Damping Moment Coefficient (N m s/rad)", "linear") + def damping_moment_coefficient(self): + """Pitch-plane damping moment coefficient ``C2`` as a function of time -- + the moment opposing the pitch rate, summing aerodynamic damping (from + every surface) and propulsive (jet) damping.""" + inertia = self._lateral_inertia(self.rocket.dry_I_11, self.rocket.motor.I_11) + _, damping, _, _ = self._dynamic_stability( + self.rocket.total_lift_coeff_der, self.rocket.stability_margin, inertia + ) + return np.column_stack((self.time, damping)) + + @funcify_method("Time (s)", "Pitch Natural Frequency (rad/s)", "linear") + def pitch_natural_frequency(self): + """Undamped natural frequency of the pitch oscillation, + ``omega_n = sqrt(C1 / I_L)``, as a function of time (rad/s).""" + inertia = self._lateral_inertia(self.rocket.dry_I_11, self.rocket.motor.I_11) + _, _, natural_frequency, _ = self._dynamic_stability( + self.rocket.total_lift_coeff_der, self.rocket.stability_margin, inertia + ) + return np.column_stack((self.time, natural_frequency)) + + @funcify_method("Time (s)", "Pitch Damping Ratio", "linear") + def pitch_damping_ratio(self): + """Damping ratio of the pitch oscillation, + ``zeta = C2 / (2 sqrt(C1 I_L))``, as a function of time. ``zeta < 1`` is + underdamped (oscillatory), ``zeta > 1`` overdamped.""" + inertia = self._lateral_inertia(self.rocket.dry_I_11, self.rocket.motor.I_11) + _, _, _, damping_ratio = self._dynamic_stability( + self.rocket.total_lift_coeff_der, self.rocket.stability_margin, inertia + ) + return np.column_stack((self.time, damping_ratio)) + + @funcify_method("Time (s)", "Yaw Natural Frequency (rad/s)", "linear") + def yaw_natural_frequency(self): + """Undamped natural frequency of the yaw oscillation as a function of + time (rad/s). Equals :meth:`pitch_natural_frequency` for an axisymmetric + rocket.""" + inertia = self._lateral_inertia(self.rocket.dry_I_22, self.rocket.motor.I_22) + _, _, natural_frequency, _ = self._dynamic_stability( + self.rocket.total_side_coeff_der, + self.rocket.stability_margin_yaw, + inertia, + ) + return np.column_stack((self.time, natural_frequency)) + + @funcify_method("Time (s)", "Yaw Damping Ratio", "linear") + def yaw_damping_ratio(self): + """Damping ratio of the yaw oscillation as a function of time. Equals + :meth:`pitch_damping_ratio` for an axisymmetric rocket.""" + inertia = self._lateral_inertia(self.rocket.dry_I_22, self.rocket.motor.I_22) + _, _, _, damping_ratio = self._dynamic_stability( + self.rocket.total_side_coeff_der, + self.rocket.stability_margin_yaw, + inertia, + ) + return np.column_stack((self.time, damping_ratio)) + # Rail Button Forces @cached_property diff --git a/rocketpy/simulation/helpers/flight_derivatives.py b/rocketpy/simulation/helpers/flight_derivatives.py index 1426e2cd3..ac28b08cd 100644 --- a/rocketpy/simulation/helpers/flight_derivatives.py +++ b/rocketpy/simulation/helpers/flight_derivatives.py @@ -47,6 +47,65 @@ def _compute_drag_7d_inputs( return alpha, beta, stream_mach, reynolds +def _aerodynamic_drag_force( + flight, time, rho, stream_speed, alpha, beta, mach, reynolds, omega +): + """Total rocket axial aerodynamic (drag) force, including air brakes. + + Selects the power-on/power-off drag curve based on the motor burn state, and + then adds (or, when ``override_rocket_drag`` is set, substitutes) the drag of + any deployed air brakes, evaluated through the generic-surface coefficient + machinery. + + Parameters + ---------- + flight : Flight + Flight object providing the rocket. + time : float + Simulation time, used to select the power-on vs power-off drag curve. + rho : float + Air density. + stream_speed : float + Freestream speed magnitude. + alpha, beta, mach, reynolds : float + Standard aerodynamic coefficient inputs at the current state. + omega : tuple of float + Body angular rates ``(omega1, omega2, omega3)``. + + Returns + ------- + float + The axial (body z) aerodynamic drag force. + """ + rocket = flight.rocket + if time < rocket.motor.burn_out_time: + drag_coefficient = rocket.power_on_drag_7d( + alpha, beta, mach, reynolds, omega[0], omega[1], omega[2] + ) + else: + drag_coefficient = rocket.power_off_drag_7d( + alpha, beta, mach, reynolds, omega[0], omega[1], omega[2] + ) + drag_force = -0.5 * rho * stream_speed**2 * rocket.area * drag_coefficient + + # Air brakes are drag-only and may override the rocket drag. + for air_brakes in rocket.air_brakes: + if air_brakes.deployment_level > 0: + air_brakes_cd = air_brakes.cD.get_value_opt( + *air_brakes._coefficient_arguments( + alpha, beta, mach, reynolds, omega[0], omega[1], omega[2] + ) + ) + air_brakes_force = ( + -0.5 * rho * stream_speed**2 * air_brakes.reference_area * air_brakes_cd + ) + if air_brakes.override_rocket_drag: + drag_force = air_brakes_force # Substitutes rocket drag + else: + drag_force += air_brakes_force + return drag_force + + def udot_rail1(flight, t, u, post_processing=False): """Compute the 1-DOF rail-flight state derivative. @@ -282,43 +341,10 @@ def u_dot(flight, t, u, post_processing=False): rho, dynamic_viscosity, ) - if t < flight.rocket.motor.burn_out_time: - drag_coeff = flight.rocket.power_on_drag_7d( - alpha, - beta, - mach, - reynolds, - omega1, - omega2, - omega3, - ) - else: - drag_coeff = flight.rocket.power_off_drag_7d( - alpha, - beta, - mach, - reynolds, - omega1, - omega2, - omega3, - ) - R3 = -0.5 * rho * (free_stream_speed**2) * flight.rocket.area * drag_coeff - for air_brakes in flight.rocket.air_brakes: - if air_brakes.deployment_level > 0: - air_brakes_cd = air_brakes.drag_coefficient.get_value_opt( - air_brakes.deployment_level, free_stream_mach - ) - air_brakes_force = ( - -0.5 - * rho - * (free_stream_speed**2) - * air_brakes.reference_area - * air_brakes_cd - ) - if air_brakes.override_rocket_drag: - R3 = air_brakes_force # Substitutes rocket drag coefficient - else: - R3 += air_brakes_force + R3 = _aerodynamic_drag_force( + flight, t, rho, free_stream_speed, alpha, beta, mach, reynolds, + (omega1, omega2, omega3), + ) # Off center moment M1 += flight.rocket.cp_eccentricity_y * R3 M2 -= flight.rocket.cp_eccentricity_x * R3 @@ -549,31 +575,12 @@ def u_dot_generalized_3dof(flight, t, u, post_processing=False): dynamic_viscosity, ) - # Drag computation - if t < flight.rocket.motor.burn_out_time: - cd = flight.rocket.power_on_drag_7d( - alpha, beta, mach, reynolds, omega1, omega2, omega3 - ) - else: - cd = flight.rocket.power_off_drag_7d( - alpha, beta, mach, reynolds, omega1, omega2, omega3 - ) - + # Drag computation (rocket body drag + air brakes) R1, R2 = 0, 0 - R3 = -0.5 * rho * free_stream_speed**2 * flight.rocket.area * cd - - for air_brake in flight.rocket.air_brakes: - if air_brake.deployment_level > 0: - ab_cd = air_brake.drag_coefficient.get_value_opt( - air_brake.deployment_level, mach - ) - ab_force = ( - -0.5 * rho * free_stream_speed**2 * air_brake.reference_area * ab_cd - ) - if air_brake.override_rocket_drag: - R3 = ab_force - else: - R3 += ab_force + R3 = _aerodynamic_drag_force( + flight, t, rho, free_stream_speed, alpha, beta, mach, reynolds, + (omega1, omega2, omega3), + ) # Velocity in body frame vb_body = Kt @ v @@ -806,43 +813,12 @@ def u_dot_generalized(flight, t, u, post_processing=False): + flight.rocket.motor.pressure_thrust(pressure), 0, ) - drag_coeff = flight.rocket.power_on_drag_7d( - alpha, - beta, - mach, - reynolds, - omega1, - omega2, - omega3, - ) else: net_thrust = 0 - drag_coeff = flight.rocket.power_off_drag_7d( - alpha, - beta, - mach, - reynolds, - omega1, - omega2, - omega3, - ) - R3 += -0.5 * rho * (free_stream_speed**2) * flight.rocket.area * drag_coeff - for air_brakes in flight.rocket.air_brakes: - if air_brakes.deployment_level > 0: - air_brakes_cd = air_brakes.drag_coefficient.get_value_opt( - air_brakes.deployment_level, free_stream_mach - ) - air_brakes_force = ( - -0.5 - * rho - * (free_stream_speed**2) - * air_brakes.reference_area - * air_brakes_cd - ) - if air_brakes.override_rocket_drag: - R3 = air_brakes_force # Substitutes rocket drag coefficient - else: - R3 += air_brakes_force + R3 = _aerodynamic_drag_force( + flight, t, rho, free_stream_speed, alpha, beta, mach, reynolds, + (omega1, omega2, omega3), + ) # Get rocket velocity in body frame velocity_in_body_frame = Kt @ v # Calculate lift and moment for each component of the rocket From b45178fa5d6667d4207c32356e37d935a64a4d74 Mon Sep 17 00:00:00 2001 From: MateusStano Date: Sat, 27 Jun 2026 15:52:10 -0300 Subject: [PATCH 02/22] ENH: second draft --- rocketpy/plots/aero_surface_plots.py | 85 +- rocketpy/plots/flight_plots.py | 11 +- rocketpy/plots/rocket_plots.py | 99 +-- rocketpy/prints/aero_surface_prints.py | 96 +-- rocketpy/rocket/aero_surface/__init__.py | 6 +- .../rocket/aero_surface/_barrowman_surface.py | 8 +- .../rocket/aero_surface/aero_coefficient.py | 471 +++++++++-- rocketpy/rocket/aero_surface/air_brakes.py | 9 +- .../controllable_generic_surface.py | 15 +- rocketpy/rocket/aero_surface/fins/fin.py | 26 +- .../rocket/aero_surface/generic_surface.py | 233 +++-- .../aero_surface/linear_generic_surface.py | 85 +- rocketpy/rocket/aero_surface/rail_buttons.py | 4 + rocketpy/rocket/point_mass_rocket.py | 8 +- rocketpy/rocket/rocket.py | 795 +++++------------- rocketpy/simulation/flight.py | 44 +- .../simulation/helpers/flight_derivatives.py | 43 +- 17 files changed, 942 insertions(+), 1096 deletions(-) diff --git a/rocketpy/plots/aero_surface_plots.py b/rocketpy/plots/aero_surface_plots.py index fe9eec783..a3753d660 100644 --- a/rocketpy/plots/aero_surface_plots.py +++ b/rocketpy/plots/aero_surface_plots.py @@ -1,6 +1,4 @@ # pylint: disable=too-many-statements -from abc import ABC, abstractmethod - import matplotlib.pyplot as plt import numpy as np from matplotlib.patches import Ellipse @@ -8,16 +6,16 @@ from .plot_helpers import show_or_save_plot -class _AeroSurfacePlots(ABC): - """Abstract class that contains all aero surface plots.""" +class _GenericSurfacePlots: + """Base plots for a generic aerodynamic surface.""" def __init__(self, aero_surface): """Initialize the class Parameters ---------- - aero_surface : rocketpy.AeroSurface - AeroSurface object to be plotted + aero_surface : rocketpy.GenericSurface + Aerodynamic surface object to be plotted Returns ------- @@ -25,20 +23,8 @@ def __init__(self, aero_surface): """ self.aero_surface = aero_surface - @abstractmethod def draw(self, *, filename=None): - pass - - def lift(self): - """Plots the lift coefficient of the aero surface as a function of Mach - and the angle of attack. A 3D plot is expected. See the rocketpy.Function - class for more information on how this plot is made. - - Returns - ------- - None - """ - self.aero_surface.cl() + """A plain generic surface has no geometry to draw.""" # Coefficients swept against their most relevant incidence angle: pitch-plane # coefficients vs. angle of attack, yaw-plane ones vs. sideslip. @@ -115,20 +101,40 @@ def coefficients(self, *, mach=0.3, angle_range_deg=15.0, filename=None): show_or_save_plot(filename) def all(self): - """Plots all aero surface plots. + """Plots the generic surface's aerodynamic coefficients.""" + self.coefficients() + + +class _LinearGenericSurfacePlots(_GenericSurfacePlots): + """Plots for a linear generic surface; same plots as the generic base.""" + + +class _BarrowmanSurfacePlots(_LinearGenericSurfacePlots): + """Plots shared by the geometry-defined (Barrowman) surfaces: adds the + geometry drawing and the lift-coefficient surface plot.""" + + def lift(self): + """Plots the lift coefficient of the aero surface as a function of Mach + and the angle of attack. A 3D plot is expected. See the rocketpy.Function + class for more information on how this plot is made. Returns ------- None """ + self.aero_surface.cl() + + def all(self): + """Plots the surface geometry, the lift coefficient and the + aerodynamic coefficients.""" self.draw() self.lift() self.coefficients() -class _NoseConePlots(_AeroSurfacePlots): +class _NoseConePlots(_BarrowmanSurfacePlots): """Class that contains all nosecone plots. This class inherits from the - _AeroSurfacePlots class.""" + _BarrowmanSurfacePlots class.""" def draw(self, *, filename=None): """Draw the nosecone shape along with some important information, @@ -211,9 +217,9 @@ def draw(self, *, filename=None): show_or_save_plot(filename) -class _FinsPlots(_AeroSurfacePlots): +class _FinsPlots(_BarrowmanSurfacePlots): """Abstract class that contains all fin plots. This class inherits from the - _AeroSurfacePlots class.""" + _BarrowmanSurfacePlots class.""" def airfoil(self, *, filename=None): """Plots the airfoil information when the fin has an airfoil shape. If @@ -293,9 +299,9 @@ def all(self, *, filename=None): self.coefficients(filename=filename) -class _FinPlots(_AeroSurfacePlots): +class _FinPlots(_BarrowmanSurfacePlots): """Abstract class that contains all fin plots. This class inherits from the - _AeroSurfacePlots class.""" + _BarrowmanSurfacePlots class.""" def airfoil(self, *, filename=None): """Plots the airfoil information when the fin has an airfoil shape. If @@ -918,7 +924,7 @@ def draw(self, *, filename=None): show_or_save_plot(filename) -class _TailPlots(_AeroSurfacePlots): +class _TailPlots(_BarrowmanSurfacePlots): """Class that contains all tail plots.""" def draw(self, *, filename=None): @@ -926,7 +932,7 @@ def draw(self, *, filename=None): pass -class _AirBrakesPlots(_AeroSurfacePlots): +class _AirBrakesPlots(_GenericSurfacePlots): """Class that contains all air brakes plots.""" def drag_coefficient_curve(self): @@ -947,26 +953,3 @@ def all(self): None """ self.drag_coefficient_curve() - - -class _GenericSurfacePlots(_AeroSurfacePlots): - """Class that contains all generic surface plots.""" - - def draw(self, *, filename=None): - pass - - def all(self): - """Plots all generic surface plots (the aerodynamic coefficients).""" - self.coefficients() - - -class _LinearGenericSurfacePlots(_AeroSurfacePlots): - """Class that contains all linear generic surface plots.""" - - def draw(self, *, filename=None): - pass - - def all(self): - """Plots all linear generic surface plots (the aerodynamic - coefficients).""" - self.coefficients() diff --git a/rocketpy/plots/flight_plots.py b/rocketpy/plots/flight_plots.py index 681d08b13..f162f268b 100644 --- a/rocketpy/plots/flight_plots.py +++ b/rocketpy/plots/flight_plots.py @@ -1366,15 +1366,20 @@ def dynamic_stability_data(self, *, filename=None): if asymmetric: yaw_freq = self.flight.yaw_natural_frequency ax1.plot( - yaw_freq[:, 0], yaw_freq[:, 1] / (2 * np.pi), "--", + yaw_freq[:, 0], + yaw_freq[:, 1] / (2 * np.pi), + "--", label="Yaw natural freq.", ) # Roll rate as a frequency: where it crosses the natural frequency the # rocket is in roll resonance (roll-pitch/yaw coupling). roll_rate = self.flight.w3 ax1.plot( - roll_rate[:, 0], np.abs(roll_rate[:, 1]) / (2 * np.pi), ":", - color="tab:red", label="Roll rate (resonance if crossing)", + roll_rate[:, 0], + np.abs(roll_rate[:, 1]) / (2 * np.pi), + ":", + color="tab:red", + label="Roll rate (resonance if crossing)", ) ax1.set_title("Natural Frequency & Roll Rate") ax1.set_xlabel("Time (s)") diff --git a/rocketpy/plots/rocket_plots.py b/rocketpy/plots/rocket_plots.py index 561732534..95a746151 100644 --- a/rocketpy/plots/rocket_plots.py +++ b/rocketpy/plots/rocket_plots.py @@ -125,78 +125,6 @@ def stability_margin_yaw(self): alpha=1, ) - def stability_margin_over_alpha(self, *, filename=None): - """Plots the stability margin in calibers as a function of angle of - attack, for a range of Mach numbers. Built on the nonlinear center of - pressure, it shows how the margin changes with incidence -- the - angle-of-attack analogue of the static margin, which a Barrowman - (Mach-only) estimate cannot capture. Evaluated at the loaded center of - mass (``time = 0``). - - Parameters - ---------- - filename : str | None, optional - The path the plot should be saved to. By default None, in which case - the plot will be shown instead of saved. Supported file endings are: - eps, jpg, jpeg, pdf, pgf, png, ps, raw, rgba, svg, svgz, tif, tiff - and webp (these are the formats supported by matplotlib). - - Returns - ------- - None - """ - alphas_deg = np.linspace(0, 15, 50) - alphas_rad = np.radians(alphas_deg) - - _, ax = plt.subplots() - for mach in (0.1, 0.5, 0.8, 1.2, 2.0): - margin = self.rocket.stability_margin_over_alpha(mach=mach) - margin_values = [margin.get_value_opt(a) for a in alphas_rad] - ax.plot(alphas_deg, margin_values, label=f"Mach {mach}") - - ax.set_title("Stability Margin vs Angle of Attack (loaded)") - ax.set_xlabel("Angle of Attack (deg)") - ax.set_ylabel("Stability Margin (c)") - ax.legend(loc="best", shadow=True) - plt.grid(True) - show_or_save_plot(filename) - - def stability_margin_over_beta(self, *, filename=None): - """Plots the stability margin in calibers as a function of sideslip - angle, for a range of Mach numbers -- the yaw-plane companion to - :meth:`stability_margin_over_alpha`. Most informative for - non-axisymmetric rockets, whose yaw-plane center of pressure differs - from the pitch-plane one. Evaluated at the loaded center of mass - (``time = 0``). - - Parameters - ---------- - filename : str | None, optional - The path the plot should be saved to. By default None, in which case - the plot will be shown instead of saved. Supported file endings are: - eps, jpg, jpeg, pdf, pgf, png, ps, raw, rgba, svg, svgz, tif, tiff - and webp (these are the formats supported by matplotlib). - - Returns - ------- - None - """ - betas_deg = np.linspace(0, 15, 50) - betas_rad = np.radians(betas_deg) - - _, ax = plt.subplots() - for mach in (0.1, 0.5, 0.8, 1.2, 2.0): - margin = self.rocket.stability_margin_over_beta(mach=mach) - margin_values = [margin.get_value_opt(b) for b in betas_rad] - ax.plot(betas_deg, margin_values, label=f"Mach {mach}") - - ax.set_title("Stability Margin vs Sideslip Angle (loaded)") - ax.set_xlabel("Sideslip Angle (deg)") - ax.set_ylabel("Stability Margin (c)") - ax.legend(loc="best", shadow=True) - plt.grid(True) - show_or_save_plot(filename) - # pylint: disable=too-many-statements def drag_curves(self, *, filename=None): """Plots power off and on drag curves of the rocket as a function of time. @@ -275,8 +203,7 @@ def aerodynamic_coefficients(self, *, filename=None): _, (ax1, ax2) = plt.subplots(1, 2, figsize=(11, 4.5)) for mach in (0.1, 0.5, 0.8, 1.2, 2.0): coeffs = [ - self.rocket.aerodynamic_coefficients(a, 0.0, mach) - for a in alphas_rad + self.rocket.aerodynamic_coefficients(a, 0.0, mach) for a in alphas_rad ] ax1.plot( alphas_deg, [c["normal_force"] for c in coeffs], label=f"Mach {mach}" @@ -845,24 +772,24 @@ def _draw_center_of_mass_and_pressure(self, ax, plane="xz"): label="Center of Pressure Range", ) - ax.scatter( - cp, 0, label="Center of Pressure", color="red", s=10, zorder=10 - ) + ax.scatter(cp, 0, label="Center of Pressure", color="red", s=10, zorder=10) def _center_of_pressure_range(self, plane, max_angle=np.deg2rad(15), samples=31): """Min and max center-of-pressure position over an incidence sweep. Sweeps the angle of attack (xz plane) or sideslip (yz plane) from 0 to - ``max_angle`` using :meth:`Rocket.center_of_pressure_over_alpha` / - :meth:`Rocket.center_of_pressure_over_beta` and returns the extent of - the resulting center-of-pressure travel. + ``max_angle`` using the nonlinear :meth:`Rocket.center_of_pressure` and + returns the extent of the resulting center-of-pressure travel. """ + angles = np.linspace(0, max_angle, samples) if plane == "yz": - cp_travel = self.rocket.center_of_pressure_over_beta() + positions = np.array( + [self.rocket.center_of_pressure(0.0, b, 0.0) for b in angles] + ) else: - cp_travel = self.rocket.center_of_pressure_over_alpha() - angles = np.linspace(0, max_angle, samples) - positions = np.array([cp_travel.get_value_opt(a) for a in angles]) + positions = np.array( + [self.rocket.center_of_pressure(a, 0.0, 0.0) for a in angles] + ) positions = positions[np.isfinite(positions)] if len(positions) == 0: return (0.0, 0.0) @@ -956,13 +883,11 @@ def all(self): print("-" * 20) # Separator for Stability Plots self.static_margin() self.stability_margin() - self.stability_margin_over_alpha() # Non-axisymmetric rockets: the above describe the pitch plane only, so - # also show the yaw-plane margins (including the sideslip sweep). + # also show the yaw-plane margins. if not self.rocket.is_axisymmetric: self.static_margin_yaw() self.stability_margin_yaw() - self.stability_margin_over_beta() # Thrust-to-Weight Plot print("\nThrust-to-Weight Plot") diff --git a/rocketpy/prints/aero_surface_prints.py b/rocketpy/prints/aero_surface_prints.py index d85b6e243..12a2ba5c5 100644 --- a/rocketpy/prints/aero_surface_prints.py +++ b/rocketpy/prints/aero_surface_prints.py @@ -1,10 +1,16 @@ -from abc import ABC, abstractmethod - import numpy as np -# TODO: the rocketpy/prints/aero_surface_prints.py file could be separated into different, smaller files. -class _AeroSurfacePrints(ABC): +# The print classes mirror the aerodynamic-surface class hierarchy: +# GenericSurface -> _GenericSurfacePrints (root) +# LinearGenericSurface -> _LinearGenericSurfacePrints +# _BarrowmanSurface -> _BarrowmanSurfacePrints (adds clalpha/CP lift) +# NoseCone / Tail / Fins/Fin -> the leaf print classes below +# ControllableGenericSurface / AirBrakes / RailButtons -> generic-rooted leaves +# TODO: this file could be separated into different, smaller files. +class _GenericSurfacePrints: + """Base prints for a generic aerodynamic surface.""" + def __init__(self, aero_surface): self.aero_surface = aero_surface @@ -59,9 +65,33 @@ def identity(self): print(f"Name: {self.aero_surface.name}") print(f"Python Class: {str(self.aero_surface.__class__)}\n") - @abstractmethod def geometry(self): - pass + """Prints the reference geometry of the generic surface.""" + print("Geometric information of the Surface:") + print("----------------------------------") + print(f"Reference Area: {self.aero_surface.reference_area:.3f} m^2") + print(f"Reference length: {self.aero_surface.reference_length:.3f} m\n") + + def all(self): + """Prints all information of the generic surface. + + Returns + ------- + None + """ + self.identity() + self.geometry() + self.coefficients() + + +class _LinearGenericSurfacePrints(_GenericSurfacePrints): + """Prints for a linear generic surface; same reporting as the generic + base.""" + + +class _BarrowmanSurfacePrints(_LinearGenericSurfacePrints): + """Prints shared by the geometry-defined (Barrowman) surfaces: adds the + center-of-pressure / lift-curve-slope report on top of the generic base.""" def lift(self): """Prints the lift information of the aero surface. @@ -95,7 +125,7 @@ def all(self): self.coefficients() -class _NoseConePrints(_AeroSurfacePrints): +class _NoseConePrints(_BarrowmanSurfacePrints): """Class that contains all nosecone prints.""" def geometry(self): @@ -114,7 +144,7 @@ def geometry(self): print(f"Reference radius ratio: {self.aero_surface.radius_ratio:.3f}\n") -class _FinsPrints(_AeroSurfacePrints): +class _FinsPrints(_BarrowmanSurfacePrints): def geometry(self): print("Geometric information of the fin set:") print("-------------------------------------") @@ -212,7 +242,7 @@ def all(self): self.lift() -class _FinPrints(_AeroSurfacePrints): +class _FinPrints(_BarrowmanSurfacePrints): def geometry(self): print("Geometric information of the fin set:") print("-------------------------------------") @@ -333,7 +363,7 @@ class _FreeFormFinPrints(_FinPrints): """Class that contains all free form fins prints.""" -class _TailPrints(_AeroSurfacePrints): +class _TailPrints(_BarrowmanSurfacePrints): """Class that contains all tail prints.""" def geometry(self): @@ -353,7 +383,7 @@ def geometry(self): print(f"Surface area: {self.aero_surface.surface_area:.6f} m²\n") -class _RailButtonsPrints(_AeroSurfacePrints): +class _RailButtonsPrints(_GenericSurfacePrints): """Class that contains all rail buttons prints.""" def geometry(self): @@ -369,7 +399,7 @@ def geometry(self): ) -class _AirBrakesPrints(_AeroSurfacePrints): +class _AirBrakesPrints(_GenericSurfacePrints): """Class that contains all air_brakes prints. Not yet implemented.""" def geometry(self): @@ -377,45 +407,3 @@ def geometry(self): def all(self): pass - - -class _GenericSurfacePrints(_AeroSurfacePrints): - """Class that contains all generic surface prints.""" - - def geometry(self): - print("Geometric information of the Surface:") - print("----------------------------------") - print(f"Reference Area: {self.aero_surface.reference_area:.3f} m^2") - print(f"Reference length: {self.aero_surface.reference_length:.3f} m\n") - - def all(self): - """Prints all information of the generic surface. - - Returns - ------- - None - """ - self.identity() - self.geometry() - self.coefficients() - - -class _LinearGenericSurfacePrints(_AeroSurfacePrints): - """Class that contains all linear generic surface prints.""" - - def geometry(self): - print("Geometric information of the Surface:") - print("----------------------------------") - print(f"Reference Area: {self.aero_surface.reference_area:.3f} m^2") - print(f"Reference length: {self.aero_surface.reference_length:.3f} m\n") - - def all(self): - """Prints all information of the linear generic surface. - - Returns - ------- - None - """ - self.identity() - self.geometry() - self.coefficients() diff --git a/rocketpy/rocket/aero_surface/__init__.py b/rocketpy/rocket/aero_surface/__init__.py index 542435781..7a6e7ac2d 100644 --- a/rocketpy/rocket/aero_surface/__init__.py +++ b/rocketpy/rocket/aero_surface/__init__.py @@ -1,5 +1,8 @@ from rocketpy.rocket.aero_surface.aero_surface import AeroSurface from rocketpy.rocket.aero_surface.air_brakes import AirBrakes +from rocketpy.rocket.aero_surface.controllable_generic_surface import ( + ControllableGenericSurface, +) from rocketpy.rocket.aero_surface.fins import ( EllipticalFin, EllipticalFins, @@ -10,9 +13,6 @@ TrapezoidalFin, TrapezoidalFins, ) -from rocketpy.rocket.aero_surface.controllable_generic_surface import ( - ControllableGenericSurface, -) from rocketpy.rocket.aero_surface.generic_surface import GenericSurface from rocketpy.rocket.aero_surface.linear_generic_surface import LinearGenericSurface from rocketpy.rocket.aero_surface.nose_cone import NoseCone diff --git a/rocketpy/rocket/aero_surface/_barrowman_surface.py b/rocketpy/rocket/aero_surface/_barrowman_surface.py index 9a9843193..3ae96024b 100644 --- a/rocketpy/rocket/aero_surface/_barrowman_surface.py +++ b/rocketpy/rocket/aero_surface/_barrowman_surface.py @@ -27,6 +27,11 @@ class _BarrowmanSurface(LinearGenericSurface): ``self.roll_parameters = [clf_delta, cld_omega, cant_angle_rad]``. """ + # Geometry-defined Barrowman surfaces are axisymmetric by construction + # (``cQ_beta = -cL_alpha``, etc.), so they contribute identically to the + # pitch and yaw planes. The individual ``Fin`` overrides this back to False. + is_axisymmetric = True + @staticmethod def _beta(mach): """Prandtl-Glauert compressibility factor used to correct subsonic @@ -115,6 +120,7 @@ def _mach_coefficient(self, func_of_mach, name="coefficient"): return AeroCoefficient( func_of_mach, depends_on=("mach",), - independent_vars=self.independent_vars, + unsteady_aero=self._unsteady_aero, + control_variables=self.control_variables, name=name, ) diff --git a/rocketpy/rocket/aero_surface/aero_coefficient.py b/rocketpy/rocket/aero_surface/aero_coefficient.py index e65aa3a9a..fa0b35012 100644 --- a/rocketpy/rocket/aero_surface/aero_coefficient.py +++ b/rocketpy/rocket/aero_surface/aero_coefficient.py @@ -13,35 +13,167 @@ selection that the CSV loader used to do inline. """ +import copy +import csv import inspect from rocketpy.mathutils import Function +# Single source of truth for the seven base coefficient independent variables. +BASE_INDEPENDENT_VARS = [ + "alpha", + "beta", + "mach", + "reynolds", + "pitch_rate", + "yaw_rate", + "roll_rate", +] + + +def build_independent_vars(unsteady_aero=False, control_variables=()): + """Build the ordered independent-variable list of a coefficient/surface. + + The seven base axes (``BASE_INDEPENDENT_VARS``), plus ``alpha_dot`` and + ``beta_dot`` when ``unsteady_aero`` is enabled (axes the flight integrator + supplies automatically), plus any ``control_variables`` (axes supplied + externally, e.g. by a controller). Shared by :class:`AeroCoefficient` and + :class:`GenericSurface` so the ordering is defined in exactly one place. + """ + names = list(BASE_INDEPENDENT_VARS) + if unsteady_aero: + names += ["alpha_dot", "beta_dot"] + names += list(control_variables) + return names + class AeroCoefficient: """A single aerodynamic coefficient stored at minimal dimensionality. - Parameters - ---------- - source : int, float, callable, or Function - The coefficient value. A number is stored as a constant; a callable or - :class:`Function` is stored over ``depends_on``. - depends_on : sequence of str - The independent variables the coefficient depends on, a (possibly - empty) subset of ``independent_vars``. The order is normalized to the - order of ``independent_vars``. - independent_vars : sequence of str - The full, ordered list of independent variables of the owning surface - (e.g. ``alpha, beta, mach, reynolds, pitch_rate, yaw_rate, roll_rate`` - plus any control or unsteady axes). Defines the argument order accepted - by :meth:`__call__`. - name : str, optional - Name of the coefficient, used for the underlying ``Function`` output. + Building goes through :meth:`__init__`: pass a raw coefficient input + (number, callable, :class:`Function`, list/tuple of points, CSV path, or + another :class:`AeroCoefficient`) and ``depends_on`` is inferred; pass + ``depends_on`` explicitly only on the fast path where it is already known. """ - def __init__(self, source, depends_on, independent_vars, name="coefficient"): + def __init__( + self, + source, + depends_on=None, + unsteady_aero=False, + control_variables=(), + name="coefficient", + extrapolation=None, + single_var=None, + ): + """Build a coefficient stored at minimal dimensionality. + + A number is kept as a plain constant. Anything else is wrapped in a + :class:`Function` over only the variables it depends on (``depends_on``), + so a Mach-only curve stays 1-D instead of being stretched across all + seven axes. On each call the full argument tuple is mapped down to just + those arguments (using the precomputed ``_indices``). The full, ordered + list of variables comes from ``unsteady_aero`` and ``control_variables`` + via :func:`build_independent_vars`. + + Usually you do not pass ``depends_on``: leave it as ``None`` and it is + worked out from ``source`` (a number, a callable, a :class:`Function`, a + list of points, a CSV path, or another :class:`AeroCoefficient`), the + same inputs :class:`GenericSurface` accepts (see :meth:`_resolve_input`). + Pass ``depends_on`` yourself only on the fast path, where the source and + its argument order are already known (the Barrowman surfaces and + serialization). + + Parameters + ---------- + source : number, str, list, tuple, callable, Function, or AeroCoefficient + The coefficient value, or an input it can be worked out from when + ``depends_on`` is ``None``. The accepted forms are: + + - **number**: kept as a constant. Calls return it directly, and + ``is_zero`` is set when it is exactly ``0.0`` (the linear model + uses that to skip the term). It depends on nothing. + - **callable** (function or ``lambda``): wrapped in a + :class:`Function`. When ``depends_on`` is worked out, the + parameter *names* decide it: name them after the variables they + use (e.g. ``lambda alpha, mach: ...``), give one argument per + variable, or use one argument together with ``single_var``. + - **Function**: used as given. If ``extrapolation`` is set, it is + applied to a copy, never to the object you passed in (it may be + shared elsewhere). + - **list/tuple of points**: turned into a :class:`Function` with + linear interpolation, so a list and the same data in a CSV give + the same result. + - **str**: a path to a data file. A ``.csv`` file is read by the CSV + loader (column headers name the variables; a headerless + two-column file is a 1-D table over ``single_var``); other files + are read by :class:`Function`. + - **AeroCoefficient**: an existing coefficient, re-keyed to this + surface's variables. This is what lets a surface round-trip + through ``to_dict``/``from_dict`` and lets one coefficient be + reused on several surfaces. + depends_on : sequence of str, optional + The variables this coefficient actually uses, a (possibly empty) + subset of the surface's full variable list (set by ``unsteady_aero`` + and ``control_variables``). Keep them in the same order as the + source's own arguments (a callable's parameters, a CSV's columns): + that order is used to pick the right values out of the full argument + tuple on each call. For example, ``()`` for a constant, ``("mach",)`` + for a Mach-only curve, or the whole list for something that uses + every variable. A name that is not one of the surface's variables + raises a ``ValueError``. Leave it as ``None`` (the default) to have + it worked out from ``source``; pass it only on the fast path, where + the source and its argument order are already known. + unsteady_aero : bool, optional + Add the unsteady axes to this coefficient's variables. When ``True``, + ``alpha_dot`` and ``beta_dot`` (the rates of change of the angle of + attack and sideslip) are added after the seven base axes, so calls + take two more arguments. The flight integrator fills these in, using + ``0`` when it does not compute them, so ordinary tables keep working. + Match the owning surface's setting. Default ``False``. + control_variables : sequence of str, optional + Names of extra axes supplied from outside, such as control-surface + deflections from a controller. They are added after the base and + unsteady axes, and each one becomes an extra call argument, in the + order given. Used by :class:`ControllableGenericSurface` and air + brakes; empty for ordinary surfaces. Default ``()``. + name : str, optional + A readable name for the coefficient (e.g. ``"cL_alpha"`` or + ``"Drag Coefficient with Power Off"``). It labels the underlying + :class:`Function` and appears in error messages, so a clear name + makes problems easier to spot. Default ``"coefficient"``. + extrapolation : str, optional + How the stored :class:`Function` behaves outside its data range, one + of the options of :meth:`Function.set_extrapolation`: ``"constant"`` + holds the edge value (used for drag, which should not run past its + data), ``"natural"`` keeps following the curve, ``"zero"`` returns + ``0``. ``None`` (the default) leaves a :class:`Function` you passed + in unchanged, and uses ``"natural"`` for one built from a callable. + An override is always applied to a copy, so your object is never + changed. + single_var : str, optional + Which variable a 1-D input maps to. Used only while working out + ``depends_on`` for a single-dimension source: a headerless + two-column CSV, a 1-D :class:`Function`, or a one-argument callable. + ``None`` (the default) guesses it from the input's label, falling + back to the first variable; drag passes ``"mach"`` so a plain + Cd-vs-Mach curve maps to Mach. Ignored when ``depends_on`` is given. + Default ``None``. + """ self.name = name - self.independent_vars = tuple(independent_vars) + self.extrapolation = extrapolation + self.unsteady_aero = unsteady_aero + self.control_variables = tuple(control_variables) + self.independent_vars = tuple( + build_independent_vars(unsteady_aero, control_variables) + ) + # Infer the stored source and its dependencies from the raw input when + # ``depends_on`` is not given. ``_resolve_input`` may also adopt the + # input's extrapolation (re-keying an AeroCoefficient), so refresh the + # local ``extrapolation`` used by the source-storage block below. + if depends_on is None: + source, depends_on = self._resolve_input(source, single_var) + extrapolation = self.extrapolation # ``depends_on`` is kept in the given order because it matches the # positional argument order of the stored source (callable parameters, # CSV columns, …). ``_indices`` therefore maps the full argument tuple @@ -60,6 +192,13 @@ def __init__(self, source, depends_on, independent_vars, name="coefficient"): self.is_zero = False self._constant = None if isinstance(source, Function): + # Only override extrapolation when explicitly asked, and on a copy: + # the source may be a user-owned Function reused elsewhere, so + # mutating it in place (e.g. drag forcing "constant") would change + # its behavior everywhere the caller reuses it. + if extrapolation is not None: + source = copy.deepcopy(source) + source.set_extrapolation(extrapolation) self.function = source elif callable(source): self.function = Function( @@ -67,7 +206,7 @@ def __init__(self, source, depends_on, independent_vars, name="coefficient"): list(self.depends_on) or ["x"], [name], interpolation="linear", - extrapolation="natural", + extrapolation=extrapolation or "natural", ) else: # Scalar constant. @@ -77,83 +216,201 @@ def __init__(self, source, depends_on, independent_vars, name="coefficient"): self._evaluate = self.function.get_value_opt - @classmethod - def from_input(cls, input_data, name, independent_vars, csv_loader=None): - """Build an :class:`AeroCoefficient` from a user coefficient input. + def _resolve_input(self, source, single_var): + """Infer ``(stored source, depends_on)`` from a raw coefficient input. + + Mirrors the coefficient inputs accepted by :class:`GenericSurface`: a + number, a callable, a :class:`Function`, a list/tuple of data points, a + path to a CSV (or other text) file, or another :class:`AeroCoefficient` + (re-keyed). + Called by :meth:`__init__` when ``depends_on`` is omitted; the returned + ``source`` is a number, a callable or a :class:`Function`, which the + constructor's source-storage block then stores. + """ + name = self.name + independent_vars = self.independent_vars + n_vars = len(independent_vars) + + if isinstance(source, AeroCoefficient): + # An already-built coefficient passed straight through, re-keyed to + # this surface's variable order. This is how a *surface* round-trips: + # GenericSurface/ControllableGenericSurface store their processed + # AeroCoefficients in ``to_dict`` and feed them back on ``from_dict`` + # (and a user may reuse one coefficient across surfaces). Adopt its + # extrapolation when none was requested. + if self.extrapolation is None: + self.extrapolation = source.extrapolation + value = ( + source._constant if source._constant is not None else source.function + ) + return value, source.depends_on - Mirrors the accepted coefficient inputs of - :class:`GenericSurface`: a number, a callable, a :class:`Function`, or a - path to a CSV file, inferring ``depends_on`` from each. + if isinstance(source, str): + if source.lower().endswith(".csv"): + return self._load_csv( + source, + name, + independent_vars, + extrapolation=self.extrapolation or "natural", + single_var=single_var, + ) + # Any other path (e.g. a whitespace-delimited ``.txt`` curve) is read + # by Function, which auto-detects the delimiter. Linear interpolation + # matches the CSV loader, so the same data gives identical results + # whatever file form it is given. Falls through to the Function + # branch below (a 1-D table keyed to ``single_var``). + source = Function(source, interpolation="linear") + + # A list/tuple of data points is parsed by Function and handled below. + # Linear interpolation matches the CSV loader, so the same tabular data + # gives identical results whether supplied as a list or a CSV file + # (Function would otherwise default to spline). + if isinstance(source, (list, tuple)): + try: + source = Function(list(source), interpolation="linear") + except (TypeError, ValueError) as exc: + raise TypeError( + f"Invalid list/tuple input for {name}: could not be parsed " + "into a Function of the independent variables." + ) from exc + + if isinstance(source, Function): + dom_dim = source.__dom_dim__ + if dom_dim == n_vars: + return source, list(independent_vars) + if dom_dim == 1: + # A 1-D Function depends on ``single_var`` when given, else on + # the first independent variable unless its input name matches. + return source, [ + single_var or self._infer_single_var(source, independent_vars) + ] + raise ValueError( + f"{name} Function must have {n_vars} input arguments " + f"({', '.join(independent_vars)}) or be one-dimensional." + ) + + if callable(source): + return source, self._infer_callable_depends_on( + source, independent_vars, name, single_var=single_var + ) + + # Anything else must be a scalar number. + try: + float(source) + except (TypeError, ValueError) as exc: + raise TypeError( + f"Invalid input for {name}: must be a number, a CSV file path, " + "a list of data points, a callable, or a Function." + ) from exc + return source, () + + @staticmethod + def _load_csv( + file_path, name, independent_vars, extrapolation="natural", single_var=None + ): # pylint: disable=too-many-statements + """Load a coefficient CSV at minimal dimension. + + Expects header-based CSV data whose columns (except the last) are + independent variables among ``independent_vars``; the last column is the + coefficient value. The coefficient is stored over only the columns that + are present, in their header order. A headerless two-column file is + treated as a one-dimensional table over ``single_var``. Parameters ---------- - input_data : int, float, str, callable, or Function - The coefficient value (number, CSV path, callable, or Function). + file_path : str + Path to the CSV file. name : str Coefficient name, used for error messages and the Function output. independent_vars : sequence of str - The owning surface's ordered independent variables. - csv_loader : callable, optional - Callable ``(file_path, name) -> (function, depends_on)`` used to - load a CSV coefficient at minimal dimension. Required when - ``input_data`` is a string path. + The owning surface's ordered independent variables, used to validate + the CSV header columns. + extrapolation : str, optional + Extrapolation method for the loaded ``Function``. Defaults to + ``"natural"``; drag coefficients pass ``"constant"``. + single_var : str, optional + Independent variable a headerless two-column table depends on. + Defaults to the first independent variable. Returns ------- - AeroCoefficient + tuple + ``(function, depends_on)`` where ``function`` is a low-dimensional + ``Function`` over the present columns and ``depends_on`` lists those + columns. Consumed by :meth:`_resolve_input`. """ independent_vars = list(independent_vars) - n_vars = len(independent_vars) - vars_repr = ", ".join(independent_vars) - - if isinstance(input_data, AeroCoefficient): - # Already an AeroCoefficient (e.g. a to_dict/from_dict round trip): - # re-key it to the requested independent-variable order. - return cls( - input_data._constant - if input_data._constant is not None - else input_data.function, - input_data.depends_on, - independent_vars, - name, + + try: + with open(file_path, mode="r") as file: + reader = csv.reader(file) + header = next(reader) + except (FileNotFoundError, IOError) as e: + raise ValueError(f"Error reading {name} CSV file: {e}") from e + except StopIteration as e: + raise ValueError(f"Invalid or empty CSV file for {name}.") from e + + if not header: + raise ValueError(f"Invalid or empty CSV file for {name}.") + + header = [column.strip() for column in header] + + # Headerless two-column (x, coefficient) table: a 1-D table over + # ``single_var`` (e.g. a Mach-only drag curve given as ``mach, cd``). + def _is_numeric(value): + try: + float(value) + return True + except (TypeError, ValueError): + return False + + if len(header) == 2 and all(_is_numeric(cell) for cell in header): + csv_func = Function( + file_path, + interpolation="linear", + extrapolation=extrapolation, ) + return csv_func, [single_var or independent_vars[0]] - if isinstance(input_data, str): - if csv_loader is None: # pragma: no cover - defensive - raise ValueError("A csv_loader is required for CSV coefficients.") - function, depends_on = csv_loader(input_data, name) - return cls(function, depends_on, independent_vars, name) + present_columns = [col for col in independent_vars if col in header] - if isinstance(input_data, Function): - dom_dim = input_data.__dom_dim__ - if dom_dim == n_vars: - depends_on = independent_vars - elif dom_dim == 1: - # A 1-D Function is taken to depend on the first independent - # variable (alpha) unless its input name matches one of them. - depends_on = [cls._infer_single_var(input_data, independent_vars)] - else: - raise ValueError( - f"{name} Function must have {n_vars} input arguments " - f"({vars_repr}) or be one-dimensional." - ) - return cls(input_data, depends_on, independent_vars, name) + invalid_columns = [col for col in header[:-1] if col not in independent_vars] + if invalid_columns: + raise ValueError( + f"Invalid independent variable(s) in {name} CSV: " + f"{invalid_columns}. Valid options are: {independent_vars}." + ) - if callable(input_data): - depends_on = cls._infer_callable_depends_on( - input_data, independent_vars, name + if header[-1] in independent_vars: + raise ValueError( + f"Last column in {name} CSV must be the coefficient" + " value, not an independent variable." ) - return cls(input_data, depends_on, independent_vars, name) - # Anything else must be a scalar number. - try: - float(input_data) - except (TypeError, ValueError) as exc: - raise TypeError( - f"Invalid input for {name}: must be a number, a CSV file path, " - "a callable, or a Function." - ) from exc - return cls(input_data, (), independent_vars, name) + if not present_columns: + raise ValueError(f"No independent variables found in {name} CSV.") + + ordered_present_columns = [ + col for col in header[:-1] if col in independent_vars + ] + + csv_func = Function.from_regular_grid_csv( + file_path, + ordered_present_columns, + name, + extrapolation=extrapolation, + ) + if csv_func is None: + csv_func = Function( + file_path, + interpolation="linear", + extrapolation=extrapolation, + ) + + # The CSV columns may appear in any order; AeroCoefficient maps the full + # argument tuple to ``ordered_present_columns`` order, so the stored + # Function is queried directly at its own (minimal) dimensionality. + return csv_func, ordered_present_columns @staticmethod def _infer_single_var(function, independent_vars): @@ -163,17 +420,26 @@ def _infer_single_var(function, independent_vars): except (AttributeError, IndexError, TypeError): return independent_vars[0] label_lower = str(label).lower() + # Exact match first; then substring, longest variable name first, so a + # label like "alpha_dot" binds to "alpha_dot" rather than the shorter + # substring "alpha". for var in independent_vars: + if var == label_lower: + return var + for var in sorted(independent_vars, key=len, reverse=True): if var in label_lower: return var return independent_vars[0] @staticmethod - def _infer_callable_depends_on(func, independent_vars, name): + def _infer_callable_depends_on(func, independent_vars, name, single_var=None): """Infer ``depends_on`` for a plain callable. - Two conventions are accepted, checked in order: + Conventions are accepted in order: + 0. *Single variable* - when ``single_var`` is given and the callable + takes a single argument, it depends on that one variable regardless + of the parameter name (e.g. a Mach-only drag ``lambda mach: ...``). 1. *Named subset* - every parameter name is an independent variable, so the parameters themselves name the dependency subset (e.g. ``lambda alpha, mach: ...``). @@ -189,6 +455,8 @@ def _infer_callable_depends_on(func, independent_vars, name): params = [] names = [p.name for p in params] + if single_var and len(names) == 1: + return [single_var] if names and set(names) <= set(independent_vars): return names if len(names) == n_vars: @@ -223,7 +491,50 @@ def get_value_opt(self, *args): # model grabs ``get_value_opt`` directly for the hot loop. __call__ = get_value_opt + def __mul__(self, other): + """Scale the coefficient by ``other``, returning a new AeroCoefficient. + + Used by the Monte Carlo drag factor (``coefficient *= factor``). The + underlying constant or :class:`Function` is scaled while ``depends_on``, + the independent-variable axes and ``extrapolation`` are preserved. + """ + source = self._constant if self._constant is not None else self.function + return AeroCoefficient( + source * other, + self.depends_on, + self.unsteady_aero, + self.control_variables, + self.name, + extrapolation=self.extrapolation, + ) + + __rmul__ = __mul__ + + def to_dict(self, **kwargs): # pylint: disable=unused-argument + """Serialize the coefficient for :class:`rocketpy._encoders.RocketPyEncoder`.""" + return { + "source": self._constant if self._constant is not None else self.function, + "depends_on": list(self.depends_on), + "unsteady_aero": self.unsteady_aero, + "control_variables": list(self.control_variables), + "name": self.name, + "extrapolation": self.extrapolation, + } + + @classmethod + def from_dict(cls, data): + """Rebuild an :class:`AeroCoefficient` from its :meth:`to_dict` form.""" + return cls( + data["source"], + data["depends_on"], + data.get("unsteady_aero", False), + data.get("control_variables", ()), + data["name"], + extrapolation=data.get("extrapolation"), + ) + def __repr__(self): + """Return a concise representation showing the constant or dependencies.""" if self._constant is not None: return f"AeroCoefficient({self.name}={self._constant})" return f"AeroCoefficient({self.name}, depends_on={self.depends_on})" diff --git a/rocketpy/rocket/aero_surface/air_brakes.py b/rocketpy/rocket/aero_surface/air_brakes.py index 3c4958255..221440dc6 100644 --- a/rocketpy/rocket/aero_surface/air_brakes.py +++ b/rocketpy/rocket/aero_surface/air_brakes.py @@ -120,7 +120,14 @@ def __init__( # ``deployment_level`` control axis. The deployment-0 ⇒ Cd 0 rule applies # only when the air brakes add to (rather than override) the rocket drag. def drag_coefficient_function( - alpha, beta, mach, reynolds, pitch_rate, yaw_rate, roll_rate, deployment_level + alpha, + beta, + mach, + reynolds, + pitch_rate, + yaw_rate, + roll_rate, + deployment_level, ): # pylint: disable=unused-argument if deployment_level == 0 and not self.override_rocket_drag: return 0.0 diff --git a/rocketpy/rocket/aero_surface/controllable_generic_surface.py b/rocketpy/rocket/aero_surface/controllable_generic_surface.py index 42e473eb9..049ba6206 100644 --- a/rocketpy/rocket/aero_surface/controllable_generic_surface.py +++ b/rocketpy/rocket/aero_surface/controllable_generic_surface.py @@ -1,9 +1,4 @@ -from rocketpy.plots.aero_surface_plots import _GenericSurfacePlots -from rocketpy.prints.aero_surface_prints import _GenericSurfacePrints -from rocketpy.rocket.aero_surface.generic_surface import ( - BASE_INDEPENDENT_VARS, - GenericSurface, -) +from rocketpy.rocket.aero_surface.generic_surface import GenericSurface class ControllableGenericSurface(GenericSurface): @@ -61,9 +56,9 @@ def __init__( """ # These must be set before ``super().__init__`` so coefficient # processing (arity, CSV validation) and the derived-cp accessors see - # the extended variable list and the current control values. + # the extended variable list (via the ``independent_vars`` property, + # which appends ``control_variables``) and the current control values. self.control_variables = list(controls) - self.independent_vars = BASE_INDEPENDENT_VARS + self.control_variables self.control_state = {name: 0.0 for name in self.control_variables} super().__init__( @@ -73,9 +68,7 @@ def __init__( center_of_pressure=center_of_pressure, name=name, ) - - self.prints = _GenericSurfacePrints(self) - self.plots = _GenericSurfacePlots(self) + # ``self.prints``/``self.plots`` are the generic ones wired by the base. def _coefficient_arguments( self, diff --git a/rocketpy/rocket/aero_surface/fins/fin.py b/rocketpy/rocket/aero_surface/fins/fin.py index ac49d92e7..b2875e658 100644 --- a/rocketpy/rocket/aero_surface/fins/fin.py +++ b/rocketpy/rocket/aero_surface/fins/fin.py @@ -91,6 +91,12 @@ class Fin(_BaseFin): damping coefficient and the cant angle in radians. """ + # A single fin contributes unequally to the pitch and yaw planes + # (``cL_alpha`` ~ sin^2(phi), ``cQ_beta`` ~ cos^2(phi)), so it is not + # axisymmetric on its own. A complete, evenly spaced set may still be + # axisymmetric collectively, which the rocket's numeric check resolves. + is_axisymmetric = False + def __init__( self, angular_position, @@ -296,7 +302,12 @@ def evaluate_rotation_matrix(self): sin_delta = math.sin(delta) cos_delta = math.cos(delta) - # Rotation about body Z by angular position + # The body -> fin change of basis is composed right-to-left as + # ``R_delta @ R_phi @ R_pi`` (R_pi first, R_delta last). Each factor + # therefore acts on the coordinates produced by the factors to its right, + # i.e. in the *current* (partially rotated) frame, not the body frame. + + # Roll by the angular position, about the rocket longitudinal axis. R_phi = Matrix( [ [cos_phi, -sin_phi, 0], @@ -305,7 +316,12 @@ def evaluate_rotation_matrix(self): ] ) - # Cant rotation about body Y + # Cant rotation about the fin **span (y) axis**. Because R_delta is the + # leftmost factor, it acts on coordinates already in the rolled + # uncanted-fin frame, so it rotates about that frame's y axis (the fin's + # own root-to-tip direction) -- NOT body Y, with which it coincides only + # at angular_position = 0. This is what makes each fin cant about its own + # span; using body Y (``R_uncanted @ R_delta``) would be wrong. R_delta = Matrix( [ [cos_delta, 0, -sin_delta], @@ -314,7 +330,9 @@ def evaluate_rotation_matrix(self): ] ) - # 180 flip about Y to align fin leading/trailing edge + # 180 flip about Y so the uncanted fin z axis points leading -> trailing + # edge (toward the tail, i.e. -body z), with x completing a right-handed + # frame. Proper rotation (det +1), not a reflection. R_pi = Matrix( [ [-1, 0, 0], @@ -323,7 +341,7 @@ def evaluate_rotation_matrix(self): ] ) - # Uncanted body to fin, then apply cant + # Uncanted body -> fin, then apply the cant in the fin span frame. R_uncanted = R_phi @ R_pi R_body_to_fin = R_delta @ R_uncanted diff --git a/rocketpy/rocket/aero_surface/generic_surface.py b/rocketpy/rocket/aero_surface/generic_surface.py index 90c0a3537..d9f6fe82a 100644 --- a/rocketpy/rocket/aero_surface/generic_surface.py +++ b/rocketpy/rocket/aero_surface/generic_surface.py @@ -1,25 +1,16 @@ import copy -import csv import math import numpy as np from rocketpy.mathutils import Function from rocketpy.mathutils.vector_matrix import Matrix, Vector -from rocketpy.rocket.aero_surface.aero_coefficient import AeroCoefficient - -# Single source of truth for the coefficient independent variables. Subclasses -# (e.g. ControllableGenericSurface, or the alpha_dot/beta_dot extension) append -# extra axes to this base via ``self.independent_vars``. -BASE_INDEPENDENT_VARS = [ - "alpha", - "beta", - "mach", - "reynolds", - "pitch_rate", - "yaw_rate", - "roll_rate", -] +from rocketpy.plots.aero_surface_plots import _GenericSurfacePlots +from rocketpy.prints.aero_surface_prints import _GenericSurfacePrints +from rocketpy.rocket.aero_surface.aero_coefficient import ( + AeroCoefficient, + build_independent_vars, +) class GenericSurface: @@ -28,6 +19,14 @@ class GenericSurface: attack, sideslip angle, Mach number, Reynolds number, pitch rate, yaw rate and roll rate.""" + #: Whether this surface contributes identically to the pitch and yaw planes + #: *by construction*. Conservatively ``False`` for a generic surface (its + #: coefficients may differ between planes); the built-in axisymmetric + #: surfaces override it to ``True``. The rocket uses it to skip the numeric + #: pitch/yaw axisymmetry check when every surface is symmetric by + #: construction. + is_axisymmetric = False + def __init__( self, reference_area, @@ -50,6 +49,13 @@ def __init__( "reynolds", "pitch_rate", "yaw_rate" and "roll_rate". The independent variable columns can be provided in any order. + The angular-rate inputs ("pitch_rate", "yaw_rate", "roll_rate") are the + conventional **non-dimensional reduced rates**, ``q* = q * L_ref / (2 * V)`` + (and likewise for ``r``/``p``), matching how published and tool-generated + aerotables (Missile DATCOM, OpenVSP, CFD/wind-tunnel data) tabulate rate + derivatives. Provide coefficient tables against the reduced rates, not the + raw body rates in rad/s. + See Also -------- :ref:`genericsurfaces`. @@ -99,16 +105,24 @@ def __init__( unaffected. Default is False. """ - # Independent variables the coefficients depend on. Subclasses may set - # this (with extra axes appended) before calling ``super().__init__``. - # When ``unsteady_aero`` is enabled, the time-derivatives of the flow - # angles (``alpha_dot``, ``beta_dot``) are appended as extra axes - # (defaulting to 0 at runtime, so existing tables are unaffected). + # The independent variables of the coefficients are derived (see the + # ``independent_vars`` property) from ``unsteady_aero`` and, for + # subclasses, ``control_variables``. When ``unsteady_aero`` is enabled, + # the time-derivatives of the flow angles (``alpha_dot``, ``beta_dot``) + # become extra axes (defaulting to 0 at runtime, so existing tables are + # unaffected). Subclasses that add externally-supplied axes set + # ``control_variables`` before calling ``super().__init__``. self._unsteady_aero = unsteady_aero - if not hasattr(self, "independent_vars"): - self.independent_vars = list(BASE_INDEPENDENT_VARS) - if unsteady_aero: - self.independent_vars += ["alpha_dot", "beta_dot"] + # Externally-supplied axes (e.g. control deflections). Subclasses set + # this before ``super().__init__``; defaults to none for plain surfaces. + self.control_variables = getattr(self, "control_variables", ()) + # Ordered independent variables accepted by every coefficient: the seven + # base axes, plus ``alpha_dot``/``beta_dot`` when ``unsteady_aero`` is + # enabled (integrator-supplied), plus any ``control_variables`` a + # subclass appended (externally supplied). Fixed at construction. + self.independent_vars = build_independent_vars( + self._unsteady_aero, self.control_variables + ) self.reference_area = reference_area self.reference_length = reference_length @@ -125,12 +139,22 @@ def __init__( self._check_coefficients(coefficients, default_coefficients) coefficients = self._complete_coefficients(coefficients, default_coefficients) for coeff, coeff_value in coefficients.items(): - value = self._process_input(coeff_value, coeff) + value = AeroCoefficient( + coeff_value, + unsteady_aero=self._unsteady_aero, + control_variables=self.control_variables, + name=coeff, + ) setattr(self, coeff, value) self.evaluate_coefficients() self._evaluate_derived_coefficients() + # Reporting layers. Subclasses override these with their own (more + # specific) prints/plots after calling ``super().__init__``. + self.prints = _GenericSurfacePrints(self) + self.plots = _GenericSurfacePlots(self) + @property def force_application_point(self): """Local point (surface frame) at which the resultant force is applied @@ -142,6 +166,27 @@ def force_application_point(self): """ return Vector([self.cpx, self.cpy, self.cpz]) + def info(self): + """Prints a summary of the surface's geometry and aerodynamic + coefficients. Subclasses override this with surface-specific summaries. + + Returns + ------- + None + """ + self.prints.geometry() + self.prints.coefficients() + + def all_info(self): + """Prints and plots all available information of the surface. + + Returns + ------- + None + """ + self.prints.all() + self.plots.all() + def evaluate_coefficients(self): """Hook for subclasses to (re)populate the aerodynamic coefficient ``Function``s from their geometry. The base class builds coefficients @@ -202,10 +247,16 @@ def _set_derived_cp_accessors(self, cL_alpha, cm_alpha, cQ_beta, cn_beta): local_cpz = self.force_application_point[2] def _cp_z(force_slope, moment_slope): + # Recover the center of pressure from a force slope and its matching + # moment slope, as a Function of Mach. def cp_z(mach): slope = force_slope.get_value_opt(mach) + # No force at this Mach -> the cp is undefined; fall back to the + # geometric application point so this surface contributes zero + # weight to the force-weighted cp average. if slope == 0: return local_cpz + # cp = application point - (moment slope / force slope) * L_ref. return ( local_cpz - moment_slope.get_value_opt(mach) / slope * reference_length @@ -217,9 +268,9 @@ def cp_z(mach): self.lift_coefficient_derivative = cL_alpha self.center_of_pressure_z = _cp_z(cL_alpha, cm_alpha) - # Yaw plane. The side-force slope is sign-adjusted (``-cQ_beta``) so that - # an axisymmetric surface yields the same signed weight as the pitch - # plane, making the two planes' margins coincide when symmetric. + # Yaw plane. The side-force slope is sign-adjusted (``-cQ_beta``) so + # that an axisymmetric surface yields the same signed weight as the + # pitch plane, making the two planes' margins coincide when symmetric. self.side_coefficient_derivative = -cQ_beta self.center_of_pressure_z_yaw = _cp_z(cQ_beta, cn_beta) @@ -366,11 +417,11 @@ def _compute_from_coefficients( reynolds : float Reynolds number. pitch_rate : float - Pitch rate in radians per second. + Non-dimensional (reduced) pitch rate, ``q * L_ref / (2 * V)``. yaw_rate : float - Yaw rate in radians per second. + Non-dimensional (reduced) yaw rate, ``r * L_ref / (2 * V)``. roll_rate : float - Roll rate in radians per second. + Non-dimensional (reduced) roll rate, ``p * L_ref / (2 * V)``. Returns ------- @@ -497,6 +548,17 @@ def compute_forces_and_moments( alpha = np.arctan2(stream_velocity[1], stream_velocity[2]) beta = np.arctan2(stream_velocity[0], stream_velocity[2]) + # Non-dimensionalize the body angular rates into the conventional reduced + # rates (e.g. ``q* = q * L_ref / (2 * V)``) before evaluating the + # coefficients, so coefficient tables follow the standard aerotable + # convention (Missile DATCOM, OpenVSP, CFD/wind-tunnel data tabulate rate + # derivatives against the reduced rates). The factor is 0 at zero airspeed + # (pad/static) to avoid division by zero; there is no aerodynamic damping + # there anyway. + reduced_rate_factor = ( + self.reference_length / (2 * stream_speed) if stream_speed > 0 else 0.0 + ) + # Compute aerodynamic forces and moments lift, side, drag, pitch, yaw, roll = self._compute_from_coefficients( rho, @@ -505,9 +567,9 @@ def compute_forces_and_moments( beta, stream_mach, reynolds, - omega[0], # q - omega[1], # r - omega[2], # p + omega[0] * reduced_rate_factor, # q* reduced pitch rate + omega[1] * reduced_rate_factor, # r* reduced yaw rate + omega[2] * reduced_rate_factor, # p* reduced roll rate alpha_dot, beta_dot, ) @@ -515,11 +577,7 @@ def compute_forces_and_moments( # Conversion from the aerodynamic frame to the body frame. This is the # direction cosine matrix (DCM) that expresses the aerodynamic-frame # force components in the body frame, i.e. rotations by ``-alpha`` about - # x and ``+beta`` about y. Using the opposite-sign "vector rotation" - # matrices is incorrect: it leaves the result effectively in the - # aerodynamic frame, flipping the transverse components of any force that - # has a drag part (see RocketPy issue #932). Surfaces with no drag (the - # Barrowman lift/side surfaces) differ only in the small axial term. + # x and ``+beta`` about y. rotation_matrix = Matrix( [ [1, 0, 0], @@ -539,100 +597,3 @@ def compute_forces_and_moments( M1, M2, M3 = Vector([pitch, yaw, roll]) + (cp ^ Vector([R1, R2, R3])) return R1, R2, R3, M1, M2, M3 - - def _process_input(self, input_data, coeff_name): - """Process a coefficient input into an :class:`AeroCoefficient`. - - Accepts a number, a callable, a :class:`Function`, or a path to a CSV - file, storing the coefficient at its intrinsic dimensionality (its - ``depends_on``) rather than forcing it into a full - ``len(self.independent_vars)``-D ``Function``. See - :class:`AeroCoefficient`. - - Parameters - ---------- - input_data : int, float, str, callable, or Function - Input data to be processed. - coeff_name : str - Name of the coefficient being processed for error reporting. - - Returns - ------- - AeroCoefficient - Callable over the full ``self.independent_vars`` argument tuple. - """ - return AeroCoefficient.from_input( - input_data, - coeff_name, - self.independent_vars, - csv_loader=self.__load_generic_surface_csv, - ) - - def __load_generic_surface_csv(self, file_path, coeff_name): # pylint: disable=too-many-statements,import-outside-toplevel - """Load a GenericSurface coefficient CSV at minimal dimension. - - This loader expects header-based CSV data with one or more independent - variables among ``self.independent_vars`` (the seven base variables, - plus any extra axes added by subclasses such as control deflections). - - Returns - ------- - tuple - ``(function, depends_on)`` where ``function`` is a low-dimensional - ``Function`` over the present columns and ``depends_on`` lists those - columns. Consumed by :meth:`AeroCoefficient.from_input`. - """ - independent_vars = list(self.independent_vars) - - try: - with open(file_path, mode="r") as file: - reader = csv.reader(file) - header = next(reader) - except (FileNotFoundError, IOError) as e: - raise ValueError(f"Error reading {coeff_name} CSV file: {e}") from e - except StopIteration as e: - raise ValueError(f"Invalid or empty CSV file for {coeff_name}.") from e - - if not header: - raise ValueError(f"Invalid or empty CSV file for {coeff_name}.") - - header = [column.strip() for column in header] - present_columns = [col for col in independent_vars if col in header] - - invalid_columns = [col for col in header[:-1] if col not in independent_vars] - if invalid_columns: - raise ValueError( - f"Invalid independent variable(s) in {coeff_name} CSV: " - f"{invalid_columns}. Valid options are: {independent_vars}." - ) - - if header[-1] in independent_vars: - raise ValueError( - f"Last column in {coeff_name} CSV must be the coefficient" - " value, not an independent variable." - ) - - if not present_columns: - raise ValueError(f"No independent variables found in {coeff_name} CSV.") - - ordered_present_columns = [ - col for col in header[:-1] if col in independent_vars - ] - - csv_func = Function.from_regular_grid_csv( - file_path, - ordered_present_columns, - coeff_name, - extrapolation="natural", - ) - if csv_func is None: - csv_func = Function( - file_path, - interpolation="linear", - extrapolation="natural", - ) - - # The CSV columns may appear in any order; AeroCoefficient maps the full - # argument tuple to ``ordered_present_columns`` order, so the stored - # Function is queried directly at its own (minimal) dimensionality. - return csv_func, ordered_present_columns diff --git a/rocketpy/rocket/aero_surface/linear_generic_surface.py b/rocketpy/rocket/aero_surface/linear_generic_surface.py index fa085252c..8bf2476ef 100644 --- a/rocketpy/rocket/aero_surface/linear_generic_surface.py +++ b/rocketpy/rocket/aero_surface/linear_generic_surface.py @@ -390,23 +390,6 @@ def compute_all_coefficients(self): ) self.cld = self.compute_damping_coefficient(self.cl_p, self.cl_q, self.cl_r) - self._expose_uniform_coefficients() - - def _expose_uniform_coefficients(self): - """Expose the main force/moment coefficients (``cL, cQ, cD, cm, cn``) as - the composed *forcing* coefficients, so every surface - including - Barrowman ones whose coefficients are derived from geometry - has - uniform, callable accessors over the standard argument tuple. - - The forcing coefficient is the static, flow-state part of the model - (``c_0 + c_alpha*alpha + c_beta*beta``); the rate-damping parts - (``cLd``, …) are dimensionally tied to the reduced rate and remain - separate. The roll coefficient is intentionally **not** exposed as - ``cl`` here: geometry-defined subclasses (nose cones, tails, individual - fins) use the legacy ``cl`` name for their *lift* coefficient. The - composed roll forcing/damping remain available as ``clf``/``cld``. - """ - # pylint: disable=invalid-name self.cL = self.cLf self.cQ = self.cQf self.cD = self.cDf @@ -448,11 +431,11 @@ def _compute_from_coefficients( reynolds : float Reynolds number. pitch_rate : float - Pitch rate in radians per second. + Non-dimensional (reduced) pitch rate, ``q * L_ref / (2 * V)``. yaw_rate : float - Yaw rate in radians per second. + Non-dimensional (reduced) yaw rate, ``r * L_ref / (2 * V)``. roll_rate : float - Roll rate in radians per second. + Non-dimensional (reduced) roll rate, ``p * L_ref / (2 * V)``. Returns ------- @@ -460,20 +443,14 @@ def _compute_from_coefficients( The aerodynamic forces (lift, side_force, drag) and moments (pitch, yaw, roll) in the body frame. """ - # Precompute common values + # Precompute common values. The angular rates arrive already + # non-dimensionalized (reduced rates, e.g. ``q* = q * L_ref / (2 * V)``), + # so the rate-damping terms use the same dynamic-pressure scaling as the + # forcing terms: the ``L_ref / (2 * V)`` factor now lives in the rate + # itself, not in the scaling. (Algebraically identical to the previous + # ``0.5 * rho * V * A * L / 2`` damping scaling applied to raw rates.) dyn_pressure_area = 0.5 * rho * stream_speed**2 * self.reference_area - dyn_pressure_area_damping = ( - 0.5 * rho * stream_speed * self.reference_area * self.reference_length / 2 - ) dyn_pressure_area_length = dyn_pressure_area * self.reference_length - dyn_pressure_area_length_damping = ( - 0.5 - * rho - * stream_speed - * self.reference_area - * self.reference_length**2 - / 2 - ) # Evaluate the composed coefficients through the fast, unvalidated # ``get_value_opt`` path (the composed coefficients are callable-source @@ -481,30 +458,26 @@ def _compute_from_coefficients( # ``__call__``/``get_value`` argument validation in the hot loop). args = (alpha, beta, mach, reynolds, pitch_rate, yaw_rate, roll_rate) - # Compute aerodynamic forces - lift = dyn_pressure_area * self.cLf.get_value_opt( - *args - ) + dyn_pressure_area_damping * self.cLd.get_value_opt(*args) - - side = dyn_pressure_area * self.cQf.get_value_opt( - *args - ) + dyn_pressure_area_damping * self.cQd.get_value_opt(*args) - - drag = dyn_pressure_area * self.cDf.get_value_opt( - *args - ) + dyn_pressure_area_damping * self.cDd.get_value_opt(*args) - - # Compute aerodynamic moments - pitch = dyn_pressure_area_length * self.cmf.get_value_opt( - *args - ) + dyn_pressure_area_length_damping * self.cmd.get_value_opt(*args) - - yaw = dyn_pressure_area_length * self.cnf.get_value_opt( - *args - ) + dyn_pressure_area_length_damping * self.cnd.get_value_opt(*args) + # Compute aerodynamic forces (forcing + reduced-rate damping) + lift = dyn_pressure_area * ( + self.cLf.get_value_opt(*args) + self.cLd.get_value_opt(*args) + ) + side = dyn_pressure_area * ( + self.cQf.get_value_opt(*args) + self.cQd.get_value_opt(*args) + ) + drag = dyn_pressure_area * ( + self.cDf.get_value_opt(*args) + self.cDd.get_value_opt(*args) + ) - roll = dyn_pressure_area_length * self.clf.get_value_opt( - *args - ) + dyn_pressure_area_length_damping * self.cld.get_value_opt(*args) + # Compute aerodynamic moments (forcing + reduced-rate damping) + pitch = dyn_pressure_area_length * ( + self.cmf.get_value_opt(*args) + self.cmd.get_value_opt(*args) + ) + yaw = dyn_pressure_area_length * ( + self.cnf.get_value_opt(*args) + self.cnd.get_value_opt(*args) + ) + roll = dyn_pressure_area_length * ( + self.clf.get_value_opt(*args) + self.cld.get_value_opt(*args) + ) return lift, side, drag, pitch, yaw, roll diff --git a/rocketpy/rocket/aero_surface/rail_buttons.py b/rocketpy/rocket/aero_surface/rail_buttons.py index 19ad16f32..a6fc75b56 100644 --- a/rocketpy/rocket/aero_surface/rail_buttons.py +++ b/rocketpy/rocket/aero_surface/rail_buttons.py @@ -27,6 +27,10 @@ class RailButtons(GenericSurface): calculated but flight dynamics remain unaffected. """ + # Rail buttons carry no aerodynamic force, so they contribute nothing to + # either plane: axisymmetric for the pitch/yaw check. + is_axisymmetric = True + def __init__( self, buttons_distance, diff --git a/rocketpy/rocket/point_mass_rocket.py b/rocketpy/rocket/point_mass_rocket.py index 5965a9c72..dc198c41f 100644 --- a/rocketpy/rocket/point_mass_rocket.py +++ b/rocketpy/rocket/point_mass_rocket.py @@ -57,11 +57,11 @@ class PointMassRocket(Rocket): power_on_drag_input : int, float, callable, array, string, Function Original user input for the drag coefficient with motor on. Preserved for reconstruction and Monte Carlo workflows. - power_off_drag_7d : Function - Drag coefficient function with seven inputs in the order: + power_off_drag_7d : AeroCoefficient + Drag coefficient callable over seven independent variables in the order: alpha, beta, mach, reynolds, pitch_rate, yaw_rate, roll_rate. - power_on_drag_7d : Function - Drag coefficient function with seven inputs in the order: + power_on_drag_7d : AeroCoefficient + Drag coefficient callable over seven independent variables in the order: alpha, beta, mach, reynolds, pitch_rate, yaw_rate, roll_rate. power_off_drag_by_mach : Function Convenience wrapper for power-off drag as a Mach-only function. diff --git a/rocketpy/rocket/rocket.py b/rocketpy/rocket/rocket.py index 2092e89e6..f8b4fc0f1 100644 --- a/rocketpy/rocket/rocket.py +++ b/rocketpy/rocket/rocket.py @@ -1,4 +1,3 @@ -import csv import inspect import math import warnings @@ -21,6 +20,7 @@ Tail, TrapezoidalFins, ) +from rocketpy.rocket.aero_surface.aero_coefficient import AeroCoefficient from rocketpy.rocket.aero_surface.fins.elliptical_fin import EllipticalFin from rocketpy.rocket.aero_surface.fins.free_form_fin import FreeFormFin from rocketpy.rocket.aero_surface.fins.free_form_fins import FreeFormFins @@ -165,12 +165,14 @@ class Rocket: Rocket.power_on_drag_input : int, float, callable, string, array, Function Original user input for rocket's drag coefficient when the motor is on. Preserved for reconstruction and Monte Carlo workflows. - Rocket.power_off_drag_7d : Function - Rocket's drag coefficient with motor off as a 7D function of - (alpha, beta, mach, reynolds, pitch_rate, yaw_rate, roll_rate). - Rocket.power_on_drag_7d : Function - Rocket's drag coefficient with motor on as a 7D function of - (alpha, beta, mach, reynolds, pitch_rate, yaw_rate, roll_rate). + Rocket.power_off_drag_7d : AeroCoefficient + Rocket's drag coefficient with motor off, callable over the seven + independent variables (alpha, beta, mach, reynolds, pitch_rate, + yaw_rate, roll_rate) and stored at its intrinsic dimensionality. + Rocket.power_on_drag_7d : AeroCoefficient + Rocket's drag coefficient with motor on, callable over the seven + independent variables (alpha, beta, mach, reynolds, pitch_rate, + yaw_rate, roll_rate) and stored at its intrinsic dimensionality. Rocket.power_off_drag_by_mach : Function Rocket's drag coefficient with motor off as a function of Mach number. Rocket.power_on_drag_by_mach : Function @@ -348,20 +350,20 @@ def __init__( # pylint: disable=too-many-statements self.surfaces_cp_to_cdm = {} self.rail_buttons = Components() - self.aerodynamic_center = Function( + self._aerodynamic_center = Function( lambda mach: 0, inputs="Mach Number", outputs="Aerodynamic Center Position (m)", ) - self.total_lift_coeff_der = Function( + self._total_lift_coeff_der = Function( lambda mach: 0, inputs="Mach Number", outputs="Total Lift Coefficient Derivative", ) - self.static_margin = Function( + self._static_margin = Function( lambda time: 0, inputs="Time (s)", outputs="Static Margin (c)" ) - self.stability_margin = Function( + self._stability_margin = Function( lambda mach, time: 0, inputs=["Mach", "Time (s)"], outputs="Stability Margin (c)", @@ -369,32 +371,40 @@ def __init__( # pylint: disable=too-many-statements # Yaw-plane counterparts. The pitch-plane attributes above remain the # primary (default) margin; these expose the yaw plane for # non-axisymmetric rockets (see ``evaluate_center_of_pressure``). - self.aerodynamic_center_yaw = Function( + self._aerodynamic_center_yaw = Function( lambda mach: 0, inputs="Mach Number", outputs="Aerodynamic Center Position - Yaw (m)", ) - self.total_side_coeff_der = Function( + self._total_side_coeff_der = Function( lambda mach: 0, inputs="Mach Number", outputs="Total Side Coefficient Derivative", ) - self.static_margin_yaw = Function( + self._static_margin_yaw = Function( lambda time: 0, inputs="Time (s)", outputs="Static Margin - Yaw (c)" ) - self.stability_margin_yaw = Function( + self._stability_margin_yaw = Function( lambda mach, time: 0, inputs=["Mach", "Time (s)"], outputs="Stability Margin - Yaw (c)", ) - # Define aerodynamic drag coefficients - # Coefficients used during flight simulation - self.power_off_drag_7d = self.__process_drag_input( - power_off_drag, "Drag Coefficient with Power Off" + # Define aerodynamic drag coefficients used during flight simulation. + # Drag is stored at its intrinsic dimensionality over the seven base + # independent variables; 1-D inputs are taken as Mach, and "constant" + # extrapolation is used (drag should not extrapolate beyond its range). + self.power_off_drag_7d = AeroCoefficient( + power_off_drag, + name="Drag Coefficient with Power Off", + extrapolation="constant", + single_var="mach", ) - self.power_on_drag_7d = self.__process_drag_input( - power_on_drag, "Drag Coefficient with Power On" + self.power_on_drag_7d = AeroCoefficient( + power_on_drag, + name="Drag Coefficient with Power On", + extrapolation="constant", + single_var="mach", ) self.power_on_drag_by_mach = Function( lambda mach: self.power_on_drag_7d(0, 0, mach, 0, 0, 0, 0), @@ -436,10 +446,12 @@ def __init__( # pylint: disable=too-many-statements self.evaluate_reduced_mass() self.evaluate_thrust_to_weight() - # Evaluate stability (even though no aerodynamic surfaces are present yet) - self.evaluate_center_of_pressure() - self.evaluate_stability_margin() - self.evaluate_static_margin() + # The aerodynamic center and the margins are evaluated lazily (see the + # ``aerodynamic_center`` / ``static_margin`` properties); just flag them + # outdated here. They are rebuilt on first access, once all surfaces and + # the motor have been added. + self._cp_outdated = True + self._margin_outdated = True # Initialize plots and prints object self.prints = _RocketPrints(self) @@ -633,6 +645,80 @@ def evaluate_thrust_to_weight(self): self.thrust_to_weight.set_outputs("Thrust/Weight") self.thrust_to_weight.set_title("Thrust to Weight ratio") + # Lazily-evaluated aerodynamic outputs. + # + # The pitch/yaw aerodynamic centers and the static/stability margins are + # *derived* from the aerodynamic surfaces (and, for the margins, the center + # of mass). Rather than recompute them eagerly on every ``add_*`` call - an + # O(N^2) cost while building, repeated for every rocket in a Monte Carlo run - + # the mutating methods only flag them outdated; the value is rebuilt on first + # access and cached until the next change. ``_cp_outdated`` tracks the + # surface-dependent centers; ``_margin_outdated`` additionally tracks the + # center of mass, so adding a motor refreshes the margins without recomputing + # the surface-only aerodynamic center. + + def _ensure_aerodynamic_center(self): + """Recompute the pitch/yaw aerodynamic centers if a surface changed.""" + if self._cp_outdated: + self.evaluate_center_of_pressure() # clears ``_cp_outdated`` + + def _ensure_margins(self): + """Recompute the static/stability margins if a surface or the center of + mass changed. The underlying aerodynamic center is refreshed lazily by + the margin source closures.""" + if self._margin_outdated: + self._margin_outdated = False + self.evaluate_stability_margin() + self.evaluate_static_margin() + + @property + def aerodynamic_center(self): + """Pitch-plane aerodynamic center vs Mach (lazily evaluated).""" + self._ensure_aerodynamic_center() + return self._aerodynamic_center + + @property + def aerodynamic_center_yaw(self): + """Yaw-plane aerodynamic center vs Mach (lazily evaluated).""" + self._ensure_aerodynamic_center() + return self._aerodynamic_center_yaw + + @property + def total_lift_coeff_der(self): + """Total normal-force-coefficient derivative vs Mach (lazily evaluated).""" + self._ensure_aerodynamic_center() + return self._total_lift_coeff_der + + @property + def total_side_coeff_der(self): + """Total side-force-coefficient derivative vs Mach (lazily evaluated).""" + self._ensure_aerodynamic_center() + return self._total_side_coeff_der + + @property + def static_margin(self): + """Pitch-plane static margin (calibers) vs time (lazily evaluated).""" + self._ensure_margins() + return self._static_margin + + @property + def static_margin_yaw(self): + """Yaw-plane static margin (calibers) vs time (lazily evaluated).""" + self._ensure_margins() + return self._static_margin_yaw + + @property + def stability_margin(self): + """Pitch-plane stability margin (calibers) vs Mach and time (lazy).""" + self._ensure_margins() + return self._stability_margin + + @property + def stability_margin_yaw(self): + """Yaw-plane stability margin (calibers) vs Mach and time (lazy).""" + self._ensure_margins() + return self._stability_margin_yaw + def evaluate_center_of_pressure(self): """Evaluates the rocket's **aerodynamic center** as a function of Mach number, relative to the user-defined rocket reference system. @@ -661,11 +747,17 @@ def evaluate_center_of_pressure(self): reference system. See :doc:`Positions and Coordinate Systems ` for more information. """ + # Mark the pitch/yaw centers up to date before computing, so that a read + # of the ``aerodynamic_center`` property during this method (the + # ``is_axisymmetric`` check below) returns the value being built here + # rather than recursing back into this method. + self._cp_outdated = False + # Re-Initialize total force coefficient derivatives and AC positions - self.total_lift_coeff_der.set_source(lambda mach: 0) - self.aerodynamic_center.set_source(lambda mach: 0) - self.total_side_coeff_der.set_source(lambda mach: 0) - self.aerodynamic_center_yaw.set_source(lambda mach: 0) + self._total_lift_coeff_der.set_source(lambda mach: 0) + self._aerodynamic_center.set_source(lambda mach: 0) + self._total_side_coeff_der.set_source(lambda mach: 0) + self._aerodynamic_center_yaw.set_source(lambda mach: 0) # Calculate total force coefficient derivatives and aerodynamic center if len(self.aerodynamic_surfaces) > 0: @@ -674,31 +766,47 @@ def evaluate_center_of_pressure(self): cp_z = aero_surface.center_of_pressure_z # ref_factor corrects force for different reference areas ref_factor = aero_surface.reference_area / self.area - self.total_lift_coeff_der += ref_factor * lift_coeff_der - self.aerodynamic_center += ( + self._total_lift_coeff_der += ref_factor * lift_coeff_der + self._aerodynamic_center += ( ref_factor * lift_coeff_der * (position.z - self._csys * cp_z) ) # Yaw plane. side_coeff_der = aero_surface.side_coefficient_derivative cp_z_yaw = aero_surface.center_of_pressure_z_yaw - self.total_side_coeff_der += ref_factor * side_coeff_der - self.aerodynamic_center_yaw += ( + self._total_side_coeff_der += ref_factor * side_coeff_der + self._aerodynamic_center_yaw += ( ref_factor * side_coeff_der * (position.z - self._csys * cp_z_yaw) ) # Avoid errors when only zero-lift surfaces are added - if self.total_lift_coeff_der.get_value(0) != 0: - self.aerodynamic_center /= self.total_lift_coeff_der - if self.total_side_coeff_der.get_value(0) != 0: - self.aerodynamic_center_yaw /= self.total_side_coeff_der + if self._total_lift_coeff_der.get_value(0) != 0: + self._aerodynamic_center /= self._total_lift_coeff_der + if self._total_side_coeff_der.get_value(0) != 0: + self._aerodynamic_center_yaw /= self._total_side_coeff_der - self._warn_if_asymmetric_cp() - return self.aerodynamic_center + return self._aerodynamic_center def _cp_plane_max_difference(self): - """Largest pitch- vs yaw-plane aerodynamic center difference, in meters, - over a few sample Mach numbers.""" - sample_machs = (0.0, 0.5, 1.0) + """Largest pitch- vs yaw-plane aerodynamic center difference, in meters. + + The difference is sampled densely across the subsonic, transonic and + supersonic regimes rather than at a few fixed Mach numbers. A + non-axisymmetric configuration (only possible through a ``GenericSurface`` + with non-mirror coefficients) can have its pitch/yaw aerodynamic centers + diverge in any Mach range, and the difference can vanish at isolated Mach + numbers; sparse fixed sampling (e.g. only 0, 0.5, 1) risks a *false + negative* -- silently reporting an asymmetric rocket as axisymmetric, so + the user trusts the pitch-plane-only static margin. The built-in + (Barrowman) surfaces are symmetric by construction, so this returns + exactly 0 for them at every Mach (no false positives). This runs once at + setup, not in the integration loop, so a dense sweep is cheap. + """ + # 0 to 3 in 0.2 steps covers RocketPy's flight regimes (sub/trans/ + # supersonic) with enough resolution that a real asymmetry, which spans a + # Mach *range*, cannot fall entirely between sample points. This only + # runs for rockets that contain a generic surface or individual fin (see + # the by-construction short-circuit in ``is_axisymmetric``). + sample_machs = np.linspace(0.0, 3.0, 16) return max( abs( self.aerodynamic_center.get_value_opt(mach) @@ -715,9 +823,38 @@ def is_axisymmetric(self): ``stability_margin`` describe the PITCH plane only and differ from their ``*_yaw`` counterparts (``aerodynamic_center_yaw``, ``static_margin_yaw``, ``stability_margin_yaw``).""" + # Fast path: the built-in nose, tail and fin sets contribute identically + # to the pitch and yaw planes by construction, so a rocket made only of + # surfaces that are axisymmetric-by-construction is axisymmetric without + # evaluating anything. Only a generic surface or an individual fin can + # break it, in which case fall back to the numeric Mach sweep below. + if all( + getattr(surface, "is_axisymmetric", False) + for surface, _ in self.aerodynamic_surfaces + ): + return True # Tolerance relative to the rocket diameter (caliber-scale). return self._cp_plane_max_difference() <= 1e-6 * (2 * self.radius) + def _warn_if_not_axisymmetric(self): + """Warn, at surface-add time, when the rocket is non-axisymmetric so the + user knows the scalar ``static_margin``/``stability_margin`` describe the + pitch plane only. Short-circuits with no computation for rockets built + solely from axisymmetric-by-construction surfaces (the Barrowman set); + only a generic surface or individual fin triggers the Mach sweep.""" + if not self.aerodynamic_surfaces or self.is_axisymmetric: + return + max_diff = self._cp_plane_max_difference() + warnings.warn( + "Pitch- and yaw-plane aerodynamic centers differ " + f"(max difference ~{max_diff:.4g} m): the rocket is not " + "axisymmetric. 'aerodynamic_center', 'static_margin' and " + "'stability_margin' describe the PITCH plane; use " + "'aerodynamic_center_yaw', 'static_margin_yaw' and " + "'stability_margin_yaw' for the yaw plane.", + stacklevel=3, + ) + @property def cp_position(self): """Deprecated alias for :attr:`aerodynamic_center` (the linearized, @@ -732,34 +869,6 @@ def cp_position(self): ) return self.aerodynamic_center - @property - def cp_position_yaw(self): - """Deprecated alias for :attr:`aerodynamic_center_yaw`.""" - warnings.warn( - "'cp_position_yaw' is deprecated and will be removed in a future " - "release; use 'aerodynamic_center_yaw' instead.", - DeprecationWarning, - stacklevel=2, - ) - return self.aerodynamic_center_yaw - - def _warn_if_asymmetric_cp(self): - """Warn when the pitch- and yaw-plane aerodynamic centers disagree, i.e. - the rocket is not axisymmetric. The ``static_margin``/ - ``stability_margin`` attributes then describe the pitch plane only; the - yaw-plane counterparts are ``*_yaw``.""" - if not self.is_axisymmetric: - max_diff = self._cp_plane_max_difference() - warnings.warn( - "Pitch- and yaw-plane aerodynamic centers differ " - f"(max difference ~{max_diff:.4g} m): the rocket is not " - "axisymmetric. 'aerodynamic_center', 'static_margin' and " - "'stability_margin' describe the PITCH plane; use " - "'aerodynamic_center_yaw', 'static_margin_yaw' and " - "'stability_margin_yaw' for the yaw plane.", - stacklevel=2, - ) - def _aerodynamic_center_limit(self, alpha, beta, mach): """Small-incidence limit of :meth:`center_of_pressure`: the linearized aerodynamic center, blended between the pitch and yaw planes by the @@ -872,8 +981,15 @@ def _aerodynamic_forces_and_moments(self, alpha, beta, mach, reynolds=0.0): for surface, _ in self.aerodynamic_surfaces: cp = self.surfaces_cp_to_cdm[surface] forces = surface.compute_forces_and_moments( - stream_velocity, stream_speed, mach, 1.0, cp, omega, - density, dynamic_viscosity, 0.0, + stream_velocity, + stream_speed, + mach, + 1.0, + cp, + omega, + density, + dynamic_viscosity, + 0.0, ) totals = [acc + value for acc, value in zip(totals, forces)] return (*totals, stream_speed) @@ -970,156 +1086,6 @@ def aerodynamic_coefficients_full(self, alpha, beta, mach, reynolds=0.0): "cl": m3 / dynamic_pressure_area_length, } - def center_of_pressure_over_alpha(self, mach=0.0, beta=0.0, reynolds=0.0): - """Center of pressure position as a Function of angle of attack. - - Convenience wrapper around :meth:`center_of_pressure` that fixes the - Mach number, sideslip and Reynolds number and exposes the center of - pressure travel with angle of attack as a plottable :class:`Function`. - - Parameters - ---------- - mach : float, optional - Free-stream Mach number. Default 0. - beta : float, optional - Sideslip angle, in radians. Default 0. - reynolds : float, optional - Rocket-level Reynolds number. Default 0. - - Returns - ------- - Function - Center of pressure position (m) versus angle of attack (rad). - """ - return Function( - lambda alpha: self.center_of_pressure(alpha, beta, mach, reynolds), - inputs="Angle of Attack (rad)", - outputs="Center of Pressure Position (m)", - title="Center of Pressure vs Angle of Attack", - ) - - def stability_margin_over_alpha( - self, mach=0.0, beta=0.0, reynolds=0.0, time=0.0 - ): - """Stability margin in calibers as a Function of angle of attack. - - The center-of-gravity-to-center-of-pressure distance divided by the - rocket diameter, using the nonlinear :meth:`center_of_pressure` so the - margin reflects how the center of pressure moves with incidence. This is - the angle-of-attack analogue of :attr:`static_margin` (which is the - ``alpha = 0`` value as a function of time). - - Parameters - ---------- - mach : float, optional - Free-stream Mach number. Default 0. - beta : float, optional - Sideslip angle, in radians. Default 0. - reynolds : float, optional - Rocket-level Reynolds number. Default 0. - time : float, optional - Time at which the center of mass is evaluated, in seconds. Default 0 - (the fully loaded condition). - - Returns - ------- - Function - Stability margin (calibers) versus angle of attack (rad). - """ - center_of_pressure = self.center_of_pressure_over_alpha(mach, beta, reynolds) - center_of_mass = self.center_of_mass.get_value_opt(time) - diameter = 2 * self.radius - return Function( - lambda alpha: ( - (center_of_mass - center_of_pressure.get_value_opt(alpha)) - / diameter - * self._csys - ), - inputs="Angle of Attack (rad)", - outputs="Stability Margin (c)", - title="Stability Margin vs Angle of Attack", - ) - - def center_of_pressure_over_beta(self, mach=0.0, alpha=0.0, reynolds=0.0): - """Center of pressure position as a Function of sideslip angle. - - Yaw-plane companion to :meth:`center_of_pressure_over_alpha`: fixes the - Mach number, angle of attack and Reynolds number and exposes the center - of pressure travel with sideslip as a plottable :class:`Function`. - - Parameters - ---------- - mach : float, optional - Free-stream Mach number. Default 0. - alpha : float, optional - Angle of attack, in radians. Default 0. - reynolds : float, optional - Rocket-level Reynolds number. Default 0. - - Returns - ------- - Function - Center of pressure position (m) versus sideslip angle (rad). - """ - def _cp(beta): - # At the exact origin the CP is a 0/0 limit; the general - # center_of_pressure resolves it to the PITCH-plane aerodynamic - # center (consistent with static_margin). For a pure-sideslip sweep - # the correct limit is instead the YAW-plane aerodynamic center, - # which the nonlinear value converges to as beta grows -- use it at - # beta = 0 so the sweep stays continuous. - if alpha == 0.0 and beta == 0.0: - return self.aerodynamic_center_yaw.get_value_opt(mach) - return self.center_of_pressure(alpha, beta, mach, reynolds) - - return Function( - _cp, - inputs="Sideslip Angle (rad)", - outputs="Center of Pressure Position (m)", - title="Center of Pressure vs Sideslip Angle", - ) - - def stability_margin_over_beta( - self, mach=0.0, alpha=0.0, reynolds=0.0, time=0.0 - ): - """Stability margin in calibers as a Function of sideslip angle. - - Yaw-plane companion to :meth:`stability_margin_over_alpha`: the - center-of-gravity-to-center-of-pressure distance divided by the rocket - diameter, using the nonlinear :meth:`center_of_pressure` so the margin - reflects how the center of pressure moves with sideslip. - - Parameters - ---------- - mach : float, optional - Free-stream Mach number. Default 0. - alpha : float, optional - Angle of attack, in radians. Default 0. - reynolds : float, optional - Rocket-level Reynolds number. Default 0. - time : float, optional - Time at which the center of mass is evaluated, in seconds. Default 0 - (the fully loaded condition). - - Returns - ------- - Function - Stability margin (calibers) versus sideslip angle (rad). - """ - center_of_pressure = self.center_of_pressure_over_beta(mach, alpha, reynolds) - center_of_mass = self.center_of_mass.get_value_opt(time) - diameter = 2 * self.radius - return Function( - lambda beta: ( - (center_of_mass - center_of_pressure.get_value_opt(beta)) - / diameter - * self._csys - ), - inputs="Sideslip Angle (rad)", - outputs="Stability Margin (c)", - title="Stability Margin vs Sideslip Angle", - ) - def evaluate_surfaces_cp_to_cdm(self): """Calculates the relative position of each aerodynamic surface center of pressure to the rocket's center of dry mass in Body Axes Coordinate @@ -1173,7 +1139,7 @@ def evaluate_stability_margin(self): the center of pressure and the center of mass, divided by the rocket's diameter. """ - self.stability_margin.set_source( + self._stability_margin.set_source( lambda mach, time: ( ( ( @@ -1186,7 +1152,7 @@ def evaluate_stability_margin(self): ) ) # Yaw-plane stability margin (equal to the pitch plane when axisymmetric) - self.stability_margin_yaw.set_source( + self._stability_margin_yaw.set_source( lambda mach, time: ( ( ( @@ -1198,7 +1164,7 @@ def evaluate_stability_margin(self): * self._csys ) ) - return self.stability_margin + return self._stability_margin def evaluate_static_margin(self): """Calculates the static margin of the rocket as a function of time. @@ -1211,7 +1177,7 @@ def evaluate_static_margin(self): pressure and the center of mass, divided by the rocket's diameter. """ # Calculate static margin - self.static_margin.set_source( + self._static_margin.set_source( lambda time: ( ( self.center_of_mass.get_value_opt(time) @@ -1221,16 +1187,16 @@ def evaluate_static_margin(self): ) ) # Change sign if coordinate system is upside down - self.static_margin *= self._csys - self.static_margin.set_inputs("Time (s)") - self.static_margin.set_outputs("Static Margin (c)") - self.static_margin.set_title("Static Margin") - self.static_margin.set_discrete( + self._static_margin *= self._csys + self._static_margin.set_inputs("Time (s)") + self._static_margin.set_outputs("Static Margin (c)") + self._static_margin.set_title("Static Margin") + self._static_margin.set_discrete( lower=0, upper=self.motor.burn_out_time, samples=200 ) # Yaw-plane static margin (equal to the pitch plane when axisymmetric) - self.static_margin_yaw.set_source( + self._static_margin_yaw.set_source( lambda time: ( ( self.center_of_mass.get_value_opt(time) @@ -1239,14 +1205,14 @@ def evaluate_static_margin(self): / (2 * self.radius) ) ) - self.static_margin_yaw *= self._csys - self.static_margin_yaw.set_inputs("Time (s)") - self.static_margin_yaw.set_outputs("Static Margin - Yaw (c)") - self.static_margin_yaw.set_title("Static Margin - Yaw") - self.static_margin_yaw.set_discrete( + self._static_margin_yaw *= self._csys + self._static_margin_yaw.set_inputs("Time (s)") + self._static_margin_yaw.set_outputs("Static Margin - Yaw (c)") + self._static_margin_yaw.set_title("Static Margin - Yaw") + self._static_margin_yaw.set_discrete( lower=0, upper=self.motor.burn_out_time, samples=200 ) - return self.static_margin + return self._static_margin def evaluate_dry_inertias(self): """Calculates and returns the rocket's dry inertias relative to @@ -1576,10 +1542,10 @@ def add_motor(self, motor, position): # pylint: disable=too-many-statements self.evaluate_inertias() self.evaluate_reduced_mass() self.evaluate_thrust_to_weight() - self.evaluate_center_of_pressure() self.evaluate_surfaces_cp_to_cdm() - self.evaluate_stability_margin() - self.evaluate_static_margin() + # The motor changes the center of mass (and thus the margins) but not the + # surface-only aerodynamic center; flag only the margins for lazy rebuild. + self._margin_outdated = True self.evaluate_com_to_cdm_function() self.evaluate_nozzle_gyration_tensor() @@ -1658,9 +1624,15 @@ def add_surfaces(self, surfaces, positions): else: self.__add_single_surface(surfaces, positions) - self.evaluate_center_of_pressure() - self.evaluate_stability_margin() - self.evaluate_static_margin() + # Adding a surface changes both the aerodynamic center and the margins; + # flag them for lazy rebuild on next access (see the properties). + self._cp_outdated = True + self._margin_outdated = True + + # The asymmetry warning is the one piece evaluated eagerly: it is a + # setup-time hint about which margin attributes to trust, and the check + # is free for axisymmetric-by-construction (Barrowman) rockets. + self._warn_if_not_axisymmetric() def add_vehicle_aerodynamic_surface( self, coefficients, reference_position=None, name="Vehicle Aerodynamics" @@ -2556,73 +2528,6 @@ def controller_wrapper(**kwargs): else: return air_brakes - def add_controllable_surface( - self, - surface, - position, - controller_function, - sampling_rate, - controlled_object_name="controllable_surface", - context=None, - name="Controller", - controller_needs=None, - return_controller=False, - ): - """Add a controllable aerodynamic surface and the controller that drives - its deflection during flight. - - The surface is added like any other aerodynamic surface (so it flows - through the standard per-surface force/moment computation), and a - controller is registered to mutate the surface's control variables each - sample. The controller function should set the surface's deflection via - ``surface.set_control(name, value)``. - - Parameters - ---------- - surface : ControllableGenericSurface - The controllable surface to add. - position : int, float, tuple, list, Vector - Position of the surface, in the same convention as - :meth:`add_surfaces`. - controller_function : callable - Control logic, ``controller_function(**kwargs) -> dict or None``. - See :class:`rocketpy.control.controller._Controller` for the - available ``kwargs``. The controlled surface is exposed under - ``controlled_object_name``. - sampling_rate : float - Controller sampling rate in hertz. - controlled_object_name : str, optional - Friendly name under which the surface is exposed in the controller - ``kwargs``. Default ``"controllable_surface"``. - context : dict, optional - Initial persistent controller context. Default ``None``. - name : str, optional - Controller name. Default ``"Controller"``. - controller_needs : list or frozenset of str or None, optional - Expensive simulation values the controller accesses. - return_controller : bool, optional - If True, also return the created controller. Default False. - - Returns - ------- - ControllableGenericSurface or tuple - The surface, or ``(surface, controller)`` if ``return_controller``. - """ - self.add_surfaces(surface, position) - controller = _Controller( - controller_function=controller_function, - controlled_objects=surface, - controlled_objects_name=controlled_object_name, - sampling_rate=sampling_rate, - context=context if context is not None else {}, - name=name, - controller_needs=controller_needs, - ) - self._add_controllers(controller) - if return_controller: - return surface, controller - return surface - def set_rail_buttons( self, upper_button_position, @@ -3006,271 +2911,3 @@ def from_dict(cls, data): rocket._add_controllers(controller) return rocket - - def __process_drag_input(self, input_data, coeff_name): - """Process drag coefficient input and normalize it to a 7D Function. - - Parameters - ---------- - input_data : int, float, str, callable, Function - Input data to be processed. - coeff_name : str - Name of the coefficient being processed for error reporting. - - Returns - ------- - Function - Function object with 7 input arguments in the following order: - alpha, beta, mach, reynolds, pitch_rate, yaw_rate, roll_rate. - """ - inputs = [ - "alpha", - "beta", - "mach", - "reynolds", - "pitch_rate", - "yaw_rate", - "roll_rate", - ] - - # Helper: lift a 1D Mach-only source into the required 7D signature. - def _wrap_mach_only_source(mach_source): - return Function( - lambda alpha, beta, mach, reynolds, pitch_rate, yaw_rate, roll_rate: ( - mach_source(mach) - ), - inputs, - [coeff_name], - interpolation="linear", - extrapolation="constant", - ) - - # Helper: enforce that Function-based inputs are either 1D (Mach) or 7D. - def _validate_function_domain_dimension(function): - if function.__dom_dim__ not in (1, 7): - raise ValueError( - f"{coeff_name} function must have either 1 input argument " - "(mach) or 7 input arguments (alpha, beta, mach, reynolds, " - "pitch_rate, yaw_rate, roll_rate), in that order." - ) - - # Helper: count required positional arguments in a callable. - def _count_positional_args(callable_obj): - signature = inspect.signature(callable_obj) - positional_params = [ - parameter - for parameter in signature.parameters.values() - if parameter.kind - in ( - inspect.Parameter.POSITIONAL_ONLY, - inspect.Parameter.POSITIONAL_OR_KEYWORD, - ) - and parameter.default is inspect.Parameter.empty - ] - return len(positional_params) - - # Case 1: string input can be a CSV path or any Function-supported source. - if isinstance(input_data, str): - if input_data.lower().endswith(".csv"): - return self.__load_rocket_drag_csv(input_data, coeff_name) - - function_data = Function(input_data) - _validate_function_domain_dimension(function_data) - if function_data.__dom_dim__ == 7: - function_data.set_extrapolation("constant") - return function_data - return _wrap_mach_only_source(function_data.get_value_opt) - - # Case 2: Function input is accepted directly after domain validation. - if isinstance(input_data, Function): - _validate_function_domain_dimension(input_data) - if input_data.__dom_dim__ == 7: - input_data.set_extrapolation("constant") - return input_data - return _wrap_mach_only_source(input_data.get_value_opt) - - # Case 3: callable input must expose either 1 (Mach) or 7 arguments. - if callable(input_data): - n_positional_args = _count_positional_args(input_data) - if n_positional_args not in (1, 7): - raise ValueError( - f"{coeff_name} callable must have either 1 positional " - "argument (mach) or 7 positional arguments (alpha, beta, " - "mach, reynolds, pitch_rate, yaw_rate, roll_rate), in that " - "order." - ) - - if n_positional_args == 1: - return _wrap_mach_only_source(input_data) - - return Function( - input_data, - inputs, - [coeff_name], - interpolation="linear", - extrapolation="constant", - ) - - # Case 4: scalar input means a constant drag coefficient in all conditions. - if isinstance(input_data, (int, float)): - return Function( - lambda alpha, beta, mach, reynolds, pitch_rate, yaw_rate, roll_rate: ( - float(input_data) - ), - inputs, - [coeff_name], - interpolation="linear", - extrapolation="constant", - ) - - # If is list/tuple try to pass it to a function. - # If composed of lists/tuples len 2, then interpret as function of mach - # Otherwise interpret it as function of all 7 variables - # This reuses Function's parser and then feeds back into this same pipeline. - if isinstance(input_data, (list, tuple)): - if all( - isinstance(item, (list, tuple)) and (len(item) == 2 or len(item) == 8) - for item in input_data - ): - try: - return self.__process_drag_input( - Function(list(input_data)), coeff_name - ) - except (TypeError, ValueError) as e: - raise ValueError( - f"Invalid list/tuple format for {coeff_name}. Expected " - "a list of [mach, coefficient] pairs or a list of " - "[alpha, beta, mach, reynolds, pitch_rate, yaw_rate, " - "roll_rate, coefficient] entries." - ) from e - - raise TypeError( - f"Invalid input for {coeff_name}: must be int, float, CSV file path, " - "Function, or callable." - ) - - def __load_rocket_drag_csv(self, file_path, coeff_name): # pylint: disable=too-many-statements,import-outside-toplevel - """Load Rocket drag CSV into a 7D Function. - - Supports either headerless two-column (mach, coefficient) tables or - header-based multi-variable CSV tables. - """ - independent_vars = [ - "alpha", - "beta", - "mach", - "reynolds", - "pitch_rate", - "yaw_rate", - "roll_rate", - ] - - def _is_numeric(value): - try: - float(value) - return True - except (TypeError, ValueError): - try: - int(value) - return True - except (TypeError, ValueError): - return False - - try: - with open(file_path, mode="r") as file: - reader = csv.reader(file) - first_row = next(reader) - except (FileNotFoundError, IOError) as e: - raise ValueError(f"Error reading {coeff_name} CSV file: {e}") from e - except StopIteration as e: - raise ValueError(f"Invalid or empty CSV file for {coeff_name}.") from e - - if not first_row: - raise ValueError(f"Invalid or empty CSV file for {coeff_name}.") - - is_headerless_two_column = len(first_row) == 2 and all( - _is_numeric(cell) for cell in first_row - ) - - if is_headerless_two_column: - csv_func = Function( - file_path, - interpolation="linear", - extrapolation="constant", - ) - - def mach_wrapper( - _alpha, - _beta, - mach, - _reynolds, - _pitch_rate, - _yaw_rate, - _roll_rate, - ): - return csv_func(mach) - - return Function( - mach_wrapper, - independent_vars, - [coeff_name], - interpolation="linear", - extrapolation="constant", - ) - - header = [column.strip() for column in first_row] - present_columns = [col for col in independent_vars if col in header] - - invalid_columns = [col for col in header[:-1] if col not in independent_vars] - if invalid_columns: - raise ValueError( - f"Invalid independent variable(s) in {coeff_name} CSV: " - f"{invalid_columns}. Valid options are: {independent_vars}." - ) - - if header[-1] in independent_vars: - raise ValueError( - f"Last column in {coeff_name} CSV must be the coefficient " - "value, not an independent variable." - ) - - if not present_columns: - raise ValueError(f"No independent variables found in {coeff_name} CSV.") - - ordered_present_columns = [ - col for col in header[:-1] if col in independent_vars - ] - - csv_func = Function.from_regular_grid_csv( - file_path, - ordered_present_columns, - coeff_name, - extrapolation="constant", - ) - if csv_func is None: - csv_func = Function( - file_path, - interpolation="linear", - extrapolation="constant", - ) - - def wrapper(alpha, beta, mach, reynolds, pitch_rate, yaw_rate, roll_rate): - args_by_name = { - "alpha": alpha, - "beta": beta, - "mach": mach, - "reynolds": reynolds, - "pitch_rate": pitch_rate, - "yaw_rate": yaw_rate, - "roll_rate": roll_rate, - } - selected_args = [args_by_name[col] for col in ordered_present_columns] - return csv_func(*selected_args) - - return Function( - wrapper, - independent_vars, - [coeff_name], - interpolation="linear", - extrapolation="constant", - ) diff --git a/rocketpy/simulation/flight.py b/rocketpy/simulation/flight.py index 11eb76477..781b47c0a 100644 --- a/rocketpy/simulation/flight.py +++ b/rocketpy/simulation/flight.py @@ -2377,20 +2377,26 @@ def realized_stability_margin(self): diameter = 2 * self.rocket.radius time = self.time - alpha = np.array( - [self.partial_angle_of_attack.get_value_opt(t) for t in time] - ) - beta = np.array([self.angle_of_sideslip.get_value_opt(t) for t in time]) + # These are all tabulated at exactly ``self.time`` (their source is + # ``column_stack([self.time, values])`` or same-grid Function + # arithmetic), so read the values straight from ``.source`` instead of + # re-evaluating per node. ``mach`` is reused by both the realized cp and + # the linear margin below. ``center_of_mass`` is a rocket-level Function + # on a different grid, so it still needs ``get_value_opt(t)``. + alpha = self.partial_angle_of_attack.source[:, 1] + beta = self.angle_of_sideslip.source[:, 1] + mach = self.mach_number.source[:, 1] + reynolds = self.reynolds_number.source[:, 1] center_of_pressure = np.array( [ self.rocket.center_of_pressure( np.deg2rad(a), np.deg2rad(b), - self.mach_number.get_value_opt(t), - self.reynolds_number.get_value_opt(t), + m, + re, ) - for a, b, t in zip(alpha, beta, time) + for a, b, m, re in zip(alpha, beta, mach, reynolds) ] ) center_of_mass = np.array( @@ -2400,10 +2406,8 @@ def realized_stability_margin(self): margin_model = np.array( [ - self.rocket.stability_margin.get_value_opt( - self.mach_number.get_value_opt(t), t - ) - for t in time + self.rocket.stability_margin.get_value_opt(m, t) + for m, t in zip(mach, time) ] ) @@ -2417,9 +2421,7 @@ def realized_stability_margin(self): # Fall back fully to the linear margin where the rocket is barely moving # (dynamic pressure below 1% of its flight-wide peak: rail, rest, apogee). - dynamic_pressure = np.array( - [self.dynamic_pressure.get_value_opt(t) for t in time] - ) + dynamic_pressure = self.dynamic_pressure.source[:, 1] meaningful = dynamic_pressure > 0.01 * dynamic_pressure.max() margin = np.where(meaningful, margin_blended, margin_model) @@ -2446,9 +2448,7 @@ def _lateral_inertia(self, dry_lateral_inertia, motor_lateral_inertia): total = propellant_mass + dry_mass mu = (propellant_mass * dry_mass / total) if total > 0 else 0.0 inertia[i] = ( - dry_lateral_inertia - + motor_lateral_inertia.get_value_opt(t) - + mu * b**2 + dry_lateral_inertia + motor_lateral_inertia.get_value_opt(t) + mu * b**2 ) return inertia @@ -2494,8 +2494,7 @@ def _dynamic_stability(self, lift_slope, stability_margin, lateral_inertia): for surface, position in self.rocket.aerodynamic_surfaces: slope = surface.lift_coefficient_derivative.get_value_opt(mach) cp_position = ( - position.z - - csys * surface.center_of_pressure_z.get_value_opt(mach) + position.z - csys * surface.center_of_pressure_z.get_value_opt(mach) ) arm = cp_position - center_of_mass ref_factor = surface.reference_area / area @@ -2503,9 +2502,10 @@ def _dynamic_stability(self, lift_slope, stability_margin, lateral_inertia): damping_aero *= 0.5 * density * speed * area # Jet (propulsive) damping: mdot (x_nozzle - x_cm)^2. - damping_jet = abs(mass_flow_rate.get_value_opt(t)) * ( - nozzle_position - center_of_mass - ) ** 2 + damping_jet = ( + abs(mass_flow_rate.get_value_opt(t)) + * (nozzle_position - center_of_mass) ** 2 + ) damping[i] = damping_aero + damping_jet positive_corrective = np.clip(corrective, 0.0, None) diff --git a/rocketpy/simulation/helpers/flight_derivatives.py b/rocketpy/simulation/helpers/flight_derivatives.py index ac28b08cd..dcd275076 100644 --- a/rocketpy/simulation/helpers/flight_derivatives.py +++ b/rocketpy/simulation/helpers/flight_derivatives.py @@ -91,9 +91,23 @@ def _aerodynamic_drag_force( # Air brakes are drag-only and may override the rocket drag. for air_brakes in rocket.air_brakes: if air_brakes.deployment_level > 0: + # Air brakes are a (controllable) generic surface, so feed the + # coefficient the non-dimensional reduced rates, like every other + # generic surface (see GenericSurface.compute_forces_and_moments). + reduced = ( + air_brakes.reference_length / (2 * stream_speed) + if stream_speed > 0 + else 0.0 + ) air_brakes_cd = air_brakes.cD.get_value_opt( *air_brakes._coefficient_arguments( - alpha, beta, mach, reynolds, omega[0], omega[1], omega[2] + alpha, + beta, + mach, + reynolds, + omega[0] * reduced, + omega[1] * reduced, + omega[2] * reduced, ) ) air_brakes_force = ( @@ -342,7 +356,14 @@ def u_dot(flight, t, u, post_processing=False): dynamic_viscosity, ) R3 = _aerodynamic_drag_force( - flight, t, rho, free_stream_speed, alpha, beta, mach, reynolds, + flight, + t, + rho, + free_stream_speed, + alpha, + beta, + mach, + reynolds, (omega1, omega2, omega3), ) # Off center moment @@ -578,7 +599,14 @@ def u_dot_generalized_3dof(flight, t, u, post_processing=False): # Drag computation (rocket body drag + air brakes) R1, R2 = 0, 0 R3 = _aerodynamic_drag_force( - flight, t, rho, free_stream_speed, alpha, beta, mach, reynolds, + flight, + t, + rho, + free_stream_speed, + alpha, + beta, + mach, + reynolds, (omega1, omega2, omega3), ) @@ -816,7 +844,14 @@ def u_dot_generalized(flight, t, u, post_processing=False): else: net_thrust = 0 R3 = _aerodynamic_drag_force( - flight, t, rho, free_stream_speed, alpha, beta, mach, reynolds, + flight, + t, + rho, + free_stream_speed, + alpha, + beta, + mach, + reynolds, (omega1, omega2, omega3), ) # Get rocket velocity in body frame From e76c31c3ccce817caf29dc7115bb25f02298f004 Mon Sep 17 00:00:00 2001 From: MateusStano Date: Thu, 2 Jul 2026 08:16:38 -0300 Subject: [PATCH 03/22] TST: add tests --- .pylintrc | 2 + .../center_of_pressure_and_stability.rst | 107 +++++---- docs/user/rocket/generic_surface.rst | 22 +- rocketpy/plots/flight_plots.py | 6 - rocketpy/plots/rocket_plots.py | 38 ++- .../controllable_generic_surface.py | 33 ++- .../aero_surface/fins/elliptical_fin.py | 4 +- .../rocket/aero_surface/fins/free_form_fin.py | 4 +- .../aero_surface/fins/trapezoidal_fin.py | 4 +- rocketpy/rocket/rocket.py | 168 ++++--------- rocketpy/simulation/flight.py | 87 +------ .../aero_surface/test_aero_coefficient.py | 226 ++++++++++++++++++ .../test_barrowman_generic_equivalence.py | 163 +++++++++++++ .../test_controllable_generic_surface.py | 101 ++++++++ .../aero_surface/test_generic_surfaces.py | 39 ++- .../aero_surface/test_individual_fins.py | 122 +++++++++- .../test_linear_generic_surfaces.py | 30 +++ .../test_unsteady_generic_surface.py | 71 ++++++ tests/unit/rocket/test_rocket.py | 46 ++-- tests/unit/rocket/test_stability_rework.py | 93 +++++++ tests/unit/simulation/test_event_scheduler.py | 0 tests/unit/simulation/test_flight.py | 10 +- 22 files changed, 1046 insertions(+), 330 deletions(-) create mode 100644 tests/unit/rocket/aero_surface/test_aero_coefficient.py create mode 100644 tests/unit/rocket/aero_surface/test_barrowman_generic_equivalence.py create mode 100644 tests/unit/rocket/aero_surface/test_controllable_generic_surface.py create mode 100644 tests/unit/rocket/aero_surface/test_unsteady_generic_surface.py create mode 100644 tests/unit/rocket/test_stability_rework.py create mode 100644 tests/unit/simulation/test_event_scheduler.py diff --git a/.pylintrc b/.pylintrc index 3b059ee2c..9e4fd8e89 100644 --- a/.pylintrc +++ b/.pylintrc @@ -231,6 +231,8 @@ good-names=FlightPhases, R_uncanted, R_body_to_fin, Re, # Reynolds number + cL_alpha, + cQ_beta, # Good variable names regexes, separated by a comma. If names match any regex, # they will always be accepted diff --git a/docs/technical/aerodynamics/center_of_pressure_and_stability.rst b/docs/technical/aerodynamics/center_of_pressure_and_stability.rst index 42f281cab..80e7de108 100644 --- a/docs/technical/aerodynamics/center_of_pressure_and_stability.rst +++ b/docs/technical/aerodynamics/center_of_pressure_and_stability.rst @@ -143,10 +143,9 @@ orientation. Because the weight is the normal-force slope, a zero-lift surface ``Rocket.aerodynamic_center``. .. note:: - ``Rocket.cp_position`` is a **deprecated alias** for - ``Rocket.aerodynamic_center``. The historical "center of pressure" attribute - was always the aerodynamic center; the alias is kept (with a - ``DeprecationWarning``) for backward compatibility. + ``Rocket.cp_position`` is an **alias** for ``Rocket.aerodynamic_center``. The + historical "center of pressure" attribute was always the aerodynamic center; + the alias is kept for backward compatibility and convenience. Center of pressure (nonlinear) ------------------------------ @@ -161,17 +160,21 @@ attack/sideslip: x_\text{CP}(\alpha,\beta,M,Re) = x_\text{cdm} + c\,\frac{M_2 R_1 - M_1 R_2}{R_1^2 + R_2^2} -evaluated from the Layer-1 aggregate (:math:`M = r\times F`). Unlike the AC, the -CP **moves with incidence**. It is a :math:`0/0` limit at zero incidence and -converges to the AC as :math:`\alpha,\beta \to 0`. This is -:meth:`rocketpy.Rocket.center_of_pressure`. - -To stay well-conditioned, ``center_of_pressure`` returns the aerodynamic-center -limit below ~1° of total incidence — blended between the pitch and yaw planes by -the direction of incidence (:meth:`rocketpy.Rocket._aerodynamic_center_limit`) — -so it is continuous and never spikes as the rocket oscillates through zero -incidence. The design-time travel is exposed by -``center_of_pressure_over_alpha`` and ``center_of_pressure_over_beta``. +evaluated from the Layer-1 aggregate (:math:`M = r\times F`). Equivalently, per +plane, :math:`x_\text{CP} = x_\text{cdm} + c\,L_\text{ref}\,C_m/C_L` (pitch) and +:math:`+\,c\,L_\text{ref}\,C_n/C_Q` (yaw). Unlike the AC, the CP **moves with +incidence**. + +The CP is a **genuinely partial quantity**: it is a :math:`0/0` limit at zero +incidence (the normal force vanishes), undefined there, and converges to the AC +as :math:`\alpha,\beta \to 0`. Because it plays no role in the equations of +motion and the stability margins are correctly built on the AC (a slope, see +Layer 3), RocketPy does **not** expose it as a dedicated method — that would +force an arbitrary regularization of a real singularity. When the +force-application CP is genuinely wanted (e.g. comparing against wind-tunnel or +CFD CP-vs-:math:`\alpha` data), it is reconstructed on demand from the aggregate +coefficients :meth:`rocketpy.Rocket.aerodynamic_coefficients_full` using the +relation above, with the caller deciding how to treat the zero-incidence limit. Pitch and yaw planes -------------------- @@ -187,8 +190,7 @@ They coincide for an axisymmetric rocket; ``Rocket.is_axisymmetric`` reports whether they agree (to caliber tolerance) and :meth:`rocketpy.Rocket.evaluate_center_of_pressure` warns when they do not, since the scalar ``static_margin``/``stability_margin`` then describe the pitch plane -only. The **nonlinear** CP needs no such split — evaluated at the actual combined -incidence, a single axial location already captures both planes. +only, and the ``*_yaw`` counterparts expose the yaw plane. Layer 3 — Static and stability margins ====================================== @@ -213,24 +215,37 @@ incompressible (:math:`M=0`) limit, a function of time; the stability margin (:meth:`rocketpy.Rocket.evaluate_stability_margin`) is a function of Mach and time. The ``*_yaw`` counterparts use ``aerodynamic_center_yaw``. -**Realized (center-of-pressure) margin.** Built on the nonlinear CP at the -actual flight incidence, it reflects how the stability reference travels with -:math:`\alpha,\beta` (and combines the planes for a non-axisymmetric rocket). - -At the :class:`rocketpy.Flight` level: - -- ``Flight.stability_margin`` evaluates the **linear** margin along the realized - Mach and time — smooth, conventional, and the source of - ``initial_stability_margin`` / ``out_of_rail_stability_margin`` / - ``min_stability_margin`` / ``max_stability_margin``; -- ``Flight.realized_stability_margin`` evaluates the **nonlinear** CP at the - realized :math:`\alpha,\beta,M,Re`, falling back to the linear margin only at - negligible dynamic pressure (rail, rest, apogee), where the realized incidence - is meaningless. +At the :class:`rocketpy.Flight` level, ``Flight.stability_margin`` (and +``stability_margin_yaw``) evaluates the linear margin along the realized Mach and +time — smooth, conventional, and the source of ``initial_stability_margin`` / +``out_of_rail_stability_margin`` / ``min_stability_margin`` / +``max_stability_margin``. A positive margin (stability reference behind the center of mass) is the classic passive-stability condition. +.. note:: + **Nonlinear (large-incidence) static stability.** For tabulated + :class:`rocketpy.GenericSurface` coefficients that are nonlinear in + :math:`\alpha`, the stability reference -- the *local neutral point*, the AC + re-linearized at the flown incidence -- migrates with angle of attack, + + .. math:: + + x_\text{NP}(\alpha,\beta,M) = x_\text{cdm} + + c\,L_\text{ref}\,\frac{\partial C_m/\partial\alpha} + {\partial C_L/\partial\alpha}, + + which (unlike the singular force-application CP :math:`-C_m/C_N`) is well + conditioned at every incidence and isolates the stability-relevant part of + the CP travel. It is reconstructed on demand from + :meth:`rocketpy.Rocket.aerodynamic_coefficients_full` by a central finite + difference in :math:`\alpha`. For linear Barrowman aerodynamics it reduces to + the :math:`\alpha=0` AC, so the linear margin already captures it; only + nonlinear tabulated coefficients make it move. Large-incidence stability is + usually read more meaningfully from the dynamic-stability coefficients + (Layer 4). + Layer 4 — Dynamic stability =========================== @@ -292,31 +307,23 @@ Quick reference * - ``Rocket.aerodynamic_center`` (``_yaw``) - :math:`M` - Linear (small-incidence) center of pressure; static-margin reference. - ``cp_position`` is a deprecated alias. - * - ``Rocket.center_of_pressure(α, β, M, Re)`` - - :math:`\alpha,\beta,M,Re` - - Nonlinear CP at finite incidence; combines both planes. - * - ``Rocket.center_of_pressure_over_{alpha,beta}`` - - :math:`\alpha` / :math:`\beta` - - CP travel sweep (design time). + ``cp_position`` is an alias. * - ``Rocket.aerodynamic_coefficients(α, β, M, Re)`` - :math:`\alpha,\beta,M,Re` - Total :math:`C_N`, :math:`C_m` about the center of dry mass. + * - ``Rocket.aerodynamic_coefficients_full(α, β, M, Re)`` + - :math:`\alpha,\beta,M,Re` + - Six signed coefficients; reconstruct the nonlinear CP as + :math:`x_\text{cdm} + c\,L_\text{ref}\,C_m/C_L`. * - ``Rocket.static_margin`` (``_yaw``) - :math:`t` - Linear margin at :math:`M=0` (calibers). * - ``Rocket.stability_margin`` (``_yaw``) - :math:`M, t` - Linear margin vs Mach and time (calibers). - * - ``Rocket.stability_margin_over_{alpha,beta}`` - - :math:`\alpha` / :math:`\beta` - - Nonlinear margin travel sweep (design time). - * - ``Flight.stability_margin`` + * - ``Flight.stability_margin`` (``_yaw``) - :math:`t` - Linear margin along the realized Mach(t) — smooth. - * - ``Flight.realized_stability_margin`` - - :math:`t` - - Nonlinear margin at the realized incidence. * - ``Flight.{pitch,yaw}_natural_frequency`` - :math:`t` - Attitude oscillation natural frequency :math:`\omega_n`. @@ -331,11 +338,9 @@ Visualizing stability ===================== - ``Rocket.plots.stability_margin`` — linear margin vs Mach and time (surface). -- ``Rocket.plots.stability_margin_over_alpha`` / ``_over_beta`` — nonlinear - margin travel with incidence (yaw sweep shown when non-axisymmetric). - ``Rocket.plots.aerodynamic_coefficients`` — :math:`C_N`, :math:`C_m` vs :math:`\alpha`; ``drag_curves`` for :math:`C_D` vs Mach. -- ``Flight.plots.stability_and_control_data`` — linear and realized margin vs +- ``Flight.plots.stability_and_control_data`` — linear margin (pitch and yaw) vs time, plus the FFT frequency response. - ``Flight.plots.dynamic_stability_data`` — natural frequency and damping ratio vs time (pitch and yaw). @@ -385,8 +390,10 @@ generic coefficient path as every other surface. The independent :math:`\alpha,\ \beta` decomposition of the linear model coincides with the classical single-plane Barrowman projection to first order and diverges only at large combined angle of attack, where the - underlying linear coefficients are themselves no longer valid; the nonlinear - :meth:`rocketpy.Rocket.center_of_pressure` captures that regime. + underlying linear coefficients are themselves no longer valid; that regime is + captured by tabulated :class:`rocketpy.GenericSurface` coefficients, from + which the local neutral point and the nonlinear CP can be reconstructed via + :meth:`rocketpy.Rocket.aerodynamic_coefficients_full`. References ========== diff --git a/docs/user/rocket/generic_surface.rst b/docs/user/rocket/generic_surface.rst index 997f5a178..bf9bdcfd0 100644 --- a/docs/user/rocket/generic_surface.rst +++ b/docs/user/rocket/generic_surface.rst @@ -199,9 +199,25 @@ The coefficients are all functions of: - Side slip angle (:math:`\beta`) in radians. - Mach number (:math:`Ma`). - Reynolds number (:math:`Re`). -- Pitch rate (:math:`q`) in radians per second. -- Yaw rate (:math:`r`) in radians per second. -- Roll rate (:math:`p`) in radians per second. +- Pitch rate (:math:`q^{*}`), non-dimensional (reduced). +- Yaw rate (:math:`r^{*}`), non-dimensional (reduced). +- Roll rate (:math:`p^{*}`), non-dimensional (reduced). + +.. important:: + The angular rates are the conventional **non-dimensional reduced rates**, not + the raw body rates in rad/s: + + .. math:: + q^{*} = \frac{q \, L_{ref}}{2 V}, \quad + r^{*} = \frac{r \, L_{ref}}{2 V}, \quad + p^{*} = \frac{p \, L_{ref}}{2 V} + + where :math:`L_{ref}` is the surface reference length and :math:`V` the + freestream speed. This matches how published and tool-generated aerotables + (Missile DATCOM, OpenVSP, CFD/wind-tunnel sweeps) tabulate rate derivatives, + so such tables can be used directly. RocketPy non-dimensionalizes the body + rates internally before evaluating the coefficients (the factor is 0 at zero + airspeed). Define your tables against the reduced rates. .. math:: \begin{aligned} diff --git a/rocketpy/plots/flight_plots.py b/rocketpy/plots/flight_plots.py index f162f268b..99a528fb7 100644 --- a/rocketpy/plots/flight_plots.py +++ b/rocketpy/plots/flight_plots.py @@ -1277,12 +1277,6 @@ def stability_and_control_data(self, *, filename=None): # pylint: disable=too-m self.flight.stability_margin_yaw[:, 1], label="Linear yaw", ) - ax1.plot( - self.flight.realized_stability_margin[:, 0], - self.flight.realized_stability_margin[:, 1], - label="Realized (nonlinear CP)", - linestyle="--", - ) ax1.set_title("Stability Margin") ax1.set_xlabel("Time (s)") ax1.set_ylabel("Stability Margin (c)") diff --git a/rocketpy/plots/rocket_plots.py b/rocketpy/plots/rocket_plots.py index 95a746151..02869ece6 100644 --- a/rocketpy/plots/rocket_plots.py +++ b/rocketpy/plots/rocket_plots.py @@ -775,25 +775,37 @@ def _draw_center_of_mass_and_pressure(self, ax, plane="xz"): ax.scatter(cp, 0, label="Center of Pressure", color="red", s=10, zorder=10) def _center_of_pressure_range(self, plane, max_angle=np.deg2rad(15), samples=31): - """Min and max center-of-pressure position over an incidence sweep. + """Min and max nonlinear center-of-pressure position over an incidence + sweep. Sweeps the angle of attack (xz plane) or sideslip (yz plane) from 0 to - ``max_angle`` using the nonlinear :meth:`Rocket.center_of_pressure` and - returns the extent of the resulting center-of-pressure travel. + ``max_angle`` and reconstructs the nonlinear center of pressure -- + ``x_cdm + csys * d * Cm / CN`` -- from the rocket aerodynamic + coefficients (:meth:`Rocket.aerodynamic_coefficients_full`), returning + the extent of its travel. The center of pressure is singular at zero + incidence (``CN -> 0``); those samples are skipped. """ + rocket = self.rocket + csys = rocket._csys + diameter = 2 * rocket.radius + cdm = rocket.center_of_dry_mass_position angles = np.linspace(0, max_angle, samples) - if plane == "yz": - positions = np.array( - [self.rocket.center_of_pressure(0.0, b, 0.0) for b in angles] - ) - else: - positions = np.array( - [self.rocket.center_of_pressure(a, 0.0, 0.0) for a in angles] - ) - positions = positions[np.isfinite(positions)] + positions = [] + for angle in angles: + if plane == "yz": + coeffs = rocket.aerodynamic_coefficients_full(0.0, angle, 0.0) + force, moment = coeffs["cQ"], coeffs["cn"] + else: + coeffs = rocket.aerodynamic_coefficients_full(angle, 0.0, 0.0) + force, moment = coeffs["cL"], coeffs["cm"] + if force == 0: + continue + position = cdm + csys * diameter * moment / force + if np.isfinite(position): + positions.append(position) if len(positions) == 0: return (0.0, 0.0) - return (float(positions.min()), float(positions.max())) + return (float(min(positions)), float(max(positions))) def _draw_sensors(self, ax, sensors, plane): """Draw the sensor as a small thick line at the position of the sensor, diff --git a/rocketpy/rocket/aero_surface/controllable_generic_surface.py b/rocketpy/rocket/aero_surface/controllable_generic_surface.py index 049ba6206..793c49936 100644 --- a/rocketpy/rocket/aero_surface/controllable_generic_surface.py +++ b/rocketpy/rocket/aero_surface/controllable_generic_surface.py @@ -22,6 +22,35 @@ class ControllableGenericSurface(GenericSurface): Current value of each control variable (defaults to 0). """ + # TODO: deflection-dependent static-margin diagnostics. + # + # The in-flight dynamics are correct: the deflection feeds the coefficient + # functions live every step (see ``_coefficient_arguments``), and the surface + # never physically moves, so its force-application point / ``cp_to_cdm`` cache + # cannot go stale (unlike an individual fin's cant angle, which IS a physical + # reconfiguration and is refreshed via ``Rocket.refresh_controlled_components``). + # + # The gap is diagnostic-only. The derived ``center_of_pressure_z`` / + # ``aerodynamic_center`` come from ``cm_alpha = d(cm)/d(alpha)`` evaluated ONCE + # (in ``_set_derived_cp_accessors``) with the control variables frozen at their + # value at construction (0). So if ``cm`` couples alpha and a control axis + # (e.g. an ``alpha * deflection`` term), the reported ``static_margin`` is + # pinned to the zero-deflection configuration and does not track ``set_control``. + # It also is not a single well-defined number: the static margin of a deflected + # control surface is inherently a function of the control input. + # + # To address this properly (not a correctness fix, defer until there is a real + # need), likely some combination of: + # - an ``initial_deflection`` (per-control) argument in ``__init__`` so the + # derived cp accessors are built about a chosen reference deflection rather + # than always 0; + # - re-deriving the cp accessors when the deflection changes -- reuse the + # fin mechanism: bump ``_geometry_version`` in ``set_control`` and have + # ``Rocket.refresh_controlled_components`` re-run the derived-cp step; + # - dedicated stability plots/prints that sweep the static margin (and cp) + # OVER the control-deflection range, since a single scalar margin is the + # wrong abstraction for a controllable surface. + def __init__( self, reference_area, @@ -126,7 +155,9 @@ def get_control(self, name): """Return the current value of a control variable.""" return self.control_state[name] - def to_dict(self, include_outputs=False): # pylint: disable=unused-argument + def to_dict( # pylint: disable=unused-argument + self, include_outputs=False, **kwargs + ): return { "reference_area": self.reference_area, "reference_length": self.reference_length, diff --git a/rocketpy/rocket/aero_surface/fins/elliptical_fin.py b/rocketpy/rocket/aero_surface/fins/elliptical_fin.py index f809bca29..249a8cf70 100644 --- a/rocketpy/rocket/aero_surface/fins/elliptical_fin.py +++ b/rocketpy/rocket/aero_surface/fins/elliptical_fin.py @@ -175,8 +175,8 @@ def evaluate_center_of_pressure(self): self.cpz = cpz self.cp = (self.cpx, self.cpy, self.cpz) - def to_dict(self, include_outputs=False): - data = super().to_dict(include_outputs=include_outputs) + def to_dict(self, include_outputs=False, **kwargs): + data = super().to_dict(include_outputs=include_outputs, **kwargs) data.update(self.geometry.get_data(include_outputs=include_outputs)) return data diff --git a/rocketpy/rocket/aero_surface/fins/free_form_fin.py b/rocketpy/rocket/aero_surface/fins/free_form_fin.py index 5099d322b..bf6565010 100644 --- a/rocketpy/rocket/aero_surface/fins/free_form_fin.py +++ b/rocketpy/rocket/aero_surface/fins/free_form_fin.py @@ -172,8 +172,8 @@ def evaluate_center_of_pressure(self): def shape_points(self): return self.geometry.shape_points - def to_dict(self, include_outputs=False): - data = super().to_dict(include_outputs=include_outputs) + def to_dict(self, include_outputs=False, **kwargs): + data = super().to_dict(include_outputs=include_outputs, **kwargs) data.update(self.geometry.get_data(include_outputs=include_outputs)) return data diff --git a/rocketpy/rocket/aero_surface/fins/trapezoidal_fin.py b/rocketpy/rocket/aero_surface/fins/trapezoidal_fin.py index f6bf1a7cd..49e594ec1 100644 --- a/rocketpy/rocket/aero_surface/fins/trapezoidal_fin.py +++ b/rocketpy/rocket/aero_surface/fins/trapezoidal_fin.py @@ -219,8 +219,8 @@ def evaluate_center_of_pressure(self): self.cpz = cpz self.cp = (self.cpx, self.cpy, self.cpz) - def to_dict(self, include_outputs=False): - data = super().to_dict(include_outputs=include_outputs) + def to_dict(self, include_outputs=False, **kwargs): + data = super().to_dict(include_outputs=include_outputs, **kwargs) data.update(self.geometry.get_data(include_outputs=include_outputs)) return data diff --git a/rocketpy/rocket/rocket.py b/rocketpy/rocket/rocket.py index f8b4fc0f1..48ad2fa85 100644 --- a/rocketpy/rocket/rocket.py +++ b/rocketpy/rocket/rocket.py @@ -136,11 +136,9 @@ class Rocket: Rocket.aerodynamic_center : Function Function of Mach number expressing the rocket's aerodynamic center (the linearized, small-incidence center of pressure) position relative - to the user defined rocket reference system. The nonlinear center of - pressure at a finite angle of attack is :meth:`Rocket.center_of_pressure`. - ``Rocket.cp_position`` is a deprecated alias for this attribute. - See :doc:`Positions and Coordinate Systems ` - for more information. + to the user defined rocket reference system. ``Rocket.cp_position`` is an + alias for this attribute. See :doc:`Positions and Coordinate Systems + ` for more information. Rocket.stability_margin : Function Stability margin of the rocket, in calibers, as a function of mach number and time. Stability margin is defined as the distance between @@ -452,6 +450,9 @@ def __init__( # pylint: disable=too-many-statements # the motor have been added. self._cp_outdated = True self._margin_outdated = True + # One-shot guard for the non-axisymmetric advisory (see + # ``evaluate_center_of_pressure``); warned at most once per rocket. + self._axisymmetry_warned = False # Initialize plots and prints object self.prints = _RocketPrints(self) @@ -726,9 +727,10 @@ def evaluate_center_of_pressure(self): The aerodynamic center is the linearized (small-incidence, :math:`\\alpha=\\beta=0`) center of pressure: the normal-force-slope- weighted average of every aerodynamic surface's location. It is the - well-conditioned reference used by the static and stability margins and - is distinct from :meth:`center_of_pressure`, which is the *nonlinear* - center of pressure at a finite angle of attack/sideslip. + well-conditioned reference used by the static and stability margins. The + nonlinear center of pressure at a finite angle of attack/sideslip is a + separate, singular quantity (``x_cdm + csys * d * Cm / CN``) that can be + reconstructed from :meth:`aerodynamic_coefficients_full` when needed. It is computed independently for the **pitch** plane (``aerodynamic_center``, from the normal-force/pitch-moment slopes) and @@ -784,6 +786,25 @@ def evaluate_center_of_pressure(self): if self._total_side_coeff_der.get_value(0) != 0: self._aerodynamic_center_yaw /= self._total_side_coeff_der + # One-shot non-axisymmetry advisory. Both plane centers are already built + # here, so detection costs only a Mach sweep -- no extra evaluation and no + # per-surface-add repetition. ``_cp_outdated`` was cleared at the top, so + # reading ``is_axisymmetric`` (which reads the centers) does not recurse. + # Emitted at most once per rocket, when the scalar pitch-plane margins + # first become potentially misleading. + if not self._axisymmetry_warned and not self.is_axisymmetric: + self._axisymmetry_warned = True + max_diff = self._cp_plane_max_difference() + warnings.warn( + "Pitch- and yaw-plane aerodynamic centers differ " + f"(max difference ~{max_diff:.4g} m): the rocket is not " + "axisymmetric. 'aerodynamic_center', 'static_margin' and " + "'stability_margin' describe the PITCH plane; use " + "'aerodynamic_center_yaw', 'static_margin_yaw' and " + "'stability_margin_yaw' for the yaw plane.", + stacklevel=2, + ) + return self._aerodynamic_center def _cp_plane_max_difference(self): @@ -836,119 +857,20 @@ def is_axisymmetric(self): # Tolerance relative to the rocket diameter (caliber-scale). return self._cp_plane_max_difference() <= 1e-6 * (2 * self.radius) - def _warn_if_not_axisymmetric(self): - """Warn, at surface-add time, when the rocket is non-axisymmetric so the - user knows the scalar ``static_margin``/``stability_margin`` describe the - pitch plane only. Short-circuits with no computation for rockets built - solely from axisymmetric-by-construction surfaces (the Barrowman set); - only a generic surface or individual fin triggers the Mach sweep.""" - if not self.aerodynamic_surfaces or self.is_axisymmetric: - return - max_diff = self._cp_plane_max_difference() - warnings.warn( - "Pitch- and yaw-plane aerodynamic centers differ " - f"(max difference ~{max_diff:.4g} m): the rocket is not " - "axisymmetric. 'aerodynamic_center', 'static_margin' and " - "'stability_margin' describe the PITCH plane; use " - "'aerodynamic_center_yaw', 'static_margin_yaw' and " - "'stability_margin_yaw' for the yaw plane.", - stacklevel=3, - ) - @property def cp_position(self): - """Deprecated alias for :attr:`aerodynamic_center` (the linearized, - Mach-dependent center of pressure / aerodynamic center).""" - warnings.warn( - "'cp_position' is deprecated and will be removed in a future " - "release; use 'aerodynamic_center' (the linearized center of " - "pressure) instead. For the nonlinear center of pressure at a given " - "angle of attack use 'center_of_pressure(alpha, beta, mach)'.", - DeprecationWarning, - stacklevel=2, - ) - return self.aerodynamic_center - - def _aerodynamic_center_limit(self, alpha, beta, mach): - """Small-incidence limit of :meth:`center_of_pressure`: the linearized - aerodynamic center, blended between the pitch and yaw planes by the - squared normal-force contribution of each, so the nonlinear center of - pressure stays continuous in the ``(alpha, beta)`` direction as the - incidence goes to zero (pure pitch -> pitch AC, pure sideslip -> yaw AC). + """Alias for :attr:`aerodynamic_center`. + + Historically named "center of pressure", this is the linearized, + Mach-dependent aerodynamic center -- the slope-weighted (Barrowman) + quantity the rocketry community conventionally calls the CP, and the + well-conditioned reference used by the static and stability margins. + The genuine, force-application center of pressure at a finite incidence + is ``x_cdm + csys * d * Cm / CN`` and can be reconstructed on demand from + :meth:`aerodynamic_coefficients_full`; it is intentionally not exposed as + a method because it is singular at zero normal force (zero incidence). """ - weight_pitch = (self.total_lift_coeff_der.get_value_opt(mach) * alpha) ** 2 - weight_yaw = (self.total_side_coeff_der.get_value_opt(mach) * beta) ** 2 - pitch = self.aerodynamic_center.get_value_opt(mach) - if weight_pitch + weight_yaw == 0: - return pitch - yaw = self.aerodynamic_center_yaw.get_value_opt(mach) - return (pitch * weight_pitch + yaw * weight_yaw) / (weight_pitch + weight_yaw) - - def center_of_pressure(self, alpha, beta, mach, reynolds=0.0): - """Nonlinear center of pressure axial position, as a function of the - aerodynamic state. - - Unlike :attr:`aerodynamic_center` (the linearized center of pressure, a - function of Mach alone, valid only near zero incidence), this aggregates - the *actual* force and moment of every aerodynamic surface at the - requested angle of attack ``alpha``, sideslip ``beta`` and Mach number, - then locates the axial point about which the resultant transverse - aerodynamic force produces no moment: - ``z_cp = (M2*R1 - M1*R2) / (R1**2 + R2**2)`` (about the center of dry - mass, from ``M = r x F``). - - Because the resultant force is evaluated at the actual *combined* - incidence, a single axial location captures both the pitch and the yaw - plane -- the ``aerodynamic_center``/``aerodynamic_center_yaw`` split is - only needed for the linearized slopes, which must pick a perturbation - axis. As ``alpha, beta -> 0`` the normal force vanishes and the location - becomes a ``0/0`` limit; there the linearized aerodynamic center - (:attr:`aerodynamic_center`) is returned, which the nonlinear value - converges to. - - Parameters - ---------- - alpha : float - Angle of attack, in radians (pitch plane, body aerodynamic frame). - beta : float - Sideslip angle, in radians (yaw plane, body aerodynamic frame). - mach : float - Free-stream Mach number. - reynolds : float, optional - Rocket-level Reynolds number (based on the rocket diameter). Each - surface's Reynolds number is scaled to its own reference length. - Defaults to ``0`` (vanishing-Reynolds limit, matching the linearized - ``aerodynamic_center`` convention). - - Returns - ------- - float - Center of pressure position along the rocket axis, in the rocket - coordinate system (m). See :doc:`Positions and Coordinate Systems - `. - """ - # Below ~1 deg total incidence the normal force is too small for the - # moment/normal-force ratio to be well conditioned (a 0/0 limit at the - # origin, finite-precision noise just above it). Return the linearized - # aerodynamic center, which the nonlinear value converges to, so the - # result is continuous and never spikes as the rocket oscillates through - # zero incidence. - if alpha**2 + beta**2 < math.radians(1.0) ** 2 or ( - len(self.aerodynamic_surfaces) == 0 - ): - return self._aerodynamic_center_limit(alpha, beta, mach) - - total_x, total_y, _, moment_x, moment_y, _, _ = ( - self._aerodynamic_forces_and_moments(alpha, beta, mach, reynolds) - ) - - normal_force_sq = total_x**2 + total_y**2 - if normal_force_sq == 0: - return self._aerodynamic_center_limit(alpha, beta, mach) - # Axial offset (body frame) from the center of dry mass to the line of - # action of the resultant transverse force. - cp_offset = (moment_y * total_x - moment_x * total_y) / normal_force_sq - return self.center_of_dry_mass_position + self._csys * cp_offset + return self.aerodynamic_center def _aerodynamic_forces_and_moments(self, alpha, beta, mach, reynolds=0.0): """Total body-frame aerodynamic force ``(R1, R2, R3)`` and moment @@ -1625,15 +1547,13 @@ def add_surfaces(self, surfaces, positions): self.__add_single_surface(surfaces, positions) # Adding a surface changes both the aerodynamic center and the margins; - # flag them for lazy rebuild on next access (see the properties). + # flag them for lazy rebuild on next access (see the properties). The + # non-axisymmetry advisory is emitted (once) from + # ``evaluate_center_of_pressure`` on that first rebuild, rather than + # eagerly here on every add. self._cp_outdated = True self._margin_outdated = True - # The asymmetry warning is the one piece evaluated eagerly: it is a - # setup-time hint about which margin attributes to trust, and the check - # is free for axisymmetric-by-construction (Barrowman) rockets. - self._warn_if_not_axisymmetric() - def add_vehicle_aerodynamic_surface( self, coefficients, reference_position=None, name="Vehicle Aerodynamics" ): diff --git a/rocketpy/simulation/flight.py b/rocketpy/simulation/flight.py index 781b47c0a..7c631753b 100644 --- a/rocketpy/simulation/flight.py +++ b/rocketpy/simulation/flight.py @@ -2312,9 +2312,7 @@ def stability_margin(self): (:meth:`Rocket.stability_margin`) at the realized flight Mach and time at each instant, capturing the Mach variation of the aerodynamic center together with the center-of-mass shift as propellant burns. It is - well-conditioned and never spikes. For the nonlinear margin that follows - the center of pressure at the actual angle of attack/sideslip, see - :meth:`realized_stability_margin`. + well-conditioned and never spikes. Returns ------- @@ -2344,89 +2342,6 @@ def stability_margin_yaw(self): (t, self.rocket.stability_margin_yaw(m, t)) for t, m in self.mach_number ] - @funcify_method("Time (s)", "Realized Stability Margin (c)", "linear", "zero") - def realized_stability_margin(self): - """Nonlinear (realized) stability margin along the flight, in calibers. - - Co-equal companion to :meth:`stability_margin`: instead of the - aerodynamic center it uses the rocket's *nonlinear* center of pressure - (:meth:`Rocket.center_of_pressure`) at the realized flight state -- the - actual angle of attack, sideslip, Mach and Reynolds at each time step -- - so it reveals how the center of pressure travels with incidence (and, - for non-axisymmetric rockets, combines the pitch and yaw planes at the - actual combined incidence). - - For a non-axisymmetric rocket the margin is **direction-dependent** (the - pitch and yaw planes differ), and during most of the flight the rocket - flies at near-zero incidence, where the *direction* of the residual - incidence vector is numerical noise (slight coning). Reporting the - directional center of pressure there would make the margin swing between - the pitch- and yaw-plane values. To avoid that, the realized value is - blended into the linear margin by how much real incidence there is: at - negligible incidence the result is the linear :meth:`stability_margin`, - and only a genuine disturbance (a few degrees of incidence) reveals the - nonlinear travel. The value also falls back to the linear margin where - the dynamic pressure is negligible (rail, rest, apogee). - - Returns - ------- - stability : rocketpy.Function - Realized stability margin in calibers as a function of time. - """ - csys = self.rocket._csys - diameter = 2 * self.rocket.radius - time = self.time - - # These are all tabulated at exactly ``self.time`` (their source is - # ``column_stack([self.time, values])`` or same-grid Function - # arithmetic), so read the values straight from ``.source`` instead of - # re-evaluating per node. ``mach`` is reused by both the realized cp and - # the linear margin below. ``center_of_mass`` is a rocket-level Function - # on a different grid, so it still needs ``get_value_opt(t)``. - alpha = self.partial_angle_of_attack.source[:, 1] - beta = self.angle_of_sideslip.source[:, 1] - mach = self.mach_number.source[:, 1] - reynolds = self.reynolds_number.source[:, 1] - - center_of_pressure = np.array( - [ - self.rocket.center_of_pressure( - np.deg2rad(a), - np.deg2rad(b), - m, - re, - ) - for a, b, m, re in zip(alpha, beta, mach, reynolds) - ] - ) - center_of_mass = np.array( - [self.rocket.center_of_mass.get_value_opt(t) for t in time] - ) - margin_realized = (center_of_mass - center_of_pressure) / diameter * csys - - margin_model = np.array( - [ - self.rocket.stability_margin.get_value_opt(m, t) - for m, t in zip(mach, time) - ] - ) - - # Weight the (direction-dependent) realized value by how much real - # incidence there is, with a smoothstep ramp up to ~2 deg, so the - # near-zero-incidence direction noise collapses to the linear margin. - incidence = np.hypot(alpha, beta) - weight = np.clip(incidence / 2.0, 0.0, 1.0) - weight = weight**2 * (3.0 - 2.0 * weight) - margin_blended = (1.0 - weight) * margin_model + weight * margin_realized - - # Fall back fully to the linear margin where the rocket is barely moving - # (dynamic pressure below 1% of its flight-wide peak: rail, rest, apogee). - dynamic_pressure = self.dynamic_pressure.source[:, 1] - meaningful = dynamic_pressure > 0.01 * dynamic_pressure.max() - margin = np.where(meaningful, margin_blended, margin_model) - - return np.column_stack((time, margin)) - # Dynamic stability def _lateral_inertia(self, dry_lateral_inertia, motor_lateral_inertia): """Lateral moment of inertia about the instantaneous center of mass, as diff --git a/tests/unit/rocket/aero_surface/test_aero_coefficient.py b/tests/unit/rocket/aero_surface/test_aero_coefficient.py new file mode 100644 index 000000000..ed568773a --- /dev/null +++ b/tests/unit/rocket/aero_surface/test_aero_coefficient.py @@ -0,0 +1,226 @@ +"""Unit tests for the AeroCoefficient minimal-dimension coefficient store.""" + +import pytest + +from rocketpy import Function +from rocketpy.rocket.aero_surface.aero_coefficient import ( + AeroCoefficient, + build_independent_vars, +) + +IV = ["alpha", "beta", "mach", "reynolds", "pitch_rate", "yaw_rate", "roll_rate"] + + +# -- Construction & evaluation ------------------------------------------------ + + +def test_constant_coefficient_is_zero_flagged(): + zero = AeroCoefficient(0, (), name="cD") + assert zero.is_zero is True + assert zero.is_zero_coefficient is True + assert zero(0.1, 0.2, 0.3, 0, 0, 0, 0) == 0.0 + + const = AeroCoefficient(0.7, (), name="cD") + assert const.is_zero is False + assert const(1, 2, 3, 4, 5, 6, 7) == 0.7 + assert const.get_value_opt(1, 2, 3, 4, 5, 6, 7) == 0.7 + + +def test_call_is_get_value_opt(): + # __call__ is aliased to get_value_opt; both must behave identically. + assert AeroCoefficient.__call__ is AeroCoefficient.get_value_opt + + +def test_mach_only_coefficient_maps_arguments(): + coeff = AeroCoefficient(lambda mach: 2 * mach, ("mach",), name="cL_alpha") + assert coeff.depends_on == ("mach",) + # Only the mach argument (index 2) should be used. + assert coeff(99, 99, 0.3, 99, 99, 99, 99) == pytest.approx(0.6) + assert coeff.get_value_opt(99, 99, 0.3, 99, 99, 99, 99) == pytest.approx(0.6) + + +def test_function_source_stored_directly(): + f = Function(lambda mach: mach**2, "mach", "cD") + coeff = AeroCoefficient(f, ("mach",), name="cD") + assert coeff.function is f + assert coeff(0, 0, 0.5, 0, 0, 0, 0) == pytest.approx(0.25) + + +def test_depends_on_preserves_source_argument_order(): + # depends_on order must match the source's positional order, even when it + # differs from the independent-variable order (e.g. shuffled CSV columns). + coeff = AeroCoefficient( + lambda mach, alpha: 10 * mach + alpha, ("mach", "alpha"), name="cL" + ) + # full args: alpha=1 (idx0), mach=2 (idx2) -> source(mach=2, alpha=1) = 21 + assert coeff(1, 0, 2, 0, 0, 0, 0) == pytest.approx(21) + + +def test_unknown_dependency_raises(): + with pytest.raises(ValueError, match="unknown variable"): + AeroCoefficient(lambda x: x, ("bogus",), name="cL") + + +def test_dom_dim_matches_full_arity(): + coeff = AeroCoefficient(0, (), name="cD") + assert coeff.__dom_dim__ == len(IV) + + +def test_repr_constant_and_function(): + assert "0.5" in repr(AeroCoefficient(0.5, (), name="cD")) + function_repr = repr(AeroCoefficient(lambda mach: mach, ("mach",), name="cL")) + assert "depends_on" in function_repr and "mach" in function_repr + + +# -- Independent-variable axes (unsteady / control) --------------------------- + + +def test_build_independent_vars_base_unsteady_and_controls(): + assert build_independent_vars() == IV + assert build_independent_vars(unsteady_aero=True) == IV + ["alpha_dot", "beta_dot"] + assert build_independent_vars(control_variables=("defl",)) == IV + ["defl"] + + +def test_unsteady_aero_extends_independent_vars(): + coeff = AeroCoefficient( + lambda alpha_dot: alpha_dot, ("alpha_dot",), unsteady_aero=True, name="cL" + ) + assert coeff.independent_vars == tuple(IV + ["alpha_dot", "beta_dot"]) + assert coeff.__dom_dim__ == 9 + # alpha_dot is the 8th argument (index 7). + assert coeff(0, 0, 0, 0, 0, 0, 0, 1.5, 0) == pytest.approx(1.5) + + +def test_control_variable_axis_is_appended(): + coeff = AeroCoefficient( + lambda deflection: 2 * deflection, + ("deflection",), + control_variables=("deflection",), + name="cL", + ) + assert coeff.independent_vars[-1] == "deflection" + assert coeff(0, 0, 0, 0, 0, 0, 0, 4) == pytest.approx(8) + + +# -- constructor inference: scalar ------------------------------------------------------- + + +def test_from_input_scalar(): + coeff = AeroCoefficient(0, name="cm") + assert coeff.is_zero is True + + +def test_from_input_non_numeric_raises(): + with pytest.raises(TypeError, match="must be a number"): + AeroCoefficient(object(), name="cD") + + +# -- constructor inference: callable ----------------------------------------------------- + + +def test_from_input_full_arity_callable(): + coeff = AeroCoefficient(lambda a, b, m, r, p, q, rr: a + m, name="cL") + assert coeff.depends_on == tuple(IV) + assert coeff(0.1, 0, 0.3, 0, 0, 0, 0) == pytest.approx(0.4) + + +def test_from_input_named_subset_callable(): + coeff = AeroCoefficient(lambda alpha, mach: alpha * mach, name="cL") + assert coeff.depends_on == ("alpha", "mach") + assert coeff(2, 0, 3, 0, 0, 0, 0) == pytest.approx(6) + + +def test_from_input_rejects_unmappable_callable(): + with pytest.raises(ValueError, match="callable must accept"): + AeroCoefficient(lambda x, y, z: x, name="cL") + + +# -- constructor inference: Function ----------------------------------------------------- + + +def test_from_input_full_dim_function(): + f = Function(lambda a, b, m, r, p, q, rr: a + m, IV, "cL") + coeff = AeroCoefficient(f, name="cL") + assert coeff.depends_on == tuple(IV) + assert coeff(0.1, 0, 0.3, 0, 0, 0, 0) == pytest.approx(0.4) + + +def test_from_input_1d_function_infers_mach(): + f = Function(lambda mach: mach**2, "Mach", "cD") + coeff = AeroCoefficient(f, name="cD") + assert coeff.depends_on == ("mach",) + assert coeff(0, 0, 0.5, 0, 0, 0, 0) == pytest.approx(0.25) + + +def test_from_input_function_with_bad_dimension_raises(): + f = Function(lambda a, b: a + b, ["alpha", "beta"], "cL") + with pytest.raises(ValueError, match="must have 7 input arguments"): + AeroCoefficient(f, name="cL") + + +# -- constructor inference: CSV path ----------------------------------------------------- + + +def test_from_input_csv_loads_at_minimal_dimension(tmp_path): + csv_file = tmp_path / "coeffs.csv" + csv_file.write_text("mach,cD\n0.0,0.0\n1.0,3.0\n2.0,6.0\n") + + coeff = AeroCoefficient(str(csv_file), name="cD") + assert coeff.depends_on == ("mach",) + assert coeff(0, 0, 2, 0, 0, 0, 0) == pytest.approx(6) + + +def test_load_csv_rejects_unknown_column(tmp_path): + csv_file = tmp_path / "coeffs.csv" + csv_file.write_text("bogus,cD\n0.0,0.0\n1.0,3.0\n") + + with pytest.raises(ValueError, match="Invalid independent variable"): + AeroCoefficient(str(csv_file), name="cD") + + +# -- constructor inference: AeroCoefficient round trip ----------------------------------- + + +def test_roundtrip_callable_passthrough(): + original = AeroCoefficient(lambda alpha, mach: alpha + mach, name="cL") + rebuilt = AeroCoefficient(original, name="cL") + assert rebuilt.depends_on == original.depends_on + assert rebuilt(0.5, 0, 0.3, 0, 0, 0, 0) == pytest.approx( + original(0.5, 0, 0.3, 0, 0, 0, 0) + ) + + +def test_roundtrip_constant_passthrough(): + original = AeroCoefficient(0.9, name="cD") + rebuilt = AeroCoefficient(original, name="cD") + assert rebuilt._constant == pytest.approx(0.9) + assert rebuilt(1, 2, 3, 4, 5, 6, 7) == pytest.approx(0.9) + + +def test_to_dict_from_dict_preserves_axes(): + original = AeroCoefficient( + lambda deflection: deflection, + ("deflection",), + unsteady_aero=True, + control_variables=("deflection",), + name="cL", + ) + rebuilt = AeroCoefficient.from_dict(original.to_dict()) + assert rebuilt.unsteady_aero is True + assert rebuilt.control_variables == ("deflection",) + assert rebuilt.independent_vars == original.independent_vars + + +# -- _infer_single_var fallbacks ---------------------------------------------- + + +def test_infer_single_var_unmatched_label_defaults_to_first(): + f = Function(lambda gamma: gamma, "gamma", "cD") + assert AeroCoefficient._infer_single_var(f, IV) == IV[0] + + +def test_infer_single_var_missing_inputs_defaults_to_first(): + class NoInputs: + pass + + assert AeroCoefficient._infer_single_var(NoInputs(), IV) == IV[0] diff --git a/tests/unit/rocket/aero_surface/test_barrowman_generic_equivalence.py b/tests/unit/rocket/aero_surface/test_barrowman_generic_equivalence.py new file mode 100644 index 000000000..f9828d205 --- /dev/null +++ b/tests/unit/rocket/aero_surface/test_barrowman_generic_equivalence.py @@ -0,0 +1,163 @@ +"""Regression tests for the GenericSurface-rooted aerodynamic hierarchy. + +After the refactor, every aerodynamic surface (Barrowman or generic) is +described by the generic coefficient model and exposes the diagnostic accessors +``lift_coefficient_derivative`` and ``center_of_pressure_z`` used by the rocket's +center-of-pressure / stability-margin computation. These tests pin the +properties that the refactor is meant to guarantee. +""" + +import warnings + +import numpy as np +import pytest + +from rocketpy import LinearGenericSurface, NoseCone, Tail, TrapezoidalFins + + +def test_barrowman_derived_cp_matches_geometric_cp(): + """The derived ``center_of_pressure_z`` must reproduce the geometric cp of + each Barrowman surface (the moment is carried by ``cm`` but the diagnostic + must recover the original location).""" + nose = NoseCone( + length=0.55829, kind="vonkarman", base_radius=0.0635, rocket_radius=0.0635 + ) + tail = Tail( + top_radius=0.0635, bottom_radius=0.0435, length=0.060, rocket_radius=0.0635 + ) + fins = TrapezoidalFins( + n=4, span=0.100, root_chord=0.120, tip_chord=0.040, rocket_radius=0.0635 + ) + + for surface in (nose, tail, fins): + for mach in (0.0, 0.5, 0.9): + assert ( + pytest.approx( + surface.center_of_pressure_z.get_value_opt(mach), rel=1e-6, abs=1e-9 + ) + == surface.cpz + ) + # The normal-force slope diagnostic must equal the Barrowman clalpha. + assert pytest.approx( + nose.lift_coefficient_derivative.get_value_opt(0.0) + ) == nose.clalpha.get_value_opt(0.0) + + +def test_generic_surface_contributes_to_static_margin(calisto_motorless): + """A generic surface must now contribute to the rocket center of pressure + (previously generic surfaces were skipped, breaking stability margin).""" + rocket = calisto_motorless + rocket.add_nose(length=0.55829, kind="vonkarman", position=1.278) + + cp_without_generic = rocket.aerodynamic_center.get_value_opt(0.2) + + # A lifting generic surface placed aft should move the cp aft (more stable). + generic = LinearGenericSurface( + reference_area=rocket.area, + reference_length=2 * rocket.radius, + coefficients={ + "cL_alpha": lambda a, b, m, re, p, q, r: 2.0, + "cm_alpha": lambda a, b, m, re, p, q, r: -1.0, + }, + name="generic_fins", + ) + rocket.add_surfaces(generic, positions=-1.0) + + cp_with_generic = rocket.aerodynamic_center.get_value_opt(0.2) + assert cp_with_generic != pytest.approx(cp_without_generic) + assert np.isfinite(cp_with_generic) + + +def test_zero_lift_surface_does_not_break_cp(calisto_motorless): + """A surface with no normal-force slope must drop out of the lift-weighted + cp average without producing NaNs.""" + rocket = calisto_motorless + rocket.add_nose(length=0.55829, kind="vonkarman", position=1.278) + cp_reference = rocket.aerodynamic_center.get_value_opt(0.2) + + drag_only = LinearGenericSurface( + reference_area=rocket.area, + reference_length=2 * rocket.radius, + coefficients={"cD_0": lambda a, b, m, re, p, q, r: 0.5}, + name="drag_only", + ) + rocket.add_surfaces(drag_only, positions=-1.0) + + cp_after = rocket.aerodynamic_center.get_value_opt(0.2) + assert np.isfinite(cp_after) + assert cp_after == pytest.approx(cp_reference) + + +def test_axisymmetric_rocket_pitch_equals_yaw_margin(calisto_motorless): + """An axisymmetric rocket must have identical pitch and yaw margins and + must not raise the asymmetry warning.""" + rocket = calisto_motorless + with warnings.catch_warnings(): + warnings.simplefilter("error") # asymmetry warning would fail the test + rocket.add_nose(length=0.55829, kind="vonkarman", position=1.278) + rocket.add_trapezoidal_fins( + n=4, span=0.100, root_chord=0.120, tip_chord=0.040, position=-1.04 + ) + rocket.add_tail( + top_radius=0.0635, bottom_radius=0.0435, length=0.060, position=-1.194 + ) + + for mach in (0.0, 0.5, 0.9): + assert rocket.aerodynamic_center.get_value_opt(mach) == pytest.approx( + rocket.aerodynamic_center_yaw.get_value_opt(mach), abs=1e-9 + ) + assert rocket.static_margin.get_value_opt(0) == pytest.approx( + rocket.static_margin_yaw.get_value_opt(0), abs=1e-9 + ) + + +def test_non_axisymmetric_rocket_splits_margins_and_warns(calisto_motorless): + """A non-axisymmetric generic surface must yield distinct pitch/yaw margins + and raise a warning that the scalar margin describes the pitch plane only. + + The advisory is emitted lazily -- on the first evaluation of the aerodynamic + center, not eagerly at add time -- so adding the surface itself is silent.""" + rocket = calisto_motorless + rocket.add_nose(length=0.55829, kind="vonkarman", position=1.278) + asymmetric = LinearGenericSurface( + reference_area=rocket.area, + reference_length=2 * rocket.radius, + coefficients={ + "cL_alpha": lambda a, b, m, re, p, q, r: 2.0, + "cm_alpha": lambda a, b, m, re, p, q, r: -1.0, + "cQ_beta": lambda a, b, m, re, p, q, r: -2.0, + "cn_beta": lambda a, b, m, re, p, q, r: 2.0, + }, + name="asym", + ) + + rocket.add_surfaces(asymmetric, positions=-1.0) + + # Warning fires once, on the first aerodynamic-center evaluation. + with pytest.warns(UserWarning, match="not\\s+axisymmetric"): + ac_pitch = rocket.aerodynamic_center.get_value_opt(0.2) + + assert ac_pitch != pytest.approx( + rocket.aerodynamic_center_yaw.get_value_opt(0.2) + ) + assert rocket.static_margin.get_value_opt(0) != pytest.approx( + rocket.static_margin_yaw.get_value_opt(0) + ) + + +def test_barrowman_surface_uses_generic_compute_path(): + """Barrowman surfaces must route through the shared generic + ``compute_forces_and_moments`` (no bespoke override) and apply their force + at the origin (moment carried by the coefficients).""" + from rocketpy.rocket.aero_surface.generic_surface import GenericSurface + + nose = NoseCone( + length=0.55829, kind="vonkarman", base_radius=0.0635, rocket_radius=0.0635 + ) + assert isinstance(nose, GenericSurface) + # Force is applied at the origin; the cp offset lives in cm/cn. + assert tuple(nose.force_application_point) == (0, 0, 0) + assert ( + nose.compute_forces_and_moments.__func__ + is GenericSurface.compute_forces_and_moments + ) diff --git a/tests/unit/rocket/aero_surface/test_controllable_generic_surface.py b/tests/unit/rocket/aero_surface/test_controllable_generic_surface.py new file mode 100644 index 000000000..837803648 --- /dev/null +++ b/tests/unit/rocket/aero_surface/test_controllable_generic_surface.py @@ -0,0 +1,101 @@ +"""Unit tests for ControllableGenericSurface and the controllable-surface +controller linkage.""" + +import pytest + +from rocketpy import ControllableGenericSurface, Function, GenericSurface +from rocketpy.mathutils.vector_matrix import Vector + +DENSITY = Function(lambda z: 1.16) +VISCOSITY = Function(lambda z: 1.8e-5) + + +def _moment_at_deflection(surface, deflection, comp="pitch"): + surface.set_control("deflection", deflection) + r1, r2, r3, m1, m2, m3 = surface.compute_forces_and_moments( + Vector([0, 0, -100]), + 100, + 0.29, + 1.16, + Vector([0, 0, 0]), + Vector([0, 0, 0]), + DENSITY, + VISCOSITY, + 100.0, + ) + return {"pitch": m1, "yaw": m2, "roll": m3}[comp] + + +def test_control_variable_extends_independent_vars(): + surface = ControllableGenericSurface( + reference_area=1, reference_length=0.2, coefficients={} + ) + assert surface.independent_vars[:7] == [ + "alpha", + "beta", + "mach", + "reynolds", + "pitch_rate", + "yaw_rate", + "roll_rate", + ] + assert surface.independent_vars[7:] == ["deflection"] + assert surface.control_state == {"deflection": 0.0} + + +def test_deflection_produces_proportional_control_moment(): + surface = ControllableGenericSurface( + reference_area=1, + reference_length=0.2, + coefficients={"cm": lambda a, b, m, re, p, q, r, deflection: 0.5 * deflection}, + ) + m0 = _moment_at_deflection(surface, 0.0) + m1 = _moment_at_deflection(surface, 0.1) + m2 = _moment_at_deflection(surface, 0.2) + assert m0 == pytest.approx(0.0) + assert m2 == pytest.approx(2 * m1) + assert m1 != pytest.approx(0.0) + + +def test_multiple_named_controls(): + surface = ControllableGenericSurface( + reference_area=1, + reference_length=0.2, + coefficients={"cn": lambda a, b, m, re, p, q, r, dp, dy: 0.3 * dy}, + controls=("delta_pitch", "delta_yaw"), + ) + assert surface.independent_vars[7:] == ["delta_pitch", "delta_yaw"] + surface.set_control("delta_yaw", 0.5) + yaw = surface.compute_forces_and_moments( + Vector([0, 0, -100]), + 100, + 0.29, + 1.16, + Vector([0, 0, 0]), + Vector([0, 0, 0]), + DENSITY, + VISCOSITY, + 100.0, + )[4] + assert yaw != pytest.approx(0.0) + + +def test_set_control_unknown_name_raises(): + surface = ControllableGenericSurface( + reference_area=1, reference_length=0.2, coefficients={} + ) + with pytest.raises(KeyError): + surface.set_control("not_a_control", 0.1) + + +def test_plain_generic_surface_default_independent_vars_unchanged(): + surface = GenericSurface(reference_area=1, reference_length=0.2, coefficients={}) + assert surface.independent_vars == [ + "alpha", + "beta", + "mach", + "reynolds", + "pitch_rate", + "yaw_rate", + "roll_rate", + ] diff --git a/tests/unit/rocket/aero_surface/test_generic_surfaces.py b/tests/unit/rocket/aero_surface/test_generic_surfaces.py index c16a1b592..43543a50e 100644 --- a/tests/unit/rocket/aero_surface/test_generic_surfaces.py +++ b/tests/unit/rocket/aero_surface/test_generic_surfaces.py @@ -89,12 +89,10 @@ def test_csv_independent_variables_accept_any_order(tmp_path): coefficients={"cL": str(filename)}, ) - closure = generic_surface.cL.source.__closure__ - csv_function = next( - cell.cell_contents - for cell in closure - if isinstance(cell.cell_contents, Function) - ) + # The coefficient is stored at minimal dimension over its CSV columns, in + # header order; AeroCoefficient maps the full argument tuple onto them. + assert generic_surface.cL.depends_on == ("mach", "alpha") + csv_function = generic_surface.cL.function assert generic_surface.cL(1, 0, 2, 0, 0, 0, 0) == pytest.approx(12) assert csv_function.get_interpolation_method() == "regular_grid" @@ -117,3 +115,32 @@ def test_compute_forces_and_moments(): z=0, ) assert forces_and_moments == (0, 0, 0, 0, 0, 0) + + +def test_angular_rates_are_non_dimensionalized(): + """Coefficients receive the conventional reduced rate q* = q L_ref / (2 V), + not the raw body rate in rad/s.""" + ref_area, ref_length = 2.0, 0.5 + # Roll-moment coefficient that simply returns the roll rate it is given, so + # the resulting roll moment exposes which rate value reached the coefficient. + gs = GenericSurface(ref_area, ref_length, {"cl": lambda roll_rate: roll_rate}) + + rho, speed, raw_roll = 1.2, 10.0, 4.0 + *_, roll_moment = gs.compute_forces_and_moments( + stream_velocity=Vector((0, 0, -speed)), # along centerline -> alpha=beta=0 + stream_speed=speed, + stream_mach=0, + rho=rho, + cp=Vector((0, 0, 0)), + omega=(0, 0, raw_roll), # raw body roll rate p, rad/s + density=Function(1.0), + dynamic_viscosity=Function(1.0), + z=0, + ) + + reduced_roll = raw_roll * ref_length / (2 * speed) + dyn_pressure_area_length = 0.5 * rho * speed**2 * ref_area * ref_length + # The coefficient saw the reduced rate, ... + assert roll_moment == pytest.approx(dyn_pressure_area_length * reduced_roll) + # ... not the raw rad/s rate. + assert roll_moment != pytest.approx(dyn_pressure_area_length * raw_roll) diff --git a/tests/unit/rocket/aero_surface/test_individual_fins.py b/tests/unit/rocket/aero_surface/test_individual_fins.py index d232e0772..6db540a8a 100644 --- a/tests/unit/rocket/aero_surface/test_individual_fins.py +++ b/tests/unit/rocket/aero_surface/test_individual_fins.py @@ -7,7 +7,9 @@ from rocketpy import ( EllipticalFin, + EllipticalFins, FreeFormFin, + FreeFormFins, Rocket, TrapezoidalFin, TrapezoidalFins, @@ -375,16 +377,124 @@ def test_calisto_finset_vs_four_individual_fins_close(): mach_grid = np.linspace(0, 2, 21) # Act - cp_finset = finset_rocket.cp_position(mach_grid) - cp_individual = individual_fins_rocket.cp_position(mach_grid) + cp_finset = finset_rocket.aerodynamic_center(mach_grid) + cp_individual = individual_fins_rocket.aerodynamic_center(mach_grid) clalpha_finset = finset_rocket.total_lift_coeff_der(mach_grid) clalpha_individual = individual_fins_rocket.total_lift_coeff_der(mach_grid) - lift_correction = TrapezoidalFins.fin_num_correction(4) / 4 - clalpha_individual_corrected = np.array(clalpha_individual) * lift_correction - # Assert + # Assert. Each individual fin projects its lift slope onto the pitch plane by + # sin(phi)**2, so an evenly spaced set of 4 sums to fin_num_correction(4) = 2 + # in the plane -- matching the fin set directly, with no extra correction. np.testing.assert_allclose(cp_individual, cp_finset, rtol=1e-6, atol=1e-6) - np.testing.assert_allclose(clalpha_individual_corrected, clalpha_finset) + np.testing.assert_allclose(clalpha_individual, clalpha_finset) + + +@pytest.mark.parametrize( + "fin_cls, geometry", + [ + ( + TrapezoidalFin, + dict(root_chord=0.120, tip_chord=0.040, span=0.100, rocket_radius=0.0635), + ), + (EllipticalFin, dict(root_chord=0.120, span=0.100, rocket_radius=0.0635)), + ( + FreeFormFin, + dict(shape_points=[(0, 0), (0.06, 0.1), (0.12, 0.0)], rocket_radius=0.0635), + ), + ], +) +def test_canted_individual_fin_builds_and_places(fin_cls, geometry): + """A canted individual fin of any shape must build its body<->fin rotation + matrices at construction, so it can be placed on a rocket. Regression test + for a crash where non-trapezoidal individual fins lacked + ``_rotation_fin_to_body_uncanted`` and failed in + ``_compute_leading_edge_position``.""" + fin = fin_cls(angular_position=30, cant_angle=2.0, **geometry) + assert hasattr(fin, "_rotation_fin_to_body_uncanted") + position = fin._compute_leading_edge_position(-1.168, 1) + assert position is not None + + +@pytest.mark.parametrize( + "fin_cls, geometry", + [ + ( + TrapezoidalFin, + dict(root_chord=0.120, tip_chord=0.040, span=0.100, rocket_radius=0.0635), + ), + (EllipticalFin, dict(root_chord=0.120, span=0.100, rocket_radius=0.0635)), + ( + FreeFormFin, + dict(shape_points=[(0, 0), (0.06, 0.1), (0.12, 0.0)], rocket_radius=0.0635), + ), + ], +) +def test_individual_fin_roll_moment_independent_of_angular_position(fin_cls, geometry): + """A canted individual fin's roll moment must be the same at any angular + position (rotational symmetry about the roll axis). Regression test for a bug + where the fin's center of pressure was not rotated to its azimuth (the + rotation matrix was left as the identity), making the roll moment vary with + angular position.""" + stream_velocity = Vector([0, 0, -1.0]) + omega = Vector([0, 0, 0]) + + roll_moments = [] + for angle in (0, 90, 180, 270): + rocket = Rocket( + radius=0.0635, + mass=14.426, + inertia=(6.321, 6.321, 0.034), + power_off_drag="data/rockets/calisto/powerOffDragCurve.csv", + power_on_drag="data/rockets/calisto/powerOnDragCurve.csv", + center_of_mass_without_motor=0, + coordinate_system_orientation="tail_to_nose", + ) + fin = fin_cls(angular_position=angle, cant_angle=2.0, **geometry) + rocket.add_surfaces(fin, -1.168) + cp = rocket.surfaces_cp_to_cdm[fin] + roll = fin.compute_forces_and_moments( + stream_velocity, 1.0, 0.3, 1.0, cp, omega + )[5] + roll_moments.append(roll) + + np.testing.assert_allclose(roll_moments, roll_moments[0], rtol=1e-9) + assert abs(roll_moments[0]) > 0 + + +@pytest.mark.parametrize( + "set_cls, fin_cls, geometry", + [ + ( + TrapezoidalFins, + TrapezoidalFin, + dict(root_chord=0.120, tip_chord=0.040, span=0.100, rocket_radius=0.0635), + ), + ( + EllipticalFins, + EllipticalFin, + dict(root_chord=0.120, span=0.100, rocket_radius=0.0635), + ), + ( + FreeFormFins, + FreeFormFin, + dict(shape_points=[(0, 0), (0.06, 0.1), (0.12, 0.0)], rocket_radius=0.0635), + ), + ], +) +def test_finset_roll_forcing_equals_n_single_fins(set_cls, fin_cls, geometry): + """A fin set's roll forcing coefficient must scale with the full fin count + ``n`` (every identically-canted fin adds the same roll moment), so it equals + ``n`` times a single fin's roll forcing -- for every fin shape. Regression + test for a bug where the set used the normal-force ``fin_num_correction(n)`` + (~n/2), halving the roll forcing (and roll rate) of a fin set.""" + n = 4 + finset = set_cls(n=n, cant_angle=2.0, **geometry) + single = fin_cls(angular_position=0, cant_angle=2.0, **geometry) + + mach_grid = np.linspace(0, 2, 11) + clf_finset = finset.roll_parameters[0](mach_grid) + clf_single = single.roll_parameters[0](mach_grid) + np.testing.assert_allclose(clf_finset, n * np.array(clf_single), rtol=1e-6) @pytest.mark.parametrize( diff --git a/tests/unit/rocket/aero_surface/test_linear_generic_surfaces.py b/tests/unit/rocket/aero_surface/test_linear_generic_surfaces.py index 4f0695143..88d7973cb 100644 --- a/tests/unit/rocket/aero_surface/test_linear_generic_surfaces.py +++ b/tests/unit/rocket/aero_surface/test_linear_generic_surfaces.py @@ -91,3 +91,33 @@ def test_compute_forces_and_moments(): z=0, ) assert forces_and_moments == (0, 0, 0, 0, 0, 0) + + +def test_roll_damping_uses_reduced_rate(): + """The roll-damping derivative cl_p is applied to the reduced roll rate + p* = p L_ref / (2 V). This must equal the previous raw-rate scaling + (0.5 rho V A L^2 / 2) * cl_p * p, confirming the change is result-identical + for the linear model.""" + ref_area, ref_length, cl_p = 2.0, 0.5, 3.0 + lgs = LinearGenericSurface(ref_area, ref_length, {"cl_p": cl_p}) + + rho, speed, raw_roll = 1.2, 10.0, 4.0 + *_, roll_moment = lgs.compute_forces_and_moments( + stream_velocity=Vector((0, 0, -speed)), # along centerline -> alpha=beta=0 + stream_speed=speed, + stream_mach=0, + rho=rho, + cp=Vector((0, 0, 0)), + omega=(0, 0, raw_roll), # raw body roll rate p, rad/s + density=Function(1.0), + dynamic_viscosity=Function(1.0), + z=0, + ) + + reduced_roll = raw_roll * ref_length / (2 * speed) + dyn_pressure_area_length = 0.5 * rho * speed**2 * ref_area * ref_length + # New (reduced-rate) formulation: + assert roll_moment == pytest.approx(dyn_pressure_area_length * cl_p * reduced_roll) + # Old (raw-rate) formulation -- identical value: + old_damping_scaling = 0.5 * rho * speed * ref_area * ref_length**2 / 2 + assert roll_moment == pytest.approx(old_damping_scaling * cl_p * raw_roll) diff --git a/tests/unit/rocket/aero_surface/test_unsteady_generic_surface.py b/tests/unit/rocket/aero_surface/test_unsteady_generic_surface.py new file mode 100644 index 000000000..893f90faf --- /dev/null +++ b/tests/unit/rocket/aero_surface/test_unsteady_generic_surface.py @@ -0,0 +1,71 @@ +"""Unit tests for the optional alpha_dot/beta_dot unsteady coefficient axes of +GenericSurface.""" + +import pytest + +from rocketpy import Function, GenericSurface +from rocketpy.mathutils.vector_matrix import Vector + +DENSITY = Function(lambda z: 1.16) +VISCOSITY = Function(lambda z: 1.8e-5) + + +def _pitch_moment(surface, alpha_dot): + return surface.compute_forces_and_moments( + Vector([0, 0, -100]), + 100, + 0.29, + 1.16, + Vector([0, 0, 0]), + Vector([0, 0, 0]), + DENSITY, + VISCOSITY, + 100.0, + alpha_dot=alpha_dot, + beta_dot=0.0, + )[3] + + +def test_unsteady_axes_extend_independent_vars(): + surface = GenericSurface( + reference_area=1, reference_length=0.2, coefficients={}, unsteady_aero=True + ) + assert surface.independent_vars[7:] == ["alpha_dot", "beta_dot"] + + +def test_prescribed_alpha_dot_produces_unsteady_pitch_moment(): + surface = GenericSurface( + reference_area=1, + reference_length=0.2, + coefficients={ + "cm": lambda a, b, m, re, p, q, r, alpha_dot, beta_dot: 0.7 * alpha_dot + }, + unsteady_aero=True, + ) + m0 = _pitch_moment(surface, 0.0) + m1 = _pitch_moment(surface, 0.5) + m2 = _pitch_moment(surface, 1.0) + assert m0 == pytest.approx(0.0) + assert m1 != pytest.approx(0.0) + assert m2 == pytest.approx(2 * m1) + + +def test_default_surface_ignores_alpha_dot_and_stays_seven_var(): + """Existing 7-variable surfaces must be unaffected: independent vars + unchanged and alpha_dot/beta_dot ignored at evaluation.""" + surface = GenericSurface( + reference_area=1, + reference_length=0.2, + coefficients={"cm": lambda a, b, m, re, p, q, r: 0.1}, + ) + assert surface.independent_vars == [ + "alpha", + "beta", + "mach", + "reynolds", + "pitch_rate", + "yaw_rate", + "roll_rate", + ] + # passing nonzero alpha_dot must not change the result + assert _pitch_moment(surface, 0.0) == pytest.approx(_pitch_moment(surface, 99.0)) diff --git a/tests/unit/rocket/test_rocket.py b/tests/unit/rocket/test_rocket.py index 3c7725fa5..623eebf1a 100644 --- a/tests/unit/rocket/test_rocket.py +++ b/tests/unit/rocket/test_rocket.py @@ -34,7 +34,7 @@ def test_evaluate_static_margin_assert_cp_equals_cm(dimensionless_calisto): rocket.center_of_mass(burn_time[1]) / (2 * rocket.radius), 1e-8 ) == pytest.approx(rocket.static_margin(burn_time[1]), 1e-8) assert pytest.approx(rocket.total_lift_coeff_der(0), 1e-8) == pytest.approx(0, 1e-8) - assert pytest.approx(rocket.cp_position(0), 1e-8) == pytest.approx(0, 1e-8) + assert pytest.approx(rocket.aerodynamic_center(0), 1e-8) == pytest.approx(0, 1e-8) @pytest.mark.parametrize( @@ -53,7 +53,7 @@ def test_add_nose_assert_cp_cm_plus_nose(k, type_, calisto, dimensionless_calist assert static_margin_final == pytest.approx(calisto.static_margin(np.inf), 1e-8) assert clalpha == pytest.approx(calisto.total_lift_coeff_der(0), 1e-8) - assert calisto.cp_position(0) == pytest.approx(cpz, 1e-8) + assert calisto.aerodynamic_center(0) == pytest.approx(cpz, 1e-8) dimensionless_calisto.add_nose(length=0.55829 * m, kind=type_, position=(1.160) * m) assert pytest.approx(dimensionless_calisto.static_margin(0), 1e-8) == pytest.approx( @@ -66,8 +66,8 @@ def test_add_nose_assert_cp_cm_plus_nose(k, type_, calisto, dimensionless_calist dimensionless_calisto.total_lift_coeff_der(0), 1e-8 ) == pytest.approx(calisto.total_lift_coeff_der(0), 1e-8) assert pytest.approx( - dimensionless_calisto.cp_position(0) / m, 1e-8 - ) == pytest.approx(calisto.cp_position(0), 1e-8) + dimensionless_calisto.aerodynamic_center(0) / m, 1e-8 + ) == pytest.approx(calisto.aerodynamic_center(0), 1e-8) def test_add_tail_assert_cp_cm_plus_tail(calisto, dimensionless_calisto, m): @@ -91,7 +91,7 @@ def test_add_tail_assert_cp_cm_plus_tail(calisto, dimensionless_calisto, m): assert np.abs(clalpha) == pytest.approx( np.abs(calisto.total_lift_coeff_der(0)), 1e-8 ) - assert calisto.cp_position(0) == cpz + assert calisto.aerodynamic_center(0) == cpz dimensionless_calisto.add_tail( top_radius=0.0635 * m, @@ -109,8 +109,8 @@ def test_add_tail_assert_cp_cm_plus_tail(calisto, dimensionless_calisto, m): dimensionless_calisto.total_lift_coeff_der(0), 1e-8 ) == pytest.approx(calisto.total_lift_coeff_der(0), 1e-8) assert pytest.approx( - dimensionless_calisto.cp_position(0) / m, 1e-8 - ) == pytest.approx(calisto.cp_position(0), 1e-8) + dimensionless_calisto.aerodynamic_center(0) / m, 1e-8 + ) == pytest.approx(calisto.aerodynamic_center(0), 1e-8) @pytest.mark.parametrize( @@ -146,7 +146,9 @@ def test_add_trapezoidal_fins_sweep_angle( assert cl_alpha == pytest.approx(expected_clalpha, 0.01) # Check rocket's center of pressure (just double checking) - assert translate - calisto.cp_position(0) == pytest.approx(expected_cpz_cm, 0.01) + assert translate - calisto.aerodynamic_center(0) == pytest.approx( + expected_cpz_cm, 0.01 + ) @pytest.mark.parametrize( @@ -186,7 +188,9 @@ def test_add_trapezoidal_fins_sweep_length( assert cl_alpha == pytest.approx(expected_clalpha, 0.01) # Check rocket's center of pressure (just double checking) - assert translate - calisto.cp_position(0) == pytest.approx(expected_cpz_cm, 0.01) + assert translate - calisto.aerodynamic_center(0) == pytest.approx( + expected_cpz_cm, 0.01 + ) assert isinstance(calisto.aerodynamic_surfaces[0].component, NoseCone) @@ -223,7 +227,7 @@ def test_add_fins_assert_cp_cm_plus_fins(calisto, dimensionless_calisto, m): assert np.abs(clalpha) == pytest.approx( np.abs(calisto.total_lift_coeff_der(0)), 1e-8 ) - assert calisto.cp_position(0) == pytest.approx(cpz, 1e-8) + assert calisto.aerodynamic_center(0) == pytest.approx(cpz, 1e-8) dimensionless_calisto.add_trapezoidal_fins( 4, @@ -242,8 +246,8 @@ def test_add_fins_assert_cp_cm_plus_fins(calisto, dimensionless_calisto, m): dimensionless_calisto.total_lift_coeff_der(0), 1e-8 ) == pytest.approx(calisto.total_lift_coeff_der(0), 1e-8) assert pytest.approx( - dimensionless_calisto.cp_position(0) / m, 1e-8 - ) == pytest.approx(calisto.cp_position(0), 1e-8) + dimensionless_calisto.aerodynamic_center(0) / m, 1e-8 + ) == pytest.approx(calisto.aerodynamic_center(0), 1e-8) @pytest.mark.parametrize( @@ -732,22 +736,16 @@ def test_drag_csv_header_order_independent_for_multivariable_input(tmp_path): drag_ordered = rocket_ordered.power_off_drag_7d(0, 0, 0.8, 0.15, 0, 0, 0) drag_swapped = rocket_swapped.power_off_drag_7d(0, 0, 0.8, 0.15, 0, 0, 0) - ordered_closure = rocket_ordered.power_off_drag_7d.source.__closure__ - swapped_closure = rocket_swapped.power_off_drag_7d.source.__closure__ - ordered_csv_function = next( - cell.cell_contents - for cell in ordered_closure - if isinstance(cell.cell_contents, Function) - ) - swapped_csv_function = next( - cell.cell_contents - for cell in swapped_closure - if isinstance(cell.cell_contents, Function) - ) + # The coefficient is stored at minimal dimension over the present columns, + # keyed by name, so column order in the header does not matter. + ordered_csv_function = rocket_ordered.power_off_drag_7d.function + swapped_csv_function = rocket_swapped.power_off_drag_7d.function assert drag_ordered == pytest.approx(0.95) assert drag_swapped == pytest.approx(0.95) assert drag_swapped == pytest.approx(drag_ordered) + assert set(rocket_ordered.power_off_drag_7d.depends_on) == {"mach", "reynolds"} + assert set(rocket_swapped.power_off_drag_7d.depends_on) == {"mach", "reynolds"} assert ordered_csv_function.get_interpolation_method() == "regular_grid" assert swapped_csv_function.get_interpolation_method() == "regular_grid" diff --git a/tests/unit/rocket/test_stability_rework.py b/tests/unit/rocket/test_stability_rework.py new file mode 100644 index 000000000..e911b9feb --- /dev/null +++ b/tests/unit/rocket/test_stability_rework.py @@ -0,0 +1,93 @@ +"""Tests for the reworked stability model: the aerodynamic center, the +cp_position alias, the reconstructed nonlinear center of pressure, and the +aggregate aerodynamic coefficients.""" + +import numpy as np +import pytest + + +def test_cp_position_alias_matches_aerodynamic_center(calisto_robust): + """``cp_position`` is a plain alias of ``aerodynamic_center`` (no warning).""" + rocket = calisto_robust + assert rocket.cp_position.get_value_opt(0.3) == pytest.approx( + rocket.aerodynamic_center.get_value_opt(0.3) + ) + + +def test_reconstructed_center_of_pressure_converges_to_aerodynamic_center( + calisto_robust, +): + """The nonlinear center of pressure, reconstructed from the aggregate + coefficients as ``x_cdm + csys * d * Cm / CN``, converges to the linear + aerodynamic center as the angle of attack goes to zero. (The singular + nonlinear CP is no longer a blessed method; this is its documented + reconstruction path.)""" + rocket = calisto_robust + mach = 0.3 + aerodynamic_center = rocket.aerodynamic_center.get_value_opt(mach) + csys = rocket._csys + diameter = 2 * rocket.radius + cdm = rocket.center_of_dry_mass_position + + coeffs = rocket.aerodynamic_coefficients_full(np.radians(0.1), 0.0, mach) + reconstructed_cp = cdm + csys * diameter * coeffs["cm"] / coeffs["cL"] + assert reconstructed_cp == pytest.approx(aerodynamic_center, abs=1e-3) + + +def test_aerodynamic_coefficients_normal_force_grows_with_alpha(calisto_robust): + """Total normal-force coefficient increases with angle of attack and is zero + at zero incidence; the returned dict exposes normal force and pitch moment.""" + rocket = calisto_robust + coeffs = rocket.aerodynamic_coefficients(np.radians(5), 0.0, 0.3) + assert set(coeffs) == {"normal_force", "pitch_moment"} + + cn_2 = rocket.aerodynamic_coefficients(np.radians(2), 0.0, 0.3)["normal_force"] + cn_8 = rocket.aerodynamic_coefficients(np.radians(8), 0.0, 0.3)["normal_force"] + assert cn_8 > cn_2 > 0 + + +def test_axisymmetric_rocket_planes_coincide(calisto_robust): + """An axisymmetric rocket has matching pitch and yaw aerodynamic centers.""" + rocket = calisto_robust + assert rocket.is_axisymmetric + for mach in (0.0, 0.5, 1.0): + assert rocket.aerodynamic_center.get_value_opt(mach) == pytest.approx( + rocket.aerodynamic_center_yaw.get_value_opt(mach) + ) + + +def test_aerodynamic_coefficients_full_signed_set(calisto_robust): + """The full rocket coefficient set returns all six signed coefficients; + lift grows with alpha, drag comes from the vehicle drag curve, and the pitch + moment is restoring (negative) for a stable rocket.""" + rocket = calisto_robust + coeffs = rocket.aerodynamic_coefficients_full(np.radians(5), 0.0, 0.3) + assert set(coeffs) == {"cL", "cQ", "cD", "cm", "cn", "cl"} + + low = rocket.aerodynamic_coefficients_full(np.radians(2), 0.0, 0.3) + assert coeffs["cL"] > low["cL"] > 0 + assert coeffs["cm"] < 0 # restoring pitch moment about the center of dry mass + assert coeffs["cD"] == pytest.approx( + rocket.power_off_drag_by_mach.get_value_opt(0.3) + ) + + +def test_add_vehicle_aerodynamic_surface(calisto_robust): + """A supplied full-vehicle coefficient set is added as a single generic + surface and contributes to the rocket aggregate (rocket-as-GenericSurface).""" + rocket = calisto_robust + base_cl = rocket.aerodynamic_coefficients_full(np.radians(5), 0.0, 0.3)["cL"] + n_before = len(rocket.aerodynamic_surfaces) + + surface = rocket.add_vehicle_aerodynamic_surface( + coefficients={"cL": lambda a, b, m, re, p, q, r: 2.0 * a} + ) + + assert len(rocket.aerodynamic_surfaces) == n_before + 1 + # The vehicle surface exposes the uniform coefficient accessors. + assert surface.cL(np.radians(5), 0, 0.3, 0, 0, 0, 0) == pytest.approx( + 2.0 * np.radians(5) + ) + # Its lift adds to the rocket aggregate. + new_cl = rocket.aerodynamic_coefficients_full(np.radians(5), 0.0, 0.3)["cL"] + assert new_cl > base_cl diff --git a/tests/unit/simulation/test_event_scheduler.py b/tests/unit/simulation/test_event_scheduler.py new file mode 100644 index 000000000..e69de29bb diff --git a/tests/unit/simulation/test_flight.py b/tests/unit/simulation/test_flight.py index eacd2f3e1..0d42ee3a3 100644 --- a/tests/unit/simulation/test_flight.py +++ b/tests/unit/simulation/test_flight.py @@ -240,8 +240,8 @@ def test_export_sensor_data(flight_calisto_with_sensors): @pytest.mark.parametrize( "flight_time, expected_values", [ - ("t_initial", (0.25886, -0.649623, 0)), - ("out_of_rail_time", (0.792028, -1.987634, 0)), + ("t_initial", (-0.256474, -0.221748, 0)), + ("out_of_rail_time", (0.780787, -1.967135, 0)), ("apogee_time", (-0.509420, -0.732933, -2.089120e-14)), ("t_final", (0, 0, 0)), ], @@ -279,9 +279,9 @@ def test_aerodynamic_moments(flight_calisto_custom_wind, flight_time, expected_v @pytest.mark.parametrize( "flight_time, expected_values", [ - ("t_initial", (1.654150, 0.659142, -0.067103)), - ("out_of_rail_time", (5.052628, 2.013361, -1.75370)), - ("apogee_time", (2.321838, -1.613641, -0.962108)), + ("t_initial", (-0.062135, -1.936030, 1.612160)), + ("out_of_rail_time", (4.968766, 1.957238, -0.629070)), + ("apogee_time", (2.343357, -1.606424, -0.377026)), ("t_final", (-0.019802, 0.012030, 159.051604)), ], ) From b0c523305e9fc733c765e7a25ceeb82e55339815 Mon Sep 17 00:00:00 2001 From: MateusStano Date: Fri, 10 Jul 2026 19:15:33 -0300 Subject: [PATCH 04/22] MNT: polish event and flight-phase helper docstrings Co-Authored-By: Claude Opus 4.8 --- rocketpy/simulation/events/event.py | 26 ++++++++------- rocketpy/simulation/events/event_builders.py | 2 +- rocketpy/simulation/helpers/event_calling.py | 33 +++++++++++++++++++ rocketpy/simulation/helpers/event_commands.py | 25 +++++++++----- rocketpy/simulation/helpers/flight_phase.py | 3 ++ 5 files changed, 68 insertions(+), 21 deletions(-) diff --git a/rocketpy/simulation/events/event.py b/rocketpy/simulation/events/event.py index bfcd1051d..4029653a9 100644 --- a/rocketpy/simulation/events/event.py +++ b/rocketpy/simulation/events/event.py @@ -25,7 +25,7 @@ class Event: - """Event helper with trigger/callback execution and exact-time support. + """A rule that runs an action during a flight when a condition is met. An ``Event`` is the main way RocketPy reacts to conditions during a flight. It pairs a ``trigger`` predicate with a ``callback`` action: at @@ -147,11 +147,11 @@ def __init__( # pylint: disable=too-many-arguments slower with no gain in accuracy. Automatically forced to ``False`` when ``sampling_rate`` is ``None``. changes_dynamics : bool, optional - Set to ``True`` when the callback changes the simulation dynamics or - any parameter affecting the ODE derivative. This includes mutating an - attribute of any simulation object, and using the - ``set_derivative``, ``start_flight_phase``, or ``terminate_flight`` - commands. Defaults to ``False``. + Set to ``True`` when the callback changes anything that affects the + equations of motion. This includes changing an attribute of any + simulation object, and using the ``set_derivative``, + ``start_flight_phase``, or ``terminate_flight`` commands. Defaults to + ``False``. name : str, optional Human-readable identifier used in logs and debugging. Defaults to ``"Custom Event"``. @@ -174,12 +174,11 @@ def __init__( # pylint: disable=too-many-arguments - 3: Controller events - 4: Custom / user-defined events (default) needs : list of str or None, optional - Declares which expensive simulation values the event's trigger and - callback actually access. Valid keys are ``'state_dot'``, - ``'pressure'``, and ``'state_history'``. The default``None`` is - treated as an empty set and no expensive kwargs are computed. - Supply a list with the keys this event accesses so the runtime - computes them. + Which of the slower-to-compute simulation values the event's trigger + and callback actually use, so the rest are skipped. Valid keys are + ``'state_dot'``, ``'pressure'`` and ``'state_history'``. The default + ``None`` means none of them are computed. List the keys your event + uses to have them provided in ``kwargs``. See Also -------- @@ -306,6 +305,9 @@ def __call__(self, trigger_only=False, callback_only=False, reset=True, **kwargs If True, only execute the callback without evaluating the trigger condition. The exact time function and disable_on function are also called. + reset : bool, optional + If True (default), reset the event's queued commands (via + ``_reset_commands``) before evaluating the trigger. kwargs : dict Keyword arguments passed to the trigger and callback functions. diff --git a/rocketpy/simulation/events/event_builders.py b/rocketpy/simulation/events/event_builders.py index 6eeb56248..0e170255f 100644 --- a/rocketpy/simulation/events/event_builders.py +++ b/rocketpy/simulation/events/event_builders.py @@ -160,7 +160,7 @@ def apogee_event_exact_time_function(state, **_kwargs): ---------- state : array_like Interpolated flight state vector without time. - **kwargs : dict + **_kwargs : dict Event context (unused here). Returns diff --git a/rocketpy/simulation/helpers/event_calling.py b/rocketpy/simulation/helpers/event_calling.py index 2af5abc71..5bbb2601e 100644 --- a/rocketpy/simulation/helpers/event_calling.py +++ b/rocketpy/simulation/helpers/event_calling.py @@ -30,11 +30,29 @@ def build_event_kwargs( Parameters ---------- + flight : Flight + Flight instance whose rocket, environment and sensors are exposed. + time : float + Current simulation time. + state : array_like + Current flight state vector. + step_size : float + Size of the current integration step. + phase : FlightPhase + Active flight phase, providing the state derivative. + rollback : bool, optional + Whether this call happens during a rollback; shifts the state-history + window by one extra step. Defaults to False. needs : frozenset of str, optional Union of ``Event.needs`` across all events that will consume the returned dict. Only keys present in ``needs`` are computed for the expensive values: ``state_dot``, ``pressure``, ``state_history``. Defaults to empty (compute nothing expensive). + + Returns + ------- + dict + Keyword arguments consumed by event triggers and callbacks. """ kwargs = { "time": time, @@ -72,10 +90,25 @@ def update_overshootable_event_kwargs( Parameters ---------- + flight : Flight + Flight instance whose environment is used to recompute derived values. + phase : FlightPhase + Active flight phase, providing the state derivative. + event_kwargs : dict + Kwargs dict (from :func:`build_event_kwargs`) updated in place. + interpolated_time : float + Interpolated time of the overshootable node. + interpolated_state : array_like + Interpolated flight state at the node. needs : frozenset of str, optional Union of ``Event.needs`` across all overshootable events at this node. Expensive values are skipped when absent from ``needs``. Defaults to empty (compute nothing expensive). + + Returns + ------- + dict + The updated ``event_kwargs``. """ event_kwargs["time"] = interpolated_time event_kwargs["state"] = interpolated_state diff --git a/rocketpy/simulation/helpers/event_commands.py b/rocketpy/simulation/helpers/event_commands.py index 348e5add5..7d3391eb4 100644 --- a/rocketpy/simulation/helpers/event_commands.py +++ b/rocketpy/simulation/helpers/event_commands.py @@ -28,6 +28,9 @@ def apply_event_commands( Index of the current flight phase. node_index : int Index of the current time node. + command_time : float + Simulation time used to apply the commands when the event does not + provide an exact time. Returns ------- @@ -70,10 +73,6 @@ def apply_rollback_command(flight, time, state): ---------- flight : Flight Flight instance being updated. - event_results : dict - Result payload returned by the event system. - phase : _FlightPhase - Current flight phase. time : float Interpolated simulation time to restore. state : array_like @@ -94,6 +93,8 @@ def apply_disable_commands(_, event_results, node_index, event, phase, time): Parameters ---------- + _ : Flight + Flight instance (unused; accepted for a uniform command signature). event_results : dict Result payload returned by the event system. node_index : int @@ -102,6 +103,8 @@ def apply_disable_commands(_, event_results, node_index, event, phase, time): Event currently being processed. phase : _FlightPhase Current flight phase. + time : float + Simulation time at which the events are disabled. Returns ------- @@ -144,6 +147,8 @@ def apply_enable_commands(flight, event_results, node_index, event, phase, time) Parameters ---------- + flight : Flight + Flight instance being updated. event_results : dict Result payload returned by the event system. node_index : int @@ -152,6 +157,8 @@ def apply_enable_commands(flight, event_results, node_index, event, phase, time) Event currently being processed. phase : _FlightPhase Current flight phase. + time : float + Simulation time at which the events are enabled. Returns ------- @@ -260,6 +267,8 @@ def apply_new_phase_or_derivative( Index of the current flight phase. node_index : int Index of the current time node. + time : float + Simulation time at which the new phase or derivative takes effect. Returns ------- @@ -317,6 +326,8 @@ def apply_termination(flight, event_results, phase, phase_index, node_index, tim Index of the current flight phase. node_index : int Index of the current time node. + time : float + Simulation time at which the flight is terminated. Returns ------- @@ -358,12 +369,10 @@ def apply_event_list_updates(flight, event_results, phase, time): Flight instance being updated. event_results : dict Result payload returned by the event system. - node_index : int - Index of the current time node. - event : Event - Event currently being processed. phase : _FlightPhase Current flight phase. + time : float + Simulation time at which the new events are scheduled. Returns ------- diff --git a/rocketpy/simulation/helpers/flight_phase.py b/rocketpy/simulation/helpers/flight_phase.py index fbdd0b746..1a9d3642a 100644 --- a/rocketpy/simulation/helpers/flight_phase.py +++ b/rocketpy/simulation/helpers/flight_phase.py @@ -276,6 +276,9 @@ def add_phase( name : str, optional A descriptive name to identify the phase in logs and debug output. Default is None. + **kwargs + Additional keyword arguments forwarded to the ``_FlightPhase`` + constructor (e.g. ``parachute``). Returns ------- From 63cad27b3f69e44fe64f0da06858f150be0c5a43 Mon Sep 17 00:00:00 2001 From: MateusStano Date: Fri, 10 Jul 2026 19:15:57 -0300 Subject: [PATCH 05/22] ENH: rework aerodynamic coefficients onto a body-frame GenericSurface Every aerodynamic surface is now rooted in GenericSurface and stores its force coefficients in the body frame (cN/cY/cA) plus the cm/cn/cl moments, exposing all nine coefficients (cL/cD/cQ/cN/cY/cA/cm/cn/cl) with the wind trio lazily derived. A force_convention argument lets users supply wind- or body-frame coefficients. Barrowman surfaces (nose, tail, fin sets) keep the classic geometric normal-force/moment method, report the force at the geometric center of pressure via the classic 180-degree surface rotation, and expose cN_alpha/cY_beta stability slopes (the old clalpha relabelled). Co-Authored-By: Claude Opus 4.8 --- rocketpy/mathutils/function.py | 131 ++++- rocketpy/plots/aero_surface_plots.py | 13 +- rocketpy/plots/rocket_plots.py | 4 +- .../rocket/aero_surface/_barrowman_surface.py | 199 +++++-- .../rocket/aero_surface/aero_coefficient.py | 412 ++++++++------ rocketpy/rocket/aero_surface/air_brakes.py | 3 +- .../controllable_generic_surface.py | 64 ++- .../rocket/aero_surface/fins/_base_fin.py | 6 +- .../aero_surface/fins/elliptical_fin.py | 9 +- .../aero_surface/fins/elliptical_fins.py | 9 +- rocketpy/rocket/aero_surface/fins/fin.py | 99 ++-- rocketpy/rocket/aero_surface/fins/fins.py | 30 +- .../rocket/aero_surface/fins/free_form_fin.py | 9 +- .../aero_surface/fins/free_form_fins.py | 9 +- .../aero_surface/fins/trapezoidal_fins.py | 9 +- .../rocket/aero_surface/generic_surface.py | 504 +++++++++++++----- .../aero_surface/linear_generic_surface.py | 277 ++++++---- rocketpy/rocket/aero_surface/nose_cone.py | 21 +- rocketpy/rocket/aero_surface/tail.py | 20 +- rocketpy/rocket/rocket.py | 172 +++--- rocketpy/simulation/flight.py | 35 +- .../linear_generic_surfaces_fixtures.py | 4 +- tests/unit/mathutils/test_function.py | 87 +++ .../test_barrowman_generic_equivalence.py | 49 +- .../aero_surface/test_generic_surfaces.py | 153 +++++- .../test_linear_generic_surfaces.py | 22 +- .../test_surface_coefficient_completeness.py | 206 +++++++ tests/unit/rocket/test_rocket.py | 4 +- tests/unit/rocket/test_stability_rework.py | 27 +- tests/unit/simulation/test_flight.py | 10 +- 30 files changed, 1773 insertions(+), 824 deletions(-) create mode 100644 tests/unit/rocket/aero_surface/test_surface_coefficient_completeness.py diff --git a/rocketpy/mathutils/function.py b/rocketpy/mathutils/function.py index 33a82ec01..2787f569e 100644 --- a/rocketpy/mathutils/function.py +++ b/rocketpy/mathutils/function.py @@ -40,6 +40,32 @@ "regular_grid": 6, } EXTRAPOLATION_TYPES = {"zero": 0, "natural": 1, "constant": 2} +# Maps a requested interpolation name onto a scipy ``RegularGridInterpolator`` +# ``method`` for gridded (N-D Cartesian) data. The 1-D-only names ``spline`` and +# ``akima`` fall back to their closest grid analogs (``cubic`` and the +# shape-preserving ``pchip``); anything unrecognized defaults to ``linear``. +REGULAR_GRID_METHODS = { + "linear": "linear", + "nearest": "nearest", + "slinear": "slinear", + "cubic": "cubic", + "quintic": "quintic", + "pchip": "pchip", + "spline": "cubic", + "akima": "pchip", + "polynomial": "cubic", +} +# Minimum points per axis required by each ``RegularGridInterpolator`` method. +# A grid with fewer samples on any axis cannot use the higher-order methods, so +# the caller falls back to linear rather than letting SciPy raise mid-build. +REGULAR_GRID_MIN_POINTS = { + "nearest": 1, + "linear": 2, + "slinear": 2, + "pchip": 2, + "cubic": 4, + "quintic": 6, +} class SourceType(Enum): @@ -157,7 +183,12 @@ def __init__( @classmethod def from_regular_grid_csv( - cls, csv_source, variable_names, coeff_name, extrapolation + cls, + csv_source, + variable_names, + coeff_name, + extrapolation, + interpolation="linear", ): """Create a regular-grid Function from CSV samples when possible. @@ -171,6 +202,14 @@ def from_regular_grid_csv( Name of the output coefficient. extrapolation : str Extrapolation method passed to the Function constructor. + interpolation : str, optional + Requested interpolation. Mapped onto a + :class:`scipy.interpolate.RegularGridInterpolator` ``method`` via + :data:`REGULAR_GRID_METHODS` (e.g. ``"spline"`` -> ``"cubic"``, + ``"akima"`` -> ``"pchip"``); unrecognized names fall back to + ``"linear"``. Smooth methods require enough points per axis + (``"cubic"`` needs at least 4), otherwise SciPy raises. Default + ``"linear"``. Returns ------- @@ -215,13 +254,33 @@ def from_regular_grid_csv( return None grid_data = sorted_values.reshape(tuple(axis.size for axis in axes)) - return cls( + grid_function = cls( (axes, grid_data), inputs=variable_names, outputs=[coeff_name], interpolation="regular_grid", extrapolation=extrapolation, ) + # Honor the requested interpolation on the grid by rebuilding the + # interpolator/extrapolator with the mapped scipy ``method``. The + # constructor above always builds the default ("linear"); only rebuild + # when a different method was asked for. + grid_method = REGULAR_GRID_METHODS.get(interpolation, "linear") + smallest_axis = min(axis.size for axis in axes) + if smallest_axis < REGULAR_GRID_MIN_POINTS.get(grid_method, 2): + warnings.warn( + f"Grid interpolation method '{grid_method}' needs at least " + f"{REGULAR_GRID_MIN_POINTS[grid_method]} points per axis, but the " + f"coarsest axis of '{coeff_name}' has {smallest_axis}; falling " + "back to 'linear'.", + UserWarning, + ) + grid_method = "linear" + if grid_method != "linear": + grid_function._grid_method = grid_method + grid_function.set_interpolation("regular_grid") + grid_function.set_extrapolation(grid_function.get_extrapolation_method()) + return grid_function # Define all set methods def set_inputs(self, inputs): @@ -318,6 +377,10 @@ def set_source(self, source): # pylint: disable=too-many-statements self.__dom_dim__ = source.shape[1] - 1 self._domain = source[:, :-1] self._image = source[:, -1] + # Cache per-dimension domain bounds so the N-D hot evaluation path + # (``__get_value_opt_nd``) does not recompute them on every call. + self._domain_min = self._domain.min(axis=0) + self._domain_max = self._domain.max(axis=0) # set x and y. If Function is 2D, also set z if self.__dom_dim__ == 1: @@ -488,11 +551,20 @@ def __process_grid_source(self, source): f"{grid_data.shape[i]} points." ) if not np.all(np.diff(ax) > 0): - warnings.warn( - f"Axis {i} is not strictly sorted in ascending order. " - "RegularGridInterpolator requires sorted axes.", - UserWarning, - ) + # RegularGridInterpolator requires strictly ascending axes. Sort + # this axis (and reorder the grid data along it) so descending or + # shuffled inputs are accepted; repeated coordinates cannot form + # a regular grid and are rejected with a clear error rather than + # a cryptic SciPy failure. + order = np.argsort(ax, kind="stable") + ax = ax[order] + grid_data = np.take(grid_data, order, axis=i) + axes[i] = ax + if not np.all(np.diff(ax) > 0): + raise ValueError( + f"Axis {i} has repeated coordinates; a regular grid " + "requires strictly increasing values along each axis." + ) self._grid_axes = axes self._grid_data = grid_data @@ -596,7 +668,7 @@ def rbf_interpolation(x, x_min, x_max, x_data, y_data, coeffs): # pylint: disab grid_interpolator = RegularGridInterpolator( self._grid_axes, self._grid_data, - method="linear", + method=getattr(self, "_grid_method", "linear"), bounds_error=True, ) # Store so extrapolation funcs can reuse it @@ -720,9 +792,9 @@ def natural_extrapolation( # pylint: disable=function-redefined grid_extrapolator = RegularGridInterpolator( self._grid_axes, self._grid_data, - method="linear", + method=getattr(self, "_grid_method", "linear"), bounds_error=False, - fill_value=None, # linear extrapolation beyond edges + fill_value=None, # extrapolation beyond edges ) def natural_extrapolation( # pylint: disable=function-redefined @@ -824,8 +896,15 @@ def __get_value_opt_nd(self, *args): arg_qty = len(args) result = np.empty(arg_qty) - min_domain = self._domain.T.min(axis=1) - max_domain = self._domain.T.max(axis=1) + # Domain bounds are fixed once the source is set, so they are cached in + # ``set_source`` (this hot path runs per integration step); fall back to + # computing them for any Function built without going through it. + min_domain = getattr(self, "_domain_min", None) + if min_domain is None: + min_domain = self._domain.min(axis=0) + max_domain = self._domain.max(axis=0) + else: + max_domain = self._domain_max lower, upper = args < min_domain, args > max_domain extrap = np.logical_or(lower.any(axis=1), upper.any(axis=1)) @@ -4162,7 +4241,7 @@ def to_dict(self, **kwargs): # pylint: disable=unused-argument else: source = source.__name__ - return { + function_dict = { "source": source, "title": self.title, "inputs": self.__inputs__, @@ -4171,6 +4250,20 @@ def to_dict(self, **kwargs): # pylint: disable=unused-argument "extrapolation": self.__extrapolation__, } + # A regular-grid Function cannot be rebuilt from its flat scatter + # ``source``; persist the ``(axes, grid_data)`` structure (and the mapped + # scipy method) instead, so it round-trips through ``from_dict``. + if self.__interpolation__ == "regular_grid": + function_dict["source"] = [ + [np.asarray(axis).tolist() for axis in self._grid_axes], + np.asarray(self._grid_data).tolist(), + ] + grid_method = getattr(self, "_grid_method", "linear") + if grid_method != "linear": + function_dict["grid_method"] = grid_method + + return function_dict + @classmethod def from_dict(cls, func_dict): """Creates a Function instance from a dictionary. @@ -4184,7 +4277,7 @@ def from_dict(cls, func_dict): if func_dict["interpolation"] is None and func_dict["extrapolation"] is None: source = from_hex_decode(source) - return cls( + function = cls( source=source, interpolation=func_dict["interpolation"], extrapolation=func_dict["extrapolation"], @@ -4193,6 +4286,16 @@ def from_dict(cls, func_dict): title=func_dict["title"], ) + # Restore a non-default regular-grid method (the constructor above builds + # the "linear" default); rebuild the interpolator/extrapolator with it. + grid_method = func_dict.get("grid_method") + if grid_method and grid_method != "linear": + function._grid_method = grid_method + function.set_interpolation("regular_grid") + function.set_extrapolation(function.get_extrapolation_method()) + + return function + @staticmethod def __make_arith_lambda( operator, func, other, func_dim, other_dim=0, reverse=False diff --git a/rocketpy/plots/aero_surface_plots.py b/rocketpy/plots/aero_surface_plots.py index a3753d660..17d2e3a87 100644 --- a/rocketpy/plots/aero_surface_plots.py +++ b/rocketpy/plots/aero_surface_plots.py @@ -114,15 +114,14 @@ class _BarrowmanSurfacePlots(_LinearGenericSurfacePlots): geometry drawing and the lift-coefficient surface plot.""" def lift(self): - """Plots the lift coefficient of the aero surface as a function of Mach - and the angle of attack. A 3D plot is expected. See the rocketpy.Function - class for more information on how this plot is made. + """Plots the lift-curve slope (``clalpha``) of the aero surface as a + function of Mach number. Returns ------- None """ - self.aero_surface.cl() + self.aero_surface.clalpha() def all(self): """Plots the surface geometry, the lift coefficient and the @@ -274,8 +273,7 @@ class for more information on how this plot is made. Also, this method ------- None """ - print("Lift coefficient:") - self.aero_surface.cl(filename=filename) + print("Lift coefficient derivative:") self.aero_surface.clalpha_single_fin(filename=filename) self.aero_surface.clalpha_multiple_fins(filename=filename) @@ -356,8 +354,7 @@ class for more information on how this plot is made. Also, this method ------- None """ - print("Lift coefficient:") - self.aero_surface.cl(filename=filename) + print("Lift coefficient derivative:") self.aero_surface.clalpha_single_fin(filename=filename) def all(self, *, filename=None): diff --git a/rocketpy/plots/rocket_plots.py b/rocketpy/plots/rocket_plots.py index 02869ece6..4cfc0934b 100644 --- a/rocketpy/plots/rocket_plots.py +++ b/rocketpy/plots/rocket_plots.py @@ -794,10 +794,10 @@ def _center_of_pressure_range(self, plane, max_angle=np.deg2rad(15), samples=31) for angle in angles: if plane == "yz": coeffs = rocket.aerodynamic_coefficients_full(0.0, angle, 0.0) - force, moment = coeffs["cQ"], coeffs["cn"] + force, moment = coeffs["cY"], coeffs["cn"] else: coeffs = rocket.aerodynamic_coefficients_full(angle, 0.0, 0.0) - force, moment = coeffs["cL"], coeffs["cm"] + force, moment = coeffs["cN"], coeffs["cm"] if force == 0: continue position = cdm + csys * diameter * moment / force diff --git a/rocketpy/rocket/aero_surface/_barrowman_surface.py b/rocketpy/rocket/aero_surface/_barrowman_surface.py index 3ae96024b..addc41364 100644 --- a/rocketpy/rocket/aero_surface/_barrowman_surface.py +++ b/rocketpy/rocket/aero_surface/_barrowman_surface.py @@ -1,25 +1,34 @@ import numpy as np -from rocketpy.mathutils.vector_matrix import Vector +from rocketpy.mathutils.vector_matrix import Matrix, Vector from rocketpy.rocket.aero_surface.aero_coefficient import AeroCoefficient from rocketpy.rocket.aero_surface.linear_generic_surface import LinearGenericSurface class _BarrowmanSurface(LinearGenericSurface): - """Intermediate base for geometry-defined (Barrowman) aerodynamic surfaces + """Intermediate base for Barrowman-defined aerodynamic surfaces such as nose cones, tails/transitions and fin sets. - These surfaces historically expose a lift-curve slope ``clalpha`` (a - ``Function`` of Mach), a geometric center of pressure ``cpz`` and, for fins, - a pair of roll forcing/damping coefficients. This class translates that - Barrowman description into the linear generic-surface coefficient model so - the forces and moments are computed by the single, shared - :meth:`GenericSurface.compute_forces_and_moments`: - - - normal-force slope -> ``cL_alpha`` (pitch plane) and ``cQ_beta`` (yaw plane); - - center-of-pressure offset -> ``cm_alpha`` / ``cn_beta`` (the moment is - carried by the coefficients, with the force applied at the surface origin); - - fin roll -> ``cl_0`` (cant forcing) and ``cl_p`` (roll damping). + These surfaces expose a lift-curve slope ``clalpha`` (a ``Function`` of + Mach), a geometric center of pressure ``cpz`` and, for fins, a pair of roll + forcing/damping coefficients. + + The in-flight normal force and its moment are computed with the classic + Barrowman method (see :meth:`compute_forces_and_moments`): the normal force + uses the true total angle of attack and acts at the geometric center of + pressure, and its moment about the center of dry mass is the geometric + transport (``cp ^ force``). This reproduces the formulation used in + RocketPy's flight-test validation. The resultant force is therefore reported + at the geometric center of pressure (:attr:`force_application_point`), which + the surface-local frame maps to the body frame through + :meth:`_default_surface_rotation`. + + The class also derives the linear normal-force slopes ``cN_alpha`` (pitch + plane) and ``cY_beta`` (yaw plane), which feed the stability and + center-of-pressure diagnostics; the geometric cp is carried by the force + application point, so the moment slopes ``cm_alpha`` / ``cn_beta`` are zero. + Fin roll uses the coefficient model: ``cl_0`` (cant forcing) and ``cl_p`` + (roll damping). Subclasses must compute ``self.clalpha`` (Function of Mach) and the geometric center of pressure before calling ``super().__init__`` (which passes the @@ -28,7 +37,7 @@ class _BarrowmanSurface(LinearGenericSurface): """ # Geometry-defined Barrowman surfaces are axisymmetric by construction - # (``cQ_beta = -cL_alpha``, etc.), so they contribute identically to the + # (``cY_beta = -cN_alpha``, etc.), so they contribute identically to the # pitch and yaw planes. The individual ``Fin`` overrides this back to False. is_axisymmetric = True @@ -59,47 +68,47 @@ def _beta(mach): else: return np.sqrt(mach**2 - 1) - @property - def force_application_point(self): - """Barrowman surfaces apply the resultant force at the surface origin; - the whole center-of-pressure offset is carried by the ``cm``/``cn`` - moment coefficients (avoiding a double count with the ``cp ^ force`` - transport). The geometric center of pressure remains available through - ``self.cp``/``self.cpz`` for display and through - ``center_of_pressure_z`` as a mach-dependent diagnostic. + def _default_surface_rotation(self): + """Rotation from the surface-local frame to the body frame. A Barrowman + surface is defined in a frame flipped 180 degrees about the transverse + axis relative to the body frame (its z axis runs from the nose toward the + tail), so its geometric center of pressure maps to the body frame through + this rotation. This is RocketPy's classic convention, so the surface's + center of pressure lands at the same body-frame point as before the + generic-surface refactor. """ - return Vector([0, 0, 0]) + return Matrix([[-1, 0, 0], [0, 1, 0], [0, 0, -1]]) def evaluate_coefficients(self): - """Populate the linear generic-surface coefficient derivatives from the - surface geometry. Called by ``GenericSurface.__init__`` and again - whenever the geometry changes. + """Populate the coefficient slopes used by the stability diagnostics + from the surface geometry. Called by ``GenericSurface.__init__`` and + again whenever the geometry changes. + + Sets the normal-force slopes ``cN_alpha`` (pitch) and ``cY_beta`` (yaw) + and the fin roll coefficients when present. The geometric center of + pressure is carried by the force application point (not the moment + coefficients), so ``cm_alpha`` / ``cn_beta`` are zero. The in-flight + force and moment are computed geometrically in + :meth:`compute_forces_and_moments`. """ - clalpha = self.clalpha # Function of Mach - cpz = self.cpz # geometric center of pressure (set from center_of_pressure) - reference_length = self.reference_length - - # Axisymmetric Barrowman lift: equal-magnitude slopes in the pitch and - # yaw planes. The yaw-plane (side-force) slope is opposite in sign due to - # the aerodynamic-to-body frame convention used by the shared compute. - self.cL_alpha = self._mach_coefficient( - lambda mach: clalpha.get_value_opt(mach), "cL_alpha" - ) - self.cQ_beta = self._mach_coefficient( - lambda mach: -clalpha.get_value_opt(mach), "cQ_beta" - ) + clalpha = self.clalpha # normal-force-curve slope, a Function of Mach - # Center-of-pressure offset expressed as moment coefficients (the local - # cp ^ force couple, with the force applied at the origin). - self.cm_alpha = self._mach_coefficient( - lambda mach: -clalpha.get_value_opt(mach) * cpz / reference_length, - "cm_alpha", + # Axisymmetric Barrowman normal force: equal-magnitude slopes in the + # pitch and yaw planes. The yaw-plane (side-force) slope is opposite in + # sign due to the body-frame axis convention. + self.cN_alpha = self._mach_coefficient( + lambda mach: clalpha.get_value_opt(mach), "cN_alpha" ) - self.cn_beta = self._mach_coefficient( - lambda mach: clalpha.get_value_opt(mach) * cpz / reference_length, - "cn_beta", + self.cY_beta = self._mach_coefficient( + lambda mach: -clalpha.get_value_opt(mach), "cY_beta" ) + # The center of pressure is carried by the force application point, so + # the moment slopes add no further offset (the diagnostic recovers the + # geometric cp from the application point alone). + self.cm_alpha = self._mach_coefficient(lambda mach: 0.0, "cm_alpha") + self.cn_beta = self._mach_coefficient(lambda mach: 0.0, "cn_beta") + # Fin roll forcing (cant) and damping, when present. roll_parameters = getattr(self, "roll_parameters", None) if roll_parameters is not None: @@ -111,6 +120,102 @@ def evaluate_coefficients(self): lambda mach: cld_omega.get_value_opt(mach), "cl_p" ) + def compute_forces_and_moments( + self, + stream_velocity, + stream_speed, + stream_mach, + rho, + cp, + omega, + *args, # pylint: disable=unused-argument + ): + """Compute the surface's forces and moments with the classic Barrowman + method. Called at each simulation step. + + The normal force uses the true total angle of attack between the flow + and the body axis, ``attack_angle = arccos(-v_z / |v|)``, giving + ``0.5 * rho * V**2 * A_ref * clalpha(Mach) * attack_angle``. It is + applied perpendicular to the body axis (along the transverse flow) at the + geometric center of pressure, and its moment about the rocket's center of + dry mass is the geometric transport ``cp ^ force``. Fin sets add their + roll moment on top. + + Parameters + ---------- + stream_velocity : Vector + Velocity of the airflow relative to the surface, in the body frame. + stream_speed : float + Magnitude of the airflow speed. + stream_mach : float + Mach number of the airflow. + rho : float + Air density. + cp : Vector + Surface center of pressure relative to the center of dry mass, in + the body frame (the force-application point; see + :attr:`force_application_point`). + omega : tuple of float + Body angular velocity about the x, y, z axes. Only the roll + component (``omega[2]``) is used, by fin sets. + *args + Extra positional arguments accepted for signature compatibility with + the generic surface (``density``, ``dynamic_viscosity``, ``z``, + ``alpha_dot``, ``beta_dot``); unused by the Barrowman model. + + Returns + ------- + tuple of float + The forces (x, y, z) and the moments about the x, y, z axes, in the + body frame. + """ + R1 = R2 = R3 = M1 = M2 = M3 = 0.0 + + stream_vx, stream_vy, stream_vz = stream_velocity + if stream_vx**2 + stream_vy**2 != 0: + stream_vzn = stream_vz / stream_speed + if -stream_vzn < 1: + attack_angle = np.arccos(-stream_vzn) + c_lift = self.clalpha.get_value_opt(stream_mach) * attack_angle + lift = 0.5 * rho * stream_speed**2 * self.reference_area * c_lift + # Normal force, perpendicular to the body axis, directed along + # the transverse component of the flow. + transverse_norm = (stream_vx**2 + stream_vy**2) ** 0.5 + R1 = lift * stream_vx / transverse_norm + R2 = lift * stream_vy / transverse_norm + # The normal force acts at the geometric center of pressure, + # which ``cp`` already locates relative to the center of dry + # mass; transport its moment from there. + force = Vector([R1, R2, R3]) + M1, M2, M3 = cp ^ force + + # Fin roll (cant forcing + rate damping); zero for non-fin surfaces. + M3 += self._roll_moment(stream_speed, stream_mach, rho, omega) + + return R1, R2, R3, M1, M2, M3 + + def _roll_moment(self, stream_speed, mach, rho, omega): + """Roll moment from the linear roll coefficients: cant forcing plus + reduced-rate damping. Returns 0 for surfaces without fins, whose roll + coefficients are identically zero. + """ + reduced_roll_rate = ( + omega[2] * self.reference_length / (2 * stream_speed) + if stream_speed > 0 + else 0.0 + ) + # The Barrowman roll coefficients depend only on Mach and the roll rate. + args = (0.0, 0.0, mach, 0.0, 0.0, 0.0, reduced_roll_rate) + cl = self.clf.get_value_opt(*args) + self.cld.get_value_opt(*args) + return ( + 0.5 + * rho + * stream_speed**2 + * self.reference_area + * self.reference_length + * cl + ) + def _mach_coefficient(self, func_of_mach, name="coefficient"): """Wrap a Mach-only callable into an :class:`AeroCoefficient` that depends only on Mach but is callable over the full coefficient argument diff --git a/rocketpy/rocket/aero_surface/aero_coefficient.py b/rocketpy/rocket/aero_surface/aero_coefficient.py index fa0b35012..264b49a84 100644 --- a/rocketpy/rocket/aero_surface/aero_coefficient.py +++ b/rocketpy/rocket/aero_surface/aero_coefficient.py @@ -1,18 +1,3 @@ -"""Minimal-dimension aerodynamic coefficient storage. - -A :class:`AeroCoefficient` stores a single aerodynamic coefficient at its -*intrinsic* dimensionality - a constant, or a :class:`Function` over only the -variables the coefficient actually depends on (its ``depends_on``) - and maps -the full coefficient argument tuple (in ``independent_vars`` order) down to that -subset on every call. - -This avoids forcing a Mach-only (or constant) coefficient into a full seven -dimensional :class:`Function`: interpolation happens at the right dimension (so -a Mach-only table is not smeared across a 7-D domain) and evaluation passes only -the arguments that matter. It generalizes the per-call ``dict(zip(...))`` subset -selection that the CSV loader used to do inline. -""" - import copy import csv import inspect @@ -48,13 +33,8 @@ def build_independent_vars(unsteady_aero=False, control_variables=()): class AeroCoefficient: - """A single aerodynamic coefficient stored at minimal dimensionality. - - Building goes through :meth:`__init__`: pass a raw coefficient input - (number, callable, :class:`Function`, list/tuple of points, CSV path, or - another :class:`AeroCoefficient`) and ``depends_on`` is inferred; pass - ``depends_on`` explicitly only on the fast path where it is already known. - """ + """A single aerodynamic coefficient (such as lift or drag), stored using + only the variables it actually depends on.""" def __init__( self, @@ -64,119 +44,125 @@ def __init__( control_variables=(), name="coefficient", extrapolation=None, + interpolation=None, single_var=None, ): - """Build a coefficient stored at minimal dimensionality. - - A number is kept as a plain constant. Anything else is wrapped in a - :class:`Function` over only the variables it depends on (``depends_on``), - so a Mach-only curve stays 1-D instead of being stretched across all - seven axes. On each call the full argument tuple is mapped down to just - those arguments (using the precomputed ``_indices``). The full, ordered - list of variables comes from ``unsteady_aero`` and ``control_variables`` - via :func:`build_independent_vars`. - - Usually you do not pass ``depends_on``: leave it as ``None`` and it is - worked out from ``source`` (a number, a callable, a :class:`Function`, a - list of points, a CSV path, or another :class:`AeroCoefficient`), the - same inputs :class:`GenericSurface` accepts (see :meth:`_resolve_input`). - Pass ``depends_on`` yourself only on the fast path, where the source and - its argument order are already known (the Barrowman surfaces and - serialization). + """Build a coefficient from a value, a data table, or a function. + + A plain number is stored as a constant. Anything else is stored as a + :class:`Function` of only the variables it depends on, so a coefficient + that varies with Mach alone stays a simple 1-D curve instead of being + spread across all seven variables. When the coefficient is evaluated, + the variables it does not use are simply ignored. + + Most of the time you only pass ``source`` and leave ``depends_on`` as + ``None``, so the variables are worked out automatically. This is the + same input a :class:`GenericSurface` accepts. Pass ``depends_on`` + yourself only when the source and the order of its inputs are already + known (used internally by the Barrowman surfaces and when loading a + saved rocket). Parameters ---------- - source : number, str, list, tuple, callable, Function, or AeroCoefficient - The coefficient value, or an input it can be worked out from when - ``depends_on`` is ``None``. The accepted forms are: - - - **number**: kept as a constant. Calls return it directly, and - ``is_zero`` is set when it is exactly ``0.0`` (the linear model - uses that to skip the term). It depends on nothing. - - **callable** (function or ``lambda``): wrapped in a - :class:`Function`. When ``depends_on`` is worked out, the - parameter *names* decide it: name them after the variables they - use (e.g. ``lambda alpha, mach: ...``), give one argument per - variable, or use one argument together with ``single_var``. - - **Function**: used as given. If ``extrapolation`` is set, it is - applied to a copy, never to the object you passed in (it may be - shared elsewhere). - - **list/tuple of points**: turned into a :class:`Function` with - linear interpolation, so a list and the same data in a CSV give - the same result. - - **str**: a path to a data file. A ``.csv`` file is read by the CSV - loader (column headers name the variables; a headerless - two-column file is a 1-D table over ``single_var``); other files - are read by :class:`Function`. - - **AeroCoefficient**: an existing coefficient, re-keyed to this - surface's variables. This is what lets a surface round-trip - through ``to_dict``/``from_dict`` and lets one coefficient be - reused on several surfaces. + source : int, float, str, list, tuple, callable, Function, or AeroCoefficient + The coefficient value, given in one of these forms: + + - **number**: a constant coefficient that never changes. + - **function or lambda**: a coefficient computed from its inputs. + Name the arguments after the variables they use (e.g. + ``lambda alpha, mach: ...``), or give one argument per variable, + or a single argument together with ``single_var``. + - **Function**: a :class:`Function` you already built, used as is. + If ``extrapolation`` is given it is applied to a copy, so the + Function you passed in is left unchanged. + - **list or tuple of data points**: a table of values, read the + same way as the same data in a CSV file. The variables it depends + on are worked out from the table, using ``single_var`` for a + one-input table. + - **str**: the path to a data file. A ``.csv`` file has one column + per variable (named in the header) and the coefficient value in + the last column; a headerless two-column file is a table of + ``single_var`` versus the value. + - **AeroCoefficient**: an existing coefficient, reused as is. This + lets one coefficient be shared by several surfaces and lets a + rocket be saved and loaded. depends_on : sequence of str, optional - The variables this coefficient actually uses, a (possibly empty) - subset of the surface's full variable list (set by ``unsteady_aero`` - and ``control_variables``). Keep them in the same order as the - source's own arguments (a callable's parameters, a CSV's columns): - that order is used to pick the right values out of the full argument - tuple on each call. For example, ``()`` for a constant, ``("mach",)`` - for a Mach-only curve, or the whole list for something that uses - every variable. A name that is not one of the surface's variables - raises a ``ValueError``. Leave it as ``None`` (the default) to have - it worked out from ``source``; pass it only on the fast path, where - the source and its argument order are already known. + The variables this coefficient actually uses, chosen from the + surface's variables: the seven base ones ``"alpha"``, ``"beta"``, + ``"mach"``, ``"reynolds"``, ``"pitch_rate"``, ``"yaw_rate"``, + ``"roll_rate"``, plus ``"alpha_dot"`` and ``"beta_dot"`` when + ``unsteady_aero`` is ``True``, plus any names in + ``control_variables``. List them in the same order as the source's + own inputs (a function's arguments, a CSV's columns). For example, + ``()`` for a constant, ``("mach",)`` for a Mach-only curve, or the + whole list for something that uses every variable. A name that is + not one of the surface's variables raises a ``ValueError``. Leave it + as ``None`` (the default) to have it worked out from ``source``. unsteady_aero : bool, optional - Add the unsteady axes to this coefficient's variables. When ``True``, - ``alpha_dot`` and ``beta_dot`` (the rates of change of the angle of - attack and sideslip) are added after the seven base axes, so calls - take two more arguments. The flight integrator fills these in, using - ``0`` when it does not compute them, so ordinary tables keep working. - Match the owning surface's setting. Default ``False``. + Whether the coefficient can also depend on how fast the flow angles + are changing. When ``True``, two more variables, ``alpha_dot`` and + ``beta_dot`` (the rates of change of the angle of attack and + sideslip), are added after the seven base variables. The simulation + fills these in, using ``0`` when it does not compute them, so + ordinary coefficients keep working. This must match the surface the + coefficient belongs to. Default ``False``. control_variables : sequence of str, optional - Names of extra axes supplied from outside, such as control-surface - deflections from a controller. They are added after the base and - unsteady axes, and each one becomes an extra call argument, in the - order given. Used by :class:`ControllableGenericSurface` and air - brakes; empty for ordinary surfaces. Default ``()``. + Names of extra variables, such as control-surface deflections set by + a controller. They are added after the base (and unsteady) variables, + in the order given. Empty for ordinary surfaces. Default ``()``. name : str, optional A readable name for the coefficient (e.g. ``"cL_alpha"`` or - ``"Drag Coefficient with Power Off"``). It labels the underlying - :class:`Function` and appears in error messages, so a clear name - makes problems easier to spot. Default ``"coefficient"``. + ``"Drag Coefficient with Power Off"``). It appears in error messages, + so a clear name makes problems easier to spot. Default + ``"coefficient"``. extrapolation : str, optional - How the stored :class:`Function` behaves outside its data range, one - of the options of :meth:`Function.set_extrapolation`: ``"constant"`` - holds the edge value (used for drag, which should not run past its - data), ``"natural"`` keeps following the curve, ``"zero"`` returns - ``0``. ``None`` (the default) leaves a :class:`Function` you passed - in unchanged, and uses ``"natural"`` for one built from a callable. - An override is always applied to a copy, so your object is never - changed. + What the coefficient does outside the range of its data table: + ``"constant"`` holds the value at the nearest edge (the safe default + for aerodynamic coefficients, which should not shoot off to + unrealistic values), ``"natural"`` keeps following the curve, and + ``"zero"`` returns ``0``. ``None`` (the default) leaves a + :class:`Function` you passed in unchanged and uses ``"constant"`` for + a table built here. Has no effect on a constant or a function, which + are evaluated directly. + interpolation : str, optional + How the coefficient reads values *between* the points of its data + table, for example ``"linear"``, ``"akima"`` or ``"spline"`` for a + one-input table. Only affects data tables (CSV files, lists of + points, a :class:`Function`); it has no effect on a constant or a + function. ``None`` (the default) leaves a :class:`Function` you + passed in unchanged and uses ``"linear"`` for a table built here. single_var : str, optional - Which variable a 1-D input maps to. Used only while working out - ``depends_on`` for a single-dimension source: a headerless - two-column CSV, a 1-D :class:`Function`, or a one-argument callable. - ``None`` (the default) guesses it from the input's label, falling - back to the first variable; drag passes ``"mach"`` so a plain - Cd-vs-Mach curve maps to Mach. Ignored when ``depends_on`` is given. - Default ``None``. + Which variable a one-input table or function maps to. Used only when + working out the variables of a single-input source: a headerless + two-column CSV, a one-input :class:`Function`, or a one-argument + function. ``None`` (the default) guesses it from the input's label + and otherwise falls back to the first variable. Ignored when + ``depends_on`` is given. Default ``None``. """ self.name = name self.extrapolation = extrapolation + self.interpolation = interpolation self.unsteady_aero = unsteady_aero self.control_variables = tuple(control_variables) + # ``unsteady_aero`` and ``control_variables`` define the full ordered + # variable list: every coefficient's argument order and each variable's + # position. This is a surface-wide property, distinct from ``depends_on`` + # (the subset a single coefficient reads), and it is passed in rather + # than derived from ``depends_on``: inferring ``depends_on`` already + # needs this list, and the unsteady axes shift the position of the + # control variables even for coefficients that never use the rates. self.independent_vars = tuple( build_independent_vars(unsteady_aero, control_variables) ) # Infer the stored source and its dependencies from the raw input when - # ``depends_on`` is not given. ``_resolve_input`` may also adopt the - # input's extrapolation (re-keying an AeroCoefficient), so refresh the - # local ``extrapolation`` used by the source-storage block below. + # ``depends_on`` is not given. if depends_on is None: source, depends_on = self._resolve_input(source, single_var) extrapolation = self.extrapolation + interpolation = self.interpolation # ``depends_on`` is kept in the given order because it matches the # positional argument order of the stored source (callable parameters, - # CSV columns, …). ``_indices`` therefore maps the full argument tuple + # CSV columns, …). ``_indices`` then maps the full argument tuple # to the source's own argument order. self.depends_on = tuple(depends_on) unknown = [var for var in self.depends_on if var not in self.independent_vars] @@ -192,21 +178,26 @@ def __init__( self.is_zero = False self._constant = None if isinstance(source, Function): - # Only override extrapolation when explicitly asked, and on a copy: - # the source may be a user-owned Function reused elsewhere, so - # mutating it in place (e.g. drag forcing "constant") would change - # its behavior everywhere the caller reuses it. - if extrapolation is not None: + # Only override interpolation/extrapolation when explicitly asked, + # and always on a copy (the Function may be shared elsewhere). + if interpolation is not None or extrapolation is not None: source = copy.deepcopy(source) - source.set_extrapolation(extrapolation) + # Interpolation names like "akima"/"spline" are 1-D concepts; a + # multi-dimensional Function (e.g. a regular grid) keeps its own + # interpolation, whose method is fixed when the grid is built, so + # a 1-D name here would wrongly fall back to "shepard". + if interpolation is not None and source.__dom_dim__ == 1: + source.set_interpolation(interpolation) + if extrapolation is not None: + source.set_extrapolation(extrapolation) self.function = source elif callable(source): self.function = Function( source, list(self.depends_on) or ["x"], [name], - interpolation="linear", - extrapolation=extrapolation or "natural", + interpolation=interpolation or "linear", + extrapolation=extrapolation or "constant", ) else: # Scalar constant. @@ -219,13 +210,23 @@ def __init__( def _resolve_input(self, source, single_var): """Infer ``(stored source, depends_on)`` from a raw coefficient input. - Mirrors the coefficient inputs accepted by :class:`GenericSurface`: a - number, a callable, a :class:`Function`, a list/tuple of data points, a - path to a CSV (or other text) file, or another :class:`AeroCoefficient` - (re-keyed). - Called by :meth:`__init__` when ``depends_on`` is omitted; the returned - ``source`` is a number, a callable or a :class:`Function`, which the - constructor's source-storage block then stores. + Parameters + ---------- + source : int, float, str, list, tuple, callable, Function or AeroCoefficient + Raw coefficient input: a scalar, a CSV file path (or any other path + read by :class:`Function`), a list/tuple of data points, a callable, + a pre-built :class:`Function`, or an existing ``AeroCoefficient``. + single_var : str or None + Name of the independent variable a one-dimensional input depends on. + When ``None``, it is inferred from the source (see + :meth:`_infer_single_var` / :meth:`_infer_callable_depends_on`). + + Returns + ------- + tuple + ``(stored_source, depends_on)`` where ``stored_source`` is the scalar + or :class:`Function` kept internally and ``depends_on`` is the tuple + of independent-variable names it depends on. """ name = self.name independent_vars = self.independent_vars @@ -233,11 +234,8 @@ def _resolve_input(self, source, single_var): if isinstance(source, AeroCoefficient): # An already-built coefficient passed straight through, re-keyed to - # this surface's variable order. This is how a *surface* round-trips: - # GenericSurface/ControllableGenericSurface store their processed - # AeroCoefficients in ``to_dict`` and feed them back on ``from_dict`` - # (and a user may reuse one coefficient across surfaces). Adopt its - # extrapolation when none was requested. + # this surface's variable order. Adopt its extrapolation when none + # was requested. if self.extrapolation is None: self.extrapolation = source.extrapolation value = ( @@ -251,23 +249,25 @@ def _resolve_input(self, source, single_var): source, name, independent_vars, - extrapolation=self.extrapolation or "natural", + extrapolation=self.extrapolation or "constant", + interpolation=self.interpolation or "linear", single_var=single_var, ) - # Any other path (e.g. a whitespace-delimited ``.txt`` curve) is read - # by Function, which auto-detects the delimiter. Linear interpolation - # matches the CSV loader, so the same data gives identical results - # whatever file form it is given. Falls through to the Function - # branch below (a 1-D table keyed to ``single_var``). - source = Function(source, interpolation="linear") - - # A list/tuple of data points is parsed by Function and handled below. - # Linear interpolation matches the CSV loader, so the same tabular data - # gives identical results whether supplied as a list or a CSV file - # (Function would otherwise default to spline). + # Any other path is read by Function + source = Function( + source, + interpolation=self.interpolation or "linear", + extrapolation=self.extrapolation or "constant", + ) + + # A list/tuple of data points is parsed by Function and handled below if isinstance(source, (list, tuple)): try: - source = Function(list(source), interpolation="linear") + source = Function( + list(source), + interpolation=self.interpolation or "linear", + extrapolation=self.extrapolation or "constant", + ) except (TypeError, ValueError) as exc: raise TypeError( f"Invalid list/tuple input for {name}: could not be parsed " @@ -306,7 +306,12 @@ def _resolve_input(self, source, single_var): @staticmethod def _load_csv( - file_path, name, independent_vars, extrapolation="natural", single_var=None + file_path, + name, + independent_vars, + extrapolation="constant", + interpolation="linear", + single_var=None, ): # pylint: disable=too-many-statements """Load a coefficient CSV at minimal dimension. @@ -327,7 +332,12 @@ def _load_csv( the CSV header columns. extrapolation : str, optional Extrapolation method for the loaded ``Function``. Defaults to - ``"natural"``; drag coefficients pass ``"constant"``. + ``"constant"`` (holds the edge value past the tabulated range). + interpolation : str, optional + Interpolation method for the loaded ``Function``. Defaults to + ``"linear"``. For 1-D and non-grid tables it is used directly; a + strict Cartesian grid uses ``"regular_grid"`` with the method mapped + from this value (see :meth:`Function.from_regular_grid_csv`). single_var : str, optional Independent variable a headerless two-column table depends on. Defaults to the first independent variable. @@ -367,7 +377,7 @@ def _is_numeric(value): if len(header) == 2 and all(_is_numeric(cell) for cell in header): csv_func = Function( file_path, - interpolation="linear", + interpolation=interpolation, extrapolation=extrapolation, ) return csv_func, [single_var or independent_vars[0]] @@ -399,17 +409,15 @@ def _is_numeric(value): ordered_present_columns, name, extrapolation=extrapolation, + interpolation=interpolation, ) if csv_func is None: csv_func = Function( file_path, - interpolation="linear", + interpolation=interpolation, extrapolation=extrapolation, ) - # The CSV columns may appear in any order; AeroCoefficient maps the full - # argument tuple to ``ordered_present_columns`` order, so the stored - # Function is queried directly at its own (minimal) dimensionality. return csv_func, ordered_present_columns @staticmethod @@ -433,20 +441,25 @@ def _infer_single_var(function, independent_vars): @staticmethod def _infer_callable_depends_on(func, independent_vars, name, single_var=None): - """Infer ``depends_on`` for a plain callable. - - Conventions are accepted in order: - - 0. *Single variable* - when ``single_var`` is given and the callable - takes a single argument, it depends on that one variable regardless - of the parameter name (e.g. a Mach-only drag ``lambda mach: ...``). - 1. *Named subset* - every parameter name is an independent variable, so - the parameters themselves name the dependency subset (e.g. - ``lambda alpha, mach: ...``). - 2. *Positional full-arity* - the parameter count equals the number of - independent variables, so the callable depends on all of them - regardless of how its parameters are named (e.g. - ``lambda a, b, m, r, p, q, rr: ...``). + """Work out which variables a function coefficient uses, from its + arguments. + + Three ways to write the function are accepted, tried in this order: + + 1. One argument plus ``single_var``: the function takes a single + argument and ``single_var`` says which variable it is, whatever the + argument is named (e.g. a Mach-only drag curve ``lambda mach: ...`` + with ``single_var="mach"``). + 2. Arguments named after variables: every argument name matches one of + the surface's variables, so the names themselves list what the + function uses (e.g. ``lambda alpha, mach: ...`` uses ``alpha`` and + ``mach``). + 3. One argument per variable: the function has exactly as many arguments + as there are variables, so it is taken to use all of them, whatever + the arguments are named (e.g. ``lambda a, b, m, r, p, q, rr: ...`` + for the seven base variables). + + Anything else raises ``ValueError``. """ n_vars = len(independent_vars) try: @@ -469,19 +482,21 @@ def _infer_callable_depends_on(func, independent_vars, name, single_var=None): @property def is_zero_coefficient(self): - """Back-compat alias used by the linear model's hot-loop term skipping.""" + """Kept-for-compatibility alias of ``is_zero``: whether the coefficient + is the constant 0 (the linear model uses it to skip zero terms).""" return self.is_zero @property def __dom_dim__(self): - """Number of full independent variables (the call arity).""" + """Number of variables the coefficient is called with.""" return len(self.independent_vars) def get_value_opt(self, *args): - """Fast, unvalidated evaluation (mirrors :meth:`Function.get_value_opt`). + """Fast evaluation without input checking (mirrors + :meth:`Function.get_value_opt`). - Maps the full ``independent_vars`` argument tuple down to the source's - own ``depends_on`` arguments before evaluating; a constant short-circuits. + Receives every variable, passes on only the ones this coefficient uses, + and evaluates the source. A constant is returned right away. """ if self._constant is not None: return self._constant @@ -506,6 +521,7 @@ def __mul__(self, other): self.control_variables, self.name, extrapolation=self.extrapolation, + interpolation=self.interpolation, ) __rmul__ = __mul__ @@ -519,6 +535,7 @@ def to_dict(self, **kwargs): # pylint: disable=unused-argument "control_variables": list(self.control_variables), "name": self.name, "extrapolation": self.extrapolation, + "interpolation": self.interpolation, } @classmethod @@ -531,6 +548,7 @@ def from_dict(cls, data): data.get("control_variables", ()), data["name"], extrapolation=data.get("extrapolation"), + interpolation=data.get("interpolation"), ) def __repr__(self): @@ -538,3 +556,61 @@ def __repr__(self): if self._constant is not None: return f"AeroCoefficient({self.name}={self._constant})" return f"AeroCoefficient({self.name}, depends_on={self.depends_on})" + + def slice(self, *free_variables, at=None): + """Return a :class:`Function` of only the chosen variables, holding the + others fixed. + + This gives a lower-dimensional view of the coefficient, handy for + inspection or plotting. For example, ``cL.slice("alpha", "mach")`` is the + lift coefficient as a function of angle of attack and Mach, with sideslip, + Reynolds number and the rotation rates held at zero; ``cD.slice("mach")`` + is a Mach-only drag curve. + + Parameters + ---------- + *free_variables : str + Names of the variables to keep as inputs, in the order you want them + (for example ``"mach"`` or ``"alpha", "mach"``). Each must be one of + this coefficient's independent variables. + at : dict, optional + Values to hold the remaining variables at, keyed by variable name. + Any not listed are held at 0. + + Returns + ------- + Function + A Function of ``free_variables`` that evaluates this coefficient with + the remaining variables held fixed. + """ + fixed = dict(at or {}) + unknown = [ + var + for var in list(free_variables) + list(fixed) + if var not in self.independent_vars + ] + if unknown: + raise ValueError( + f"{self.name} has no independent variable(s) {unknown}; valid " + f"variables are {list(self.independent_vars)}." + ) + + free_positions = [self.independent_vars.index(var) for var in free_variables] + baseline = [fixed.get(var, 0.0) for var in self.independent_vars] + + if not free_variables: + return Function(self.get_value_opt(*baseline)) + + def sliced(*values): + args = list(baseline) + for position, value in zip(free_positions, values): + args[position] = value + return self.get_value_opt(*args) + + # Give the wrapper an explicit signature so Function reads the right + # number of inputs (its domain dimension comes from the parameter count). + sliced.__signature__ = inspect.Signature( + inspect.Parameter(var, inspect.Parameter.POSITIONAL_OR_KEYWORD) + for var in free_variables + ) + return Function(sliced, list(free_variables), [self.name]) diff --git a/rocketpy/rocket/aero_surface/air_brakes.py b/rocketpy/rocket/aero_surface/air_brakes.py index 221440dc6..0e7a5965f 100644 --- a/rocketpy/rocket/aero_surface/air_brakes.py +++ b/rocketpy/rocket/aero_surface/air_brakes.py @@ -9,7 +9,6 @@ from .controllable_generic_surface import ControllableGenericSurface -# TODO: review airbrakes implementation to make it more in line with events class AirBrakes(ControllableGenericSurface): """AirBrakes class. Inherits from :class:`ControllableGenericSurface`, using ``deployment_level`` as its single control variable and a multivariable drag @@ -93,7 +92,7 @@ def __init__( Default is False. deployment_level : float, optional Initial deployment level, ranging from 0 to 1. Deployment level is - the fraction of the total airbrake area that is Deployment. Default + the fraction of the total airbrake area that is deployed. Default is 0. name : str, optional Name of the air brakes. Default is "AirBrakes". diff --git a/rocketpy/rocket/aero_surface/controllable_generic_surface.py b/rocketpy/rocket/aero_surface/controllable_generic_surface.py index 793c49936..52ad1ae1f 100644 --- a/rocketpy/rocket/aero_surface/controllable_generic_surface.py +++ b/rocketpy/rocket/aero_surface/controllable_generic_surface.py @@ -2,24 +2,23 @@ class ControllableGenericSurface(GenericSurface): - """A generic aerodynamic surface whose coefficients additionally depend on - one or more **control-deflection** variables (canards, grid fins, elevons, - air-brake deployment, …) sourced at runtime from a controller. + """A generic aerodynamic surface whose coefficients also depend on one or + more control inputs (canards, grid fins, elevons, air-brake deployment, and + so on) set by a controller while the rocket flies. - On top of the seven standard independent variables of - :class:`GenericSurface` (``alpha``, ``beta``, ``mach``, ``reynolds``, - ``pitch_rate``, ``yaw_rate``, ``roll_rate``), the coefficient functions take - one extra argument per entry of ``controls`` (appended in order). The - current control values are held in :attr:`control_state` and mutated each - simulation step by a controller (see ``Rocket.add_controllable_surface``); - :meth:`_coefficient_arguments` appends them to every coefficient evaluation. + On top of the seven standard variables of :class:`GenericSurface` + (``alpha``, ``beta``, ``mach``, ``reynolds``, ``pitch_rate``, ``yaw_rate``, + ``roll_rate``), each coefficient takes one extra input per control, in the + order listed in ``controls``. A controller updates the current control + values every simulation step (see ``Rocket.add_controllable_surface``), and + they are passed to the coefficients automatically. Attributes ---------- ControllableGenericSurface.control_variables : list of str - Names of the control-deflection axes, in coefficient-argument order. + Names of the controls, in the order the coefficients expect them. ControllableGenericSurface.control_state : dict - Current value of each control variable (defaults to 0). + Current value of each control (starts at 0). """ # TODO: deflection-dependent static-margin diagnostics. @@ -32,7 +31,7 @@ class ControllableGenericSurface(GenericSurface): # # The gap is diagnostic-only. The derived ``center_of_pressure_z`` / # ``aerodynamic_center`` come from ``cm_alpha = d(cm)/d(alpha)`` evaluated ONCE - # (in ``_set_derived_cp_accessors``) with the control variables frozen at their + # (in ``_set_stability_accessors``) with the control variables frozen at their # value at construction (0). So if ``cm`` couples alpha and a control axis # (e.g. an ``alpha * deflection`` term), the reported ``static_margin`` is # pinned to the zero-deflection configuration and does not track ``set_control``. @@ -59,6 +58,8 @@ def __init__( center_of_pressure=(0, 0, 0), name="Controllable Generic Surface", controls=("deflection",), + extrapolation=None, + interpolation=None, ): """Create a controllable generic aerodynamic surface. @@ -69,19 +70,34 @@ def __init__( reference_length : int, float Reference length of the surface, in meters. coefficients : dict - Aerodynamic coefficients (``cL``, ``cQ``, ``cD``, ``cm``, ``cn``, - ``cl``), each a callable/CSV/Function of the seven base variables - **plus** the control variables listed in ``controls`` (appended in - order). Omitted coefficients default to 0. + The six force and moment coefficients (``cL``, ``cQ``, ``cD``, + ``cm``, ``cn``, ``cl``), by name. Each one can be a constant, a + function, or a path to a data file, and depends on the seven base + variables **plus** the controls listed in ``controls`` (in that + order). Any you leave out are set to 0. center_of_pressure : tuple, list, optional Application point of the aerodynamic forces and moments in the local surface frame. Default ``(0, 0, 0)``. name : str, optional Name of the surface. Default ``"Controllable Generic Surface"``. controls : iterable of str, optional - Names of the control-deflection axes. Default ``("deflection",)``. - Each name becomes an extra coefficient argument and a key in - :attr:`control_state`. + Names of the controls, such as a canard deflection angle. Default + ``("deflection",)``. Each name becomes an extra input to every + coefficient and a key in :attr:`control_state`. + extrapolation : str or dict, optional + What tabulated coefficients do outside their data range: + ``"constant"`` holds the nearest edge value, ``"natural"`` keeps + following the curve, ``"zero"`` returns 0. Give one string for all + coefficients or a dict keyed by coefficient name. ``None`` (the + default) uses ``"constant"`` for tables built here and leaves a + pre-built :class:`Function` unchanged. + interpolation : str or dict, optional + How tabulated coefficients read values between points (for example + ``"linear"``, ``"akima"`` or ``"spline"`` for a 1-D table; see + :class:`rocketpy.GenericSurface` for the full list by table type). + Give one string for all coefficients or a dict keyed by coefficient + name. ``None`` (the default) uses ``"linear"`` for tables built here + and leaves a pre-built :class:`Function` unchanged. """ # These must be set before ``super().__init__`` so coefficient # processing (arity, CSV validation) and the derived-cp accessors see @@ -96,6 +112,8 @@ def __init__( coefficients=coefficients, center_of_pressure=center_of_pressure, name=name, + extrapolation=extrapolation, + interpolation=interpolation, ) # ``self.prints``/``self.plots`` are the generic ones wired by the base. @@ -162,9 +180,9 @@ def to_dict( # pylint: disable=unused-argument "reference_area": self.reference_area, "reference_length": self.reference_length, "coefficients": { - "cL": self.cL, - "cQ": self.cQ, - "cD": self.cD, + "cN": self.cN, + "cY": self.cY, + "cA": self.cA, "cm": self.cm, "cn": self.cn, "cl": self.cl, diff --git a/rocketpy/rocket/aero_surface/fins/_base_fin.py b/rocketpy/rocket/aero_surface/fins/_base_fin.py index 332326aea..9b2b7a536 100644 --- a/rocketpy/rocket/aero_surface/fins/_base_fin.py +++ b/rocketpy/rocket/aero_surface/fins/_base_fin.py @@ -73,7 +73,7 @@ def _update_geometry_chain(self): # Geometry changed after construction: refresh the coefficients. self.evaluate_coefficients() self.compute_all_coefficients() - self._evaluate_derived_coefficients() + self._evaluate_stability_derivatives() else: self._finalize_barrowman() @@ -320,7 +320,7 @@ def evaluate_single_fin_lift_coefficient(self): 2 * np.pi * self.AR / (clalpha2D * np.cos(self.gamma_c)) ) - # Lift coefficient derivative for a single fin + # Normal-force coefficient derivative for a single fin def lift_source(mach): return ( clalpha2D(mach) @@ -336,7 +336,7 @@ def lift_source(mach): self.clalpha_single_fin = Function( lift_source, "Mach", - "Lift coefficient derivative for a single fin", + "Normal-force coefficient derivative for a single fin", ) @abstractmethod diff --git a/rocketpy/rocket/aero_surface/fins/elliptical_fin.py b/rocketpy/rocket/aero_surface/fins/elliptical_fin.py index 249a8cf70..1db9dc75d 100644 --- a/rocketpy/rocket/aero_surface/fins/elliptical_fin.py +++ b/rocketpy/rocket/aero_surface/fins/elliptical_fin.py @@ -77,11 +77,12 @@ class EllipticalFin(Fin): Fin set local center of pressure z coordinate. Has units of length and is given in meters. EllipticalFin.cl : Function - Function which defines the lift coefficient as a function of the angle - of attack and the Mach number. Takes as input the angle of attack in - radians and the Mach number. Returns the lift coefficient. + Roll-moment coefficient, inherited from the generic-surface model + (a function of the flow variables). Zero for a nose cone or tail; for a + fin set it carries the cant forcing and roll damping. The lift-curve + slope is ``clalpha``. EllipticalFin.clalpha : float - Lift coefficient slope. Has units of 1/rad. + Normal-force coefficient slope. Has units of 1/rad. """ def __init__( diff --git a/rocketpy/rocket/aero_surface/fins/elliptical_fins.py b/rocketpy/rocket/aero_surface/fins/elliptical_fins.py index 4576bd1f3..74aea986a 100644 --- a/rocketpy/rocket/aero_surface/fins/elliptical_fins.py +++ b/rocketpy/rocket/aero_surface/fins/elliptical_fins.py @@ -80,11 +80,12 @@ class EllipticalFins(Fins): Fin set local center of pressure z coordinate. Has units of length and is given in meters. EllipticalFins.cl : Function - Function which defines the lift coefficient as a function of the angle - of attack and the Mach number. Takes as input the angle of attack in - radians and the Mach number. Returns the lift coefficient. + Roll-moment coefficient, inherited from the generic-surface model + (a function of the flow variables). Zero for a nose cone or tail; for a + fin set it carries the cant forcing and roll damping. The lift-curve + slope is ``clalpha``. EllipticalFins.clalpha : float - Lift coefficient slope. Has units of 1/rad. + Normal-force coefficient slope. Has units of 1/rad. """ def __init__( diff --git a/rocketpy/rocket/aero_surface/fins/fin.py b/rocketpy/rocket/aero_surface/fins/fin.py index b2875e658..c9ca3e490 100644 --- a/rocketpy/rocket/aero_surface/fins/fin.py +++ b/rocketpy/rocket/aero_surface/fins/fin.py @@ -81,19 +81,19 @@ class Fin(_BaseFin): Fin set local center of pressure z coordinate. Has units of length and is given in meters. Fin.cl : Function - Function which defines the lift coefficient as a function of the angle - of attack and the Mach number. Takes as input the angle of attack in - radians and the Mach number. Returns the lift coefficient. + Roll-moment coefficient, inherited from the generic-surface model + (a function of the flow variables). Zero for a nose cone or tail; for a + fin set it carries the cant forcing and roll damping. The lift-curve + slope is ``clalpha``. Fin.clalpha : float - Lift coefficient slope. Has units of 1/rad. + Normal-force coefficient slope. Has units of 1/rad. Fin.roll_parameters : list List containing the roll moment lift coefficient, the roll moment damping coefficient and the cant angle in radians. """ - # A single fin contributes unequally to the pitch and yaw planes - # (``cL_alpha`` ~ sin^2(phi), ``cQ_beta`` ~ cos^2(phi)), so it is not - # axisymmetric on its own. A complete, evenly spaced set may still be + # A single fin contributes unequally to the pitch and yaw planes, so it is + # not axisymmetric on its own. A complete, evenly spaced set may still be # axisymmetric collectively, which the rocket's numeric check resolves. is_axisymmetric = False @@ -155,17 +155,8 @@ def __init__( self._angular_position_rad = math.radians(angular_position) def _update_geometry_chain(self): - """Run the base geometry/coefficient chain, then (re)build the body<->fin - rotation matrices. - - The rotation matrices must be set **after** the chain: the chain's first - call initializes the generic-surface machinery, which resets - ``_rotation_surface_to_body`` to the identity. Doing it here (rather than - in each concrete fin's ``__init__``) ensures every individual-fin - subclass -- trapezoidal, elliptical and free-form -- gets correct, - angular-position-aware rotation matrices on construction and whenever the - geometry changes. - """ + """Run the base geometry/coefficient chain, then (re)build the body to + fin rotation matrices.""" super()._update_geometry_chain() self.evaluate_rotation_matrix() @@ -224,14 +215,7 @@ def evaluate_lift_coefficient(self): self.clalpha = self.clalpha_single_fin * self.lift_interference_factor - # Cl = clalpha * alpha - self.cl = Function( - lambda alpha, mach: alpha * self.clalpha(mach), - ["Alpha (rad)", "Mach"], - "Lift coefficient", - ) - - return self.cl + return self.clalpha def evaluate_roll_parameters(self): """Calculates and returns the fin set's roll coefficients. @@ -302,12 +286,7 @@ def evaluate_rotation_matrix(self): sin_delta = math.sin(delta) cos_delta = math.cos(delta) - # The body -> fin change of basis is composed right-to-left as - # ``R_delta @ R_phi @ R_pi`` (R_pi first, R_delta last). Each factor - # therefore acts on the coordinates produced by the factors to its right, - # i.e. in the *current* (partially rotated) frame, not the body frame. - - # Roll by the angular position, about the rocket longitudinal axis. + # Rotation about body Z by angular position R_phi = Matrix( [ [cos_phi, -sin_phi, 0], @@ -316,12 +295,7 @@ def evaluate_rotation_matrix(self): ] ) - # Cant rotation about the fin **span (y) axis**. Because R_delta is the - # leftmost factor, it acts on coordinates already in the rolled - # uncanted-fin frame, so it rotates about that frame's y axis (the fin's - # own root-to-tip direction) -- NOT body Y, with which it coincides only - # at angular_position = 0. This is what makes each fin cant about its own - # span; using body Y (``R_uncanted @ R_delta``) would be wrong. + # Cant rotation about body Y R_delta = Matrix( [ [cos_delta, 0, -sin_delta], @@ -330,9 +304,7 @@ def evaluate_rotation_matrix(self): ] ) - # 180 flip about Y so the uncanted fin z axis points leading -> trailing - # edge (toward the tail, i.e. -body z), with x completing a right-handed - # frame. Proper rotation (det +1), not a reflection. + # 180 flip about Y to align fin leading/trailing edge R_pi = Matrix( [ [-1, 0, 0], @@ -341,7 +313,7 @@ def evaluate_rotation_matrix(self): ] ) - # Uncanted body -> fin, then apply the cant in the fin span frame. + # Uncanted body to fin, then apply cant R_uncanted = R_phi @ R_pi R_body_to_fin = R_delta @ R_uncanted @@ -353,36 +325,30 @@ def evaluate_rotation_matrix(self): @property def force_application_point(self): - """A single (off-axis) fin keeps its bespoke force computation and - transports the moment geometrically through its center of pressure, - so the force application point is the fin's actual cp rather than the - surface origin used by axisymmetric Barrowman surfaces. + """Point where the fin's aerodynamic force is applied, in body frame. + + Returns + ------- + Vector + The fin's center of pressure ``[cpx, cpy, cpz]``. """ return Vector([self.cpx, self.cpy, self.cpz]) def evaluate_coefficients(self): - """A single fin transports its moment geometrically (via ``cp ^ force`` - in its own ``compute_forces_and_moments``), so only the normal-force - slopes are exposed for the stability-margin diagnostic; the moment - coefficients stay zero to avoid double-counting the cp offset. - - A fin's lift only resists incidence in its own plane, so its slope is - projected onto the pitch and yaw planes by its angular position - ``phi``: ``sin(phi)**2`` to the pitch plane (``cL_alpha``) and - ``cos(phi)**2`` to the yaw plane (``cQ_beta``). A fin at ``phi = 0`` - (lying in the yaw plane) thus feeds the yaw plane only, which is what - makes a non-axisymmetric individual-fin layout report different pitch- - and yaw-plane centers of pressure. An evenly spaced set of ``n`` fins - sums to ``n / 2`` in each plane, reproducing the axisymmetric ``Fins`` - set (see :meth:`Fins.fin_num_correction`). + """Evaluate the fin's normal-force slope coefficients. + + Sets ``cN_alpha`` (pitch plane) and ``cY_beta`` (yaw plane) from the + fin's normal-force slope projected onto each plane by its angular + position. Moment coefficients are left at zero since the moment is + transported geometrically in :meth:`compute_forces_and_moments`. """ clalpha = self.clalpha sin_sq = math.sin(self.angular_position_rad) ** 2 cos_sq = math.cos(self.angular_position_rad) ** 2 - self.cL_alpha = self._mach_coefficient( + self.cN_alpha = self._mach_coefficient( lambda mach: clalpha.get_value_opt(mach) * sin_sq ) - self.cQ_beta = self._mach_coefficient( + self.cY_beta = self._mach_coefficient( lambda mach: -clalpha.get_value_opt(mach) * cos_sq ) @@ -412,6 +378,10 @@ def compute_forces_and_moments( Center of pressure coordinates in the body frame. omega: tuple[float, float, float] Tuple containing angular velocities around the x, y, z axes. + *args + Extra positional arguments accepted for signature compatibility with + the generic surface (e.g. ``density``, ``dynamic_viscosity``, ``z``, + ``alpha_dot``, ``beta_dot``). Unused by the fin's Barrowman model. Returns ------- @@ -431,7 +401,8 @@ def compute_forces_and_moments( * rho * stream_speed**2 * self.reference_area - * self.cl.get_value_opt(attack_angle, stream_mach) + * self.clalpha.get_value_opt(stream_mach) + * attack_angle ) # Force in body frame R1, R2, R3 = self._rotation_fin_to_body @ Vector([X, 0, 0]) @@ -506,7 +477,7 @@ def to_dict(self, include_outputs=False): data.update( { "cp": self.cp, - "cl": self.cl, + "clalpha": self.clalpha, "roll_parameters": self.roll_parameters, "rocket_diameter": self.rocket_diameter, "diameter": self.rocket_diameter, diff --git a/rocketpy/rocket/aero_surface/fins/fins.py b/rocketpy/rocket/aero_surface/fins/fins.py index 9913b3f5b..781857be3 100644 --- a/rocketpy/rocket/aero_surface/fins/fins.py +++ b/rocketpy/rocket/aero_surface/fins/fins.py @@ -80,11 +80,12 @@ class Fins(_BaseFin): Fin set local center of pressure z coordinate. Has units of length and is given in meters. Fins.cl : Function - Function which defines the lift coefficient as a function of the angle - of attack and the Mach number. Takes as input the angle of attack in - radians and the Mach number. Returns the lift coefficient. + Roll-moment coefficient, inherited from the generic-surface model + (a function of the flow variables). Zero for a nose cone or tail; for a + fin set it carries the cant forcing and roll damping. The lift-curve + slope is ``clalpha``. Fins.clalpha : float - Lift coefficient slope. Has units of 1/rad. + Normal-force coefficient slope. Has units of 1/rad. Fins.roll_parameters : list List containing the roll moment lift coefficient, the roll moment damping coefficient and the cant angle in radians. @@ -165,7 +166,7 @@ def evaluate_lift_coefficient(self): """ self.evaluate_single_fin_lift_coefficient() - # Lift coefficient derivative for n fins corrected with Fin-Body interference + # Normal-force coefficient derivative for n fins corrected with Fin-Body interference self.clalpha_multiple_fins = ( self.fin_num_correction(self.n) * self.lift_interference_factor @@ -173,19 +174,12 @@ def evaluate_lift_coefficient(self): ) # Function of mach number self.clalpha_multiple_fins.set_inputs("Mach") self.clalpha_multiple_fins.set_outputs( - f"Lift coefficient derivative for {self.n:.0f} fins" + f"Normal-force coefficient derivative for {self.n:.0f} fins" ) self.clalpha = self.clalpha_multiple_fins - # Cl = clalpha * alpha - self.cl = Function( - lambda alpha, mach: alpha * self.clalpha_multiple_fins(mach), - ["Alpha (rad)", "Mach"], - "Lift coefficient", - ) - - return self.cl + return self.clalpha def evaluate_roll_parameters(self): """Calculates and returns the fin set's roll coefficients. @@ -282,16 +276,14 @@ def to_dict(self, **kwargs): } if kwargs.get("include_outputs", False): - cl = self.cl + clalpha = self.clalpha if kwargs.get("discretize", False): - cl = cl.set_discrete( - (-np.pi / 6, 0), (np.pi / 6, 2), (10, 10), mutate_self=False - ) + clalpha = clalpha.set_discrete(0, 4, 50) data.update( { "cp": self.cp, - "cl": cl, + "clalpha": clalpha, "roll_parameters": self.roll_parameters, "rocket_diameter": self.rocket_diameter, "diameter": self.rocket_diameter, diff --git a/rocketpy/rocket/aero_surface/fins/free_form_fin.py b/rocketpy/rocket/aero_surface/fins/free_form_fin.py index bf6565010..eedb4b76f 100644 --- a/rocketpy/rocket/aero_surface/fins/free_form_fin.py +++ b/rocketpy/rocket/aero_surface/fins/free_form_fin.py @@ -72,11 +72,12 @@ class FreeFormFin(Fin): Fin set local center of pressure z coordinate. Has units of length and is given in meters. FreeFormFin.cl : Function - Function which defines the lift coefficient as a function of the angle - of attack and the Mach number. Takes as input the angle of attack in - radians and the Mach number. Returns the lift coefficient. + Roll-moment coefficient, inherited from the generic-surface model + (a function of the flow variables). Zero for a nose cone or tail; for a + fin set it carries the cant forcing and roll damping. The lift-curve + slope is ``clalpha``. FreeFormFin.clalpha : float - Lift coefficient slope. Has units of 1/rad. + Normal-force coefficient slope. Has units of 1/rad. FreeFormFin.mac_length : float Mean aerodynamic chord length of the fin set. FreeFormFin.mac_lead : float diff --git a/rocketpy/rocket/aero_surface/fins/free_form_fins.py b/rocketpy/rocket/aero_surface/fins/free_form_fins.py index d7c7e9512..d6186e9eb 100644 --- a/rocketpy/rocket/aero_surface/fins/free_form_fins.py +++ b/rocketpy/rocket/aero_surface/fins/free_form_fins.py @@ -73,11 +73,12 @@ class FreeFormFins(Fins): Fin set local center of pressure z coordinate. Has units of length and is given in meters. FreeFormFins.cl : Function - Function which defines the lift coefficient as a function of the angle - of attack and the Mach number. Takes as input the angle of attack in - radians and the Mach number. Returns the lift coefficient. + Roll-moment coefficient, inherited from the generic-surface model + (a function of the flow variables). Zero for a nose cone or tail; for a + fin set it carries the cant forcing and roll damping. The lift-curve + slope is ``clalpha``. FreeFormFins.clalpha : float - Lift coefficient slope. Has units of 1/rad. + Normal-force coefficient slope. Has units of 1/rad. FreeFormFins.mac_length : float Mean aerodynamic chord length of the fin set. FreeFormFins.mac_lead : float diff --git a/rocketpy/rocket/aero_surface/fins/trapezoidal_fins.py b/rocketpy/rocket/aero_surface/fins/trapezoidal_fins.py index 2c9adea58..dfffa4a83 100644 --- a/rocketpy/rocket/aero_surface/fins/trapezoidal_fins.py +++ b/rocketpy/rocket/aero_surface/fins/trapezoidal_fins.py @@ -82,11 +82,12 @@ class TrapezoidalFins(Fins): Fin set local center of pressure z coordinate. Has units of length and is given in meters. TrapezoidalFins.cl : Function - Function which defines the lift coefficient as a function of the angle - of attack and the Mach number. Takes as input the angle of attack in - radians and the Mach number. Returns the lift coefficient. + Roll-moment coefficient, inherited from the generic-surface model + (a function of the flow variables). Zero for a nose cone or tail; for a + fin set it carries the cant forcing and roll damping. The lift-curve + slope is ``clalpha``. TrapezoidalFins.clalpha : float - Lift coefficient slope. Has units of 1/rad. + Normal-force coefficient slope. Has units of 1/rad. """ def __init__( diff --git a/rocketpy/rocket/aero_surface/generic_surface.py b/rocketpy/rocket/aero_surface/generic_surface.py index d9f6fe82a..eb1dfac30 100644 --- a/rocketpy/rocket/aero_surface/generic_surface.py +++ b/rocketpy/rocket/aero_surface/generic_surface.py @@ -1,4 +1,5 @@ import copy +import inspect import math import numpy as np @@ -13,18 +14,91 @@ ) +def _as_function(func, independent_vars, name): + """Wrap a variadic callable as a :class:`Function` over ``independent_vars``. + + ``Function`` reads its domain dimension from the callable's parameter count, + so a variadic wrapper is given an explicit signature to advertise one + parameter per independent variable. + """ + func.__signature__ = inspect.Signature( + inspect.Parameter(var, inspect.Parameter.POSITIONAL_OR_KEYWORD) + for var in independent_vars + ) + return Function(func, list(independent_vars), [name]) + + +def wind_to_body_coefficients(c_lift, c_drag, c_side, independent_vars): + """Rotate wind-frame force coefficients into the body frame. + + Given the lift, drag and side-force coefficients (each callable over the + surface's independent-variable tuple, with the angle of attack and sideslip + as the first two variables), return the body-frame normal, side and axial + coefficients ``(cN, cY, cA)`` as :class:`Function`s over the same variables. + """ + lift, drag, side = c_lift.get_value_opt, c_drag.get_value_opt, c_side.get_value_opt + + def normal(*args): + alpha, beta = args[0], args[1] + transverse = math.sin(beta) * side(*args) + math.cos(beta) * drag(*args) + return math.cos(alpha) * lift(*args) + math.sin(alpha) * transverse + + def yaw_side(*args): + beta = args[1] + return math.cos(beta) * side(*args) - math.sin(beta) * drag(*args) + + def axial(*args): + alpha, beta = args[0], args[1] + transverse = math.sin(beta) * side(*args) + math.cos(beta) * drag(*args) + return -math.sin(alpha) * lift(*args) + math.cos(alpha) * transverse + + return ( + _as_function(normal, independent_vars, "cN"), + _as_function(yaw_side, independent_vars, "cY"), + _as_function(axial, independent_vars, "cA"), + ) + + +def body_to_wind_coefficients(c_normal, c_side, c_axial, independent_vars): + """Rotate body-frame force coefficients into the wind frame. + + Inverse of :func:`wind_to_body_coefficients`: given the body-frame normal, + side and axial coefficients, return the wind-frame lift, drag and + side-force coefficients ``(cL, cD, cQ)`` as :class:`Function`s. + """ + normal = c_normal.get_value_opt + side = c_side.get_value_opt + axial = c_axial.get_value_opt + + def lift(*args): + alpha = args[0] + return math.cos(alpha) * normal(*args) - math.sin(alpha) * axial(*args) + + def drag(*args): + alpha, beta = args[0], args[1] + longitudinal = math.sin(alpha) * normal(*args) + math.cos(alpha) * axial(*args) + return -math.sin(beta) * side(*args) + math.cos(beta) * longitudinal + + def yaw_side(*args): + alpha, beta = args[0], args[1] + longitudinal = math.sin(alpha) * normal(*args) + math.cos(alpha) * axial(*args) + return math.cos(beta) * side(*args) + math.sin(beta) * longitudinal + + return ( + _as_function(lift, independent_vars, "cL"), + _as_function(drag, independent_vars, "cD"), + _as_function(yaw_side, independent_vars, "cQ"), + ) + + class GenericSurface: """Defines a generic aerodynamic surface with custom force and moment coefficients. The coefficients can be nonlinear functions of the angle of attack, sideslip angle, Mach number, Reynolds number, pitch rate, yaw rate and roll rate.""" - #: Whether this surface contributes identically to the pitch and yaw planes - #: *by construction*. Conservatively ``False`` for a generic surface (its - #: coefficients may differ between planes); the built-in axisymmetric - #: surfaces override it to ``True``. The rocket uses it to skip the numeric - #: pitch/yaw axisymmetry check when every surface is symmetric by - #: construction. + # Whether this surface contributes identically to the pitch and yaw planes. + # ``False`` for a generic surface (its coefficients may differ between planes) is_axisymmetric = False def __init__( @@ -35,6 +109,9 @@ def __init__( center_of_pressure=(0, 0, 0), name="Generic Surface", unsteady_aero=False, + interpolation=None, + extrapolation=None, + force_convention=None, ): """Create a generic aerodynamic surface, defined by its aerodynamic coefficients. This surface is used to model any aerodynamic surface @@ -49,6 +126,12 @@ def __init__( "reynolds", "pitch_rate", "yaw_rate" and "roll_rate". The independent variable columns can be provided in any order. + When ``unsteady_aero`` is True, the coefficients may additionally be + functions of the flow-angle rates "alpha_dot" and "beta_dot", which are + appended (in that order) after "roll_rate": callables must accept the + two extra trailing arguments and CSV files may include "alpha_dot" and + "beta_dot" columns. + The angular-rate inputs ("pitch_rate", "yaw_rate", "roll_rate") are the conventional **non-dimensional reduced rates**, ``q* = q * L_ref / (2 * V)`` (and likewise for ``r``/``p``), matching how published and tool-generated @@ -69,57 +152,77 @@ def __init__( Reference length of the aerodynamic surface. Has the unit of meters. Commonly defined as the rocket's diameter. coefficients: dict - List of coefficients. If a coefficient is omitted, it is set to 0. - The valid coefficients are:\n - cL: str, callable, optional - Lift coefficient. Can be a path to a CSV file or a callable. - Default is 0.\n - cQ: str, callable, optional - Side force coefficient. Can be a path to a CSV file or a callable. - Default is 0.\n - cD: str, callable, optional - Drag coefficient. Can be a path to a CSV file or a callable. - Default is 0.\n + The six force and moment coefficients, by name. Any you leave out are + set to 0. Each one can be a constant number, a function of the flow + variables, a list of data points, or a path to a CSV file. By default + the force coefficients are the body-frame ones (see + ``force_convention``); the wind-frame names ``cL``/``cQ``/``cD`` are + also accepted. The coefficients are:\n + cN: str, callable, optional + Normal force coefficient (body frame). Default is 0.\n + cY: str, callable, optional + Side force coefficient (body frame). Default is 0.\n + cA: str, callable, optional + Axial force coefficient (body frame). Default is 0.\n cm: str, callable, optional - Pitch moment coefficient. Can be a path to a CSV file or a callable. - Default is 0.\n + Pitch moment coefficient. Default is 0.\n cn: str, callable, optional - Yaw moment coefficient. Can be a path to a CSV file or a callable. - Default is 0.\n + Yaw moment coefficient. Default is 0.\n cl: str, callable, optional - Roll moment coefficient. Can be a path to a CSV file or a callable. - Default is 0.\n + Roll moment coefficient. Default is 0.\n center_of_pressure : tuple, list, optional Application point of the aerodynamic forces and moments. The center of pressure is defined in the local coordinate system of the aerodynamic surface. The default value is (0, 0, 0). name : str, optional - Name of the aerodynamic surface. Default is 'GenericSurface'. + Name of the aerodynamic surface. Default is 'Generic Surface'. unsteady_aero : bool, optional If True, the coefficients additionally depend on the time derivatives of the flow angles, and ``alpha_dot`` and ``beta_dot`` are appended (in that order) to the independent variables. CSV files may then include "alpha_dot"/"beta_dot" columns, and callables must - accept the two extra trailing arguments. The simulation supplies 0 - for these unless it computes them, so existing coefficient tables are - unaffected. Default is False. + accept the two extra trailing arguments. Default is False. + interpolation : str or dict, optional + How tabulated coefficients interpolate between points. The accepted + methods depend on the coefficient's dimensionality: a 1-D table + (e.g. a Mach-only curve) accepts ``"linear"``, ``"akima"``, + ``"spline"`` and ``"polynomial"``; a multi-dimensional scattered + table accepts ``"linear"``, ``"shepard"`` and ``"rbf"``; and a + multi-dimensional table on a regular Cartesian grid accepts + ``"linear"``, ``"nearest"``, ``"slinear"``, ``"cubic"``, + ``"quintic"`` and ``"pchip"`` (with ``"spline"`` mapped to + ``"cubic"`` and ``"akima"`` to ``"pchip"``). Accepts either a simple + string or a dict keyed by coefficient name (names left out fall back + to the default). ``None`` (the default) uses ``"linear"`` for tables + built here and keeps a pre-built ``Function``'s own setting. + extrapolation : str or dict, optional + How tabulated coefficients behave outside their data range: + ``"constant"`` holds the value at the nearest data edge, + ``"natural"`` keeps following the curve, and ``"zero"`` returns 0. + Accepts either a simple string or a dict keyed by coefficient name + (names left out fall back to the default). ``None`` (the default) + uses ``"constant"`` for tables built here and keeps whatever a + pre-built ``Function`` already carries. Only affects tabulated + sources (constants and callables are evaluated directly). + force_convention : str, optional + The frame your force coefficients are given in. ``"wind"`` for the + aerodynamic-frame coefficients ``cL`` (lift), ``cQ`` (side) and + ``cD`` (drag); ``"body"`` for the body-frame coefficients ``cN`` + (normal), ``cY`` (side) and ``cA`` (axial), the convention used by + Missile DATCOM, wind tunnels and Barrowman. The moment coefficients + (``cm``, ``cn``, ``cl``) are the same in both. ``None`` (the default) + infers the frame from the coefficient names you pass. Whichever frame + you use, all nine coefficients are available as attributes afterwards + (the other frame is computed on demand). """ - # The independent variables of the coefficients are derived (see the - # ``independent_vars`` property) from ``unsteady_aero`` and, for - # subclasses, ``control_variables``. When ``unsteady_aero`` is enabled, - # the time-derivatives of the flow angles (``alpha_dot``, ``beta_dot``) - # become extra axes (defaulting to 0 at runtime, so existing tables are - # unaffected). Subclasses that add externally-supplied axes set - # ``control_variables`` before calling ``super().__init__``. self._unsteady_aero = unsteady_aero # Externally-supplied axes (e.g. control deflections). Subclasses set - # this before ``super().__init__``; defaults to none for plain surfaces. + # this before ``super().__init__``. Defaults to none for plain surfaces. self.control_variables = getattr(self, "control_variables", ()) # Ordered independent variables accepted by every coefficient: the seven # base axes, plus ``alpha_dot``/``beta_dot`` when ``unsteady_aero`` is - # enabled (integrator-supplied), plus any ``control_variables`` a - # subclass appended (externally supplied). Fixed at construction. + # enabled, plus any ``control_variables`` self.independent_vars = build_independent_vars( self._unsteady_aero, self.control_variables ) @@ -133,9 +236,22 @@ def __init__( self.cpz = center_of_pressure[2] self.name = name - self._rotation_surface_to_body = Matrix([[1, 0, 0], [0, 1, 0], [0, 0, 1]]) + self._rotation_surface_to_body = self._default_surface_rotation() default_coefficients = self._get_default_coefficients() + self.force_convention = self._resolve_force_convention( + coefficients, force_convention + ) + # The wind->body conversion only applies to surfaces whose coefficients + # are the full body-frame forces (cN/cY/cA). The linear model uses + # coefficient derivatives (cN_alpha, ...) whose frame is fixed by name. + # A non-dict input falls through to _check_coefficients, which rejects it. + if ( + self.force_convention == "wind" + and "cN" in default_coefficients + and isinstance(coefficients, dict) + ): + coefficients = self._wind_input_to_body(coefficients) self._check_coefficients(coefficients, default_coefficients) coefficients = self._complete_coefficients(coefficients, default_coefficients) for coeff, coeff_value in coefficients.items(): @@ -144,28 +260,60 @@ def __init__( unsteady_aero=self._unsteady_aero, control_variables=self.control_variables, name=coeff, + extrapolation=self._coefficient_option(extrapolation, coeff), + interpolation=self._coefficient_option(interpolation, coeff), ) setattr(self, coeff, value) self.evaluate_coefficients() - self._evaluate_derived_coefficients() + self._evaluate_stability_derivatives() # Reporting layers. Subclasses override these with their own (more # specific) prints/plots after calling ``super().__init__``. self.prints = _GenericSurfacePrints(self) self.plots = _GenericSurfacePlots(self) + def _default_surface_rotation(self): + """Rotation from the surface-local frame to the body frame. It is applied + to the :attr:`force_application_point` when the rocket locates each + surface's center of pressure relative to the center of dry mass. A plain + generic surface takes its center of pressure as already body-aligned + (the identity); geometry-defined (Barrowman) surfaces override this. + """ + return Matrix([[1, 0, 0], [0, 1, 0], [0, 0, 1]]) + @property def force_application_point(self): """Local point (surface frame) at which the resultant force is applied - when transporting its moment to the rocket's center of dry mass. For a - plain generic surface this is simply the center of pressure ``self.cp``; - the residual couple is carried by the ``cm``/``cn``/``cl`` coefficients. - Barrowman subclasses override this to the origin, because they fold the - whole cp offset into the moment coefficients instead. + when transporting its moment to the rocket's center of dry mass. This is + the center of pressure ``self.cp``; any residual couple is carried by the + ``cm``/``cn``/``cl`` coefficients. """ return Vector([self.cpx, self.cpy, self.cpz]) + @property + def cL(self): # pylint: disable=invalid-name + """Wind-frame lift coefficient, as a :class:`Function` of the surface's + independent variables. Derived from the canonical body-frame ``cN``, + ``cY`` and ``cA`` by the angle-of-attack/sideslip rotation.""" + return body_to_wind_coefficients( + self.cN, self.cY, self.cA, self.independent_vars + )[0] + + @property + def cD(self): # pylint: disable=invalid-name + """Wind-frame drag coefficient (derived from ``cN``/``cY``/``cA``).""" + return body_to_wind_coefficients( + self.cN, self.cY, self.cA, self.independent_vars + )[1] + + @property + def cQ(self): # pylint: disable=invalid-name + """Wind-frame side-force coefficient (derived from ``cN``/``cY``/``cA``).""" + return body_to_wind_coefficients( + self.cN, self.cY, self.cA, self.independent_vars + )[2] + def info(self): """Prints a summary of the surface's geometry and aerodynamic coefficients. Subclasses override this with surface-specific summaries. @@ -200,79 +348,91 @@ def evaluate_coefficients(self): None """ - def _evaluate_derived_coefficients(self): - """Build the mach-only diagnostic accessors used by the rocket's - center-of-pressure / stability-margin computation, for both the pitch - and the yaw plane. + def _evaluate_stability_derivatives(self): + """Compute the coefficient derivatives used for stability and store them + as the ``cN_alpha``, ``cm_alpha``, ``cY_beta`` and ``cn_beta`` + attributes, then build the center-of-pressure accessors from them. - These reconstruct, at the linearization point ``alpha = beta = 0`` with - zero rates, each plane's force-curve slope and the location of its - center of pressure. The center of pressure combines the surface's - declared local ``cpz`` with the offset implied by its moment - coefficient (the two representations are interchangeable; - ``cpz_eff = cpz - (dc_moment/dangle)/(dc_force/dangle) * L_ref``): - - - pitch plane: ``lift_coefficient_derivative`` (``dcL/dalpha``) and - ``center_of_pressure_z`` (from ``cm``); - - yaw plane: ``side_coefficient_derivative`` and - ``center_of_pressure_z_yaw`` (from ``cn``). + A plain generic surface recovers each derivative from its body-frame + force and moment coefficients by numerical differentiation at + ``alpha = beta = 0`` with zero rates. The Barrowman surfaces instead set + these four attributes directly from geometry and only reuse + :meth:`_set_stability_accessors` (see the :class:`LinearGenericSurface` + override). Returns ------- None """ - cL_alpha = self._partial_slope(self.cL, axis="alpha") - cm_alpha = self._partial_slope(self.cm, axis="alpha") - cQ_beta = self._partial_slope(self.cQ, axis="beta") - cn_beta = self._partial_slope(self.cn, axis="beta") - self._set_derived_cp_accessors(cL_alpha, cm_alpha, cQ_beta, cn_beta) - - def _set_derived_cp_accessors(self, cL_alpha, cm_alpha, cQ_beta, cn_beta): - """Store the pitch- and yaw-plane diagnostic accessors as mach-only - ``Function``s, guarding the moment/force division for zero-force - surfaces (which then drop out of the force-weighted cp average). + self.cN_alpha = self._derivative_coefficient(self.cN, "alpha", "cN_alpha") + self.cm_alpha = self._derivative_coefficient(self.cm, "alpha", "cm_alpha") + self.cY_beta = self._derivative_coefficient(self.cY, "beta", "cY_beta") + self.cn_beta = self._derivative_coefficient(self.cn, "beta", "cn_beta") + self._set_stability_accessors() + + def _derivative_coefficient(self, coefficient, axis, name): + """Numerically differentiate ``coefficient`` along ``axis`` at the + linearization point and wrap the Mach-only result as an + :class:`AeroCoefficient`, so every surface exposes ``cN_alpha`` and its + siblings in the same form (a coefficient callable over the full + argument tuple that depends only on Mach). Parameters ---------- - cL_alpha : Function - Pitch-plane normal-force slope ``dcL/dalpha`` vs. mach. - cm_alpha : Function - Pitch-moment slope ``dcm/dalpha`` vs. mach. - cQ_beta : Function - Yaw-plane side-force slope ``dcQ/dbeta`` vs. mach. - cn_beta : Function - Yaw-moment slope ``dcn/dbeta`` vs. mach. + coefficient : AeroCoefficient + The force or moment coefficient to differentiate. + axis : str + Either ``"alpha"`` or ``"beta"``. + name : str + Name of the resulting derivative coefficient. + + Returns + ------- + AeroCoefficient + The Mach-only derivative ``d(coefficient)/d(axis)``. + """ + slope = self._partial_slope(coefficient, axis=axis) + return AeroCoefficient( + slope, + depends_on=("mach",), + unsteady_aero=self._unsteady_aero, + control_variables=self.control_variables, + name=name, + ) + + def _set_stability_accessors(self): + """Build the pitch- and yaw-plane center-of-pressure accessors from the + stored coefficient derivatives (``cN_alpha``/``cm_alpha`` and + ``cY_beta``/``cn_beta``), each evaluated at ``alpha = beta = 0`` with + zero rates. + + Each accessor is a Mach-only :class:`Function` giving the surface's + center of pressure along the body z-axis. It combines the surface's + local application point with the offset implied by its moment + coefficient (``cp = application point - (moment slope / force slope) * + L_ref``). When a surface produces no force at some Mach the center of + pressure is undefined, so it falls back to the geometric application + point and drops out of the force-weighted average. + + Returns + ------- + None """ reference_length = self.reference_length local_cpz = self.force_application_point[2] - def _cp_z(force_slope, moment_slope): - # Recover the center of pressure from a force slope and its matching - # moment slope, as a Function of Mach. + def _cp_z(force_coeff, moment_coeff): def cp_z(mach): - slope = force_slope.get_value_opt(mach) - # No force at this Mach -> the cp is undefined; fall back to the - # geometric application point so this surface contributes zero - # weight to the force-weighted cp average. + slope = force_coeff.get_value_opt(0.0, 0.0, mach, 0.0, 0.0, 0.0, 0.0) if slope == 0: return local_cpz - # cp = application point - (moment slope / force slope) * L_ref. - return ( - local_cpz - - moment_slope.get_value_opt(mach) / slope * reference_length - ) + moment = moment_coeff.get_value_opt(0.0, 0.0, mach, 0.0, 0.0, 0.0, 0.0) + return local_cpz - moment / slope * reference_length return Function(cp_z, "Mach", "Center of pressure to local origin (m)") - # Pitch plane. - self.lift_coefficient_derivative = cL_alpha - self.center_of_pressure_z = _cp_z(cL_alpha, cm_alpha) - - # Yaw plane. The side-force slope is sign-adjusted (``-cQ_beta``) so - # that an axisymmetric surface yields the same signed weight as the - # pitch plane, making the two planes' margins coincide when symmetric. - self.side_coefficient_derivative = -cQ_beta - self.center_of_pressure_z_yaw = _cp_z(cQ_beta, cn_beta) + self.center_of_pressure_z = _cp_z(self.cN_alpha, self.cm_alpha) + self.center_of_pressure_z_yaw = _cp_z(self.cY_beta, self.cn_beta) def _partial_slope(self, coefficient, axis): """Partial derivative ``d(coefficient)/d(axis)`` at ``alpha = beta = 0`` @@ -317,6 +477,85 @@ def slope(mach): return Function(slope, "Mach", "Coefficient derivative") + @staticmethod + def _coefficient_option(option, coeff_name): + """Resolve a per-coefficient interpolation/extrapolation setting. + + ``option`` may be a single value applied to every coefficient, a dict + mapping coefficient names to values (coefficients absent from the dict + fall back to the ``AeroCoefficient`` default), or ``None``. + + Parameters + ---------- + option : str, dict, or None + The interpolation/extrapolation argument passed to ``__init__``. + coeff_name : str + Name of the coefficient being built (e.g. ``"cD"``, ``"cm_alpha"``). + + Returns + ------- + str or None + The value to forward to :class:`AeroCoefficient` for this coefficient. + """ + if isinstance(option, dict): + return option.get(coeff_name) + return option + + # Force-coefficient names in each frame. Moments (cm/cn/cl) are frame-shared. + _WIND_FORCE_NAMES = ("cL", "cQ", "cD") + _BODY_FORCE_NAMES = ("cN", "cY", "cA") + + def _resolve_force_convention(self, coefficients, force_convention): + """Decide whether the input force coefficients are given in the wind + frame (``cL``/``cQ``/``cD``) or the body frame (``cN``/``cY``/``cA``). + + When ``force_convention`` is ``None`` the frame is inferred from the + coefficient names; mixing the two frames is rejected. + """ + keys = set(coefficients) + has_wind = bool(keys & set(self._WIND_FORCE_NAMES)) + has_body = bool(keys & set(self._BODY_FORCE_NAMES)) + if force_convention is None: + if has_wind and has_body: + raise ValueError( + "Mixed wind (cL/cQ/cD) and body (cN/cY/cA) force " + "coefficients; pass force_convention='wind' or 'body'." + ) + return "body" if has_body else "wind" + if force_convention not in ("wind", "body"): + raise ValueError( + f"force_convention must be 'wind' or 'body', got {force_convention!r}." + ) + return force_convention + + def _wind_input_to_body(self, coefficients): + """Convert a wind-frame force-coefficient input (``cL``/``cQ``/``cD``) + into the canonical body-frame coefficients (``cN``/``cY``/``cA``), + leaving the moment coefficients untouched.""" + wind = {} + passthrough = {} + for name, value in coefficients.items(): + if name in self._WIND_FORCE_NAMES: + wind[name] = value + else: + passthrough[name] = value + + def as_coefficient(source, name): + return AeroCoefficient( + source, + unsteady_aero=self._unsteady_aero, + control_variables=self.control_variables, + name=name, + ) + + c_normal, c_yaw, c_axial = wind_to_body_coefficients( + as_coefficient(wind.get("cL", 0), "cL"), + as_coefficient(wind.get("cD", 0), "cD"), + as_coefficient(wind.get("cQ", 0), "cQ"), + self.independent_vars, + ) + return {"cN": c_normal, "cY": c_yaw, "cA": c_axial, **passthrough} + def _get_default_coefficients(self): """Returns default coefficients @@ -327,9 +566,9 @@ def _get_default_coefficients(self): are the default values. """ default_coefficients = { - "cL": 0, - "cQ": 0, - "cD": 0, + "cN": 0, + "cY": 0, + "cA": 0, "cm": 0, "cn": 0, "cl": 0, @@ -422,12 +661,18 @@ def _compute_from_coefficients( Non-dimensional (reduced) yaw rate, ``r * L_ref / (2 * V)``. roll_rate : float Non-dimensional (reduced) roll rate, ``p * L_ref / (2 * V)``. + alpha_dot : float, optional + Non-dimensional angle-of-attack rate, used by unsteady surfaces. + Defaults to 0. + beta_dot : float, optional + Non-dimensional sideslip-angle rate, used by unsteady surfaces. + Defaults to 0. Returns ------- tuple of float - The aerodynamic forces (lift, side_force, drag) and moments - (pitch, yaw, roll) in the body frame. + The body-frame force components ``(R1, R2, R3)`` and the moments + ``(pitch, yaw, roll)``. """ # Precompute common values dyn_pressure_area = 0.5 * rho * stream_speed**2 * self.reference_area @@ -448,17 +693,21 @@ def _compute_from_coefficients( beta_dot, ) - # Compute aerodynamic forces - lift = dyn_pressure_area * self.cL(*args) - side = dyn_pressure_area * self.cQ(*args) - drag = dyn_pressure_area * self.cD(*args) + # Body-frame force components straight from the body-frame coefficients + # (normal cN, side cY, axial cA); no wind-to-body rotation needed. + normal = dyn_pressure_area * self.cN(*args) + yaw_side = dyn_pressure_area * self.cY(*args) + axial = dyn_pressure_area * self.cA(*args) + r1 = yaw_side + r2 = -normal + r3 = -axial # Compute aerodynamic moments pitch = dyn_pressure_area_length * self.cm(*args) yaw = dyn_pressure_area_length * self.cn(*args) roll = dyn_pressure_area_length * self.cl(*args) - return lift, side, drag, pitch, yaw, roll + return r1, r2, r3, pitch, yaw, roll def _coefficient_arguments( self, @@ -524,6 +773,12 @@ def compute_forces_and_moments( z : float Altitude of the surface, used to evaluate ``density`` and ``dynamic_viscosity``. + alpha_dot : float, optional + Non-dimensional angle-of-attack rate, used by unsteady surfaces. + Defaults to 0. + beta_dot : float, optional + Non-dimensional sideslip-angle rate, used by unsteady surfaces. + Defaults to 0. Returns ------- @@ -549,18 +804,16 @@ def compute_forces_and_moments( beta = np.arctan2(stream_velocity[0], stream_velocity[2]) # Non-dimensionalize the body angular rates into the conventional reduced - # rates (e.g. ``q* = q * L_ref / (2 * V)``) before evaluating the - # coefficients, so coefficient tables follow the standard aerotable - # convention (Missile DATCOM, OpenVSP, CFD/wind-tunnel data tabulate rate - # derivatives against the reduced rates). The factor is 0 at zero airspeed - # (pad/static) to avoid division by zero; there is no aerodynamic damping - # there anyway. + # rates (e.g. ``q* = q * L_ref / (2 * V)``). reduced_rate_factor = ( self.reference_length / (2 * stream_speed) if stream_speed > 0 else 0.0 ) - # Compute aerodynamic forces and moments - lift, side, drag, pitch, yaw, roll = self._compute_from_coefficients( + # Body-frame force components and moments straight from the body-frame + # coefficients (no wind-to-body rotation: the coefficients already live + # in the body frame). ``alpha``/``beta`` are still passed to the + # coefficients, they just no longer rotate the force. + R1, R2, R3, pitch, yaw, roll = self._compute_from_coefficients( rho, stream_speed, alpha, @@ -574,25 +827,6 @@ def compute_forces_and_moments( beta_dot, ) - # Conversion from the aerodynamic frame to the body frame. This is the - # direction cosine matrix (DCM) that expresses the aerodynamic-frame - # force components in the body frame, i.e. rotations by ``-alpha`` about - # x and ``+beta`` about y. - rotation_matrix = Matrix( - [ - [1, 0, 0], - [0, math.cos(alpha), math.sin(alpha)], - [0, -math.sin(alpha), math.cos(alpha)], - ] - ) @ Matrix( - [ - [math.cos(beta), 0, math.sin(beta)], - [0, 1, 0], - [-math.sin(beta), 0, math.cos(beta)], - ] - ) - R1, R2, R3 = rotation_matrix @ Vector([side, -lift, -drag]) - # Dislocation of the aerodynamic application point to CDM M1, M2, M3 = Vector([pitch, yaw, roll]) + (cp ^ Vector([R1, R2, R3])) diff --git a/rocketpy/rocket/aero_surface/linear_generic_surface.py b/rocketpy/rocket/aero_surface/linear_generic_surface.py index 8bf2476ef..ee92f5cf2 100644 --- a/rocketpy/rocket/aero_surface/linear_generic_surface.py +++ b/rocketpy/rocket/aero_surface/linear_generic_surface.py @@ -4,10 +4,14 @@ from rocketpy.rocket.aero_surface.generic_surface import GenericSurface +# TODO: review note: ControllableGenericSurface should also be able to be modelled +# based on LinearGenericSurface.... class LinearGenericSurface(GenericSurface): - """Class that defines a generic linear aerodynamic surface. This class is - used to define aerodynamic surfaces that have aerodynamic coefficients - defined as linear functions of the coefficients derivatives.""" + """An aerodynamic surface whose forces and moments vary linearly with the + flow angles and the rotation rates. Instead of full coefficient tables, you + give the coefficient *derivatives* (slopes) -- for example how much the normal + force changes per radian of angle of attack -- and the surface adds them up + linearly.""" def __init__( self, @@ -16,6 +20,8 @@ def __init__( coefficients, center_of_pressure=(0, 0, 0), name="Generic Linear Surface", + interpolation=None, + extrapolation=None, ): """Create a generic linear aerodynamic surface, defined by its aerodynamic coefficients derivatives. This surface is used to model any @@ -42,59 +48,67 @@ def __init__( Reference length of the aerodynamic surface. Has the unit of meters. Commonly defined as the rocket's diameter. coefficients: dict, optional - List of coefficients. If a coefficient is omitted, it is set to 0. - The valid coefficients are:\n - cL_0: callable, str, optional - Coefficient of lift at zero angle of attack. Default is 0.\n - cL_alpha: callable, str, optional - Coefficient of lift derivative with respect to angle of attack. + The coefficient derivatives (slopes), by name. Any you leave out are + set to 0. Each one can be a constant, a function, or a path to a data + file, and says how one force or moment coefficient changes with one + variable (angle in radians, or a non-dimensional rotation rate). The + names follow the pattern ``_``: the coefficient + is normal force ``cN``, side force ``cY``, axial force ``cA``, pitch moment ``cm``, + yaw moment ``cn`` or roll moment ``cl``; the variable is ``0`` (the + value at zero angle of attack, zero sideslip and zero rates), + ``alpha``, ``beta``, ``p`` (roll rate), ``q`` (pitch rate) or ``r`` + (yaw rate). The full list is:\n + cN_0: callable, str, optional + Coefficient of normal force at zero angle of attack. Default is 0.\n + cN_alpha: callable, str, optional + Coefficient of normal force derivative with respect to angle of attack. Default is 0.\n - cL_beta: callable, str, optional - Coefficient of lift derivative with respect to sideslip angle. + cN_beta: callable, str, optional + Coefficient of normal force derivative with respect to sideslip angle. Default is 0.\n - cL_p: callable, str, optional - Coefficient of lift derivative with respect to roll rate. + cN_p: callable, str, optional + Coefficient of normal force derivative with respect to roll rate. Default is 0.\n - cL_q: callable, str, optional - Coefficient of lift derivative with respect to pitch rate. + cN_q: callable, str, optional + Coefficient of normal force derivative with respect to pitch rate. Default is 0.\n - cL_r: callable, str, optional - Coefficient of lift derivative with respect to yaw rate. + cN_r: callable, str, optional + Coefficient of normal force derivative with respect to yaw rate. Default is 0.\n - cQ_0: callable, str, optional + cY_0: callable, str, optional Coefficient of side force at zero angle of attack. Default is 0.\n - cQ_alpha: callable, str, optional + cY_alpha: callable, str, optional Coefficient of side force derivative with respect to angle of attack. Default is 0.\n - cQ_beta: callable, str, optional + cY_beta: callable, str, optional Coefficient of side force derivative with respect to sideslip angle. Default is 0.\n - cQ_p: callable, str, optional + cY_p: callable, str, optional Coefficient of side force derivative with respect to roll rate. Default is 0.\n - cQ_q: callable, str, optional + cY_q: callable, str, optional Coefficient of side force derivative with respect to pitch rate. Default is 0.\n - cQ_r: callable, str, optional + cY_r: callable, str, optional Coefficient of side force derivative with respect to yaw rate. Default is 0.\n - cD_0: callable, str, optional - Coefficient of drag at zero angle of attack. Default is 0.\n - cD_alpha: callable, str, optional - Coefficient of drag derivative with respect to angle of attack. + cA_0: callable, str, optional + Coefficient of axial force at zero angle of attack. Default is 0.\n + cA_alpha: callable, str, optional + Coefficient of axial force derivative with respect to angle of attack. Default is 0.\n - cD_beta: callable, str, optional - Coefficient of drag derivative with respect to sideslip angle. + cA_beta: callable, str, optional + Coefficient of axial force derivative with respect to sideslip angle. Default is 0.\n - cD_p: callable, str, optional - Coefficient of drag derivative with respect to roll rate. + cA_p: callable, str, optional + Coefficient of axial force derivative with respect to roll rate. Default is 0.\n - cD_q: callable, str, optional - Coefficient of drag derivative with respect to pitch rate. + cA_q: callable, str, optional + Coefficient of axial force derivative with respect to pitch rate. Default is 0.\n - cD_r: callable, str, optional - Coefficient of drag derivative with respect to yaw rate. + cA_r: callable, str, optional + Coefficient of axial force derivative with respect to yaw rate. Default is 0.\n cm_0: callable, str, optional Coefficient of pitch moment at zero angle of attack. @@ -154,8 +168,31 @@ def __init__( Application point of the aerodynamic forces and moments. The center of pressure is defined in the local coordinate system of the aerodynamic surface. The default value is (0, 0, 0). - name : str - Name of the aerodynamic surface. Default is 'GenericSurface'. + name : str, optional + Name of the aerodynamic surface. Default is 'Generic Linear + Surface'. + interpolation : str or dict, optional + How tabulated coefficient derivatives interpolate between points. + The accepted methods depend on the coefficient's dimensionality: a + 1-D table (e.g. a Mach-only curve) accepts ``"linear"``, ``"akima"``, + ``"spline"`` and ``"polynomial"``; a multi-dimensional scattered + table accepts ``"linear"``, ``"shepard"`` and ``"rbf"``; and a + multi-dimensional table on a regular Cartesian grid accepts + ``"linear"``, ``"nearest"``, ``"slinear"``, ``"cubic"``, + ``"quintic"`` and ``"pchip"`` (with ``"spline"`` mapped to + ``"cubic"`` and ``"akima"`` to ``"pchip"``). Accepts either a simple + string or a dict keyed by coefficient name (names left out fall back + to the default). ``None`` (the default) uses ``"linear"`` for tables + built here and keeps a pre-built ``Function``'s own setting. + extrapolation : str or dict, optional + How tabulated coefficient derivatives behave outside their data + range: ``"constant"`` holds the value at the nearest data edge, + ``"natural"`` keeps following the curve, and ``"zero"`` returns 0. + Accepts either a simple string or a dict keyed by coefficient name + (names left out fall back to the default). ``None`` (the default) + uses ``"constant"`` for tables built here and keeps whatever a + pre-built ``Function`` already carries. Only affects tabulated + sources (constants and callables are evaluated directly). """ super().__init__( @@ -164,6 +201,8 @@ def __init__( coefficients=coefficients, center_of_pressure=center_of_pressure, name=name, + extrapolation=extrapolation, + interpolation=interpolation, ) self.compute_all_coefficients() @@ -171,28 +210,17 @@ def __init__( self.prints = _LinearGenericSurfacePrints(self) self.plots = _LinearGenericSurfacePlots(self) - def _evaluate_derived_coefficients(self): - """Exact override of the diagnostic cp accessors. The linear model - already exposes the forcing derivatives ``cL_alpha``/``cm_alpha`` (pitch) - and ``cQ_beta``/``cn_beta`` (yaw), so the slopes are read directly - (frozen at zero alpha/beta/rates) instead of being recovered by - numerical differentiation. Damping derivatives (``_p/_q/_r``) are - intentionally excluded from the stability cp. - """ + def _evaluate_stability_derivatives(self): + """Build the center-of-pressure accessors for the linear model. - def _at_zero(coefficient, name): - return Function( - lambda mach: coefficient(0.0, 0.0, mach, 0.0, 0.0, 0.0, 0.0), - "Mach", - name, - ) - - self._set_derived_cp_accessors( - _at_zero(self.cL_alpha, "cL_alpha"), - _at_zero(self.cm_alpha, "cm_alpha"), - _at_zero(self.cQ_beta, "cQ_beta"), - _at_zero(self.cn_beta, "cn_beta"), - ) + The linear model already stores the coefficient derivatives + ``cN_alpha``/``cm_alpha`` (pitch) and ``cY_beta``/``cn_beta`` (yaw) as + the surface's own coefficients, so there is nothing to differentiate: + :meth:`_set_stability_accessors` reads them directly (evaluated at zero + alpha/beta and zero rates). Damping derivatives (``_p/_q/_r``) are + intentionally excluded from the stability center of pressure. + """ + self._set_stability_accessors() def _get_default_coefficients(self): """Returns default coefficients @@ -204,24 +232,24 @@ def _get_default_coefficients(self): are the default values. """ default_coefficients = { - "cL_0": 0, - "cL_alpha": 0, - "cL_beta": 0, - "cL_p": 0, - "cL_q": 0, - "cL_r": 0, - "cQ_0": 0, - "cQ_alpha": 0, - "cQ_beta": 0, - "cQ_p": 0, - "cQ_q": 0, - "cQ_r": 0, - "cD_0": 0, - "cD_alpha": 0, - "cD_beta": 0, - "cD_p": 0, - "cD_q": 0, - "cD_r": 0, + "cN_0": 0, + "cN_alpha": 0, + "cN_beta": 0, + "cN_p": 0, + "cN_q": 0, + "cN_r": 0, + "cY_0": 0, + "cY_alpha": 0, + "cY_beta": 0, + "cY_p": 0, + "cY_q": 0, + "cY_r": 0, + "cA_0": 0, + "cA_alpha": 0, + "cA_beta": 0, + "cA_p": 0, + "cA_q": 0, + "cA_r": 0, "cm_0": 0, "cm_alpha": 0, "cm_beta": 0, @@ -262,6 +290,21 @@ def compute_forcing_coefficient(self, c_0, c_alpha, c_beta): terms that are identically zero are skipped entirely. For a Barrowman surface each forcing coefficient has at most one non-zero derivative, so this typically collapses to a single source call (or to a constant 0). + + Parameters + ---------- + c_0 : AeroCoefficient + Zero-angle derivative (constant term). + c_alpha : AeroCoefficient + Derivative with respect to the angle of attack ``alpha``. + c_beta : AeroCoefficient + Derivative with respect to the sideslip angle ``beta``. + + Returns + ------- + Function + Coefficient as a function of the independent variables + ``(alpha, beta, mach, reynolds, pitch_rate, yaw_rate, roll_rate)``. """ has_0 = not getattr(c_0, "is_zero_coefficient", False) has_alpha = not getattr(c_alpha, "is_zero_coefficient", False) @@ -311,6 +354,21 @@ def compute_damping_coefficient(self, c_p, c_q, c_r): non-zero terms (see :meth:`compute_forcing_coefficient`). For a Barrowman surface only ``cl_p`` (roll damping) is non-zero, so most damping coefficients collapse to a constant 0. + + Parameters + ---------- + c_p : AeroCoefficient + Derivative with respect to the roll rate ``roll_rate``. + c_q : AeroCoefficient + Derivative with respect to the pitch rate ``pitch_rate``. + c_r : AeroCoefficient + Derivative with respect to the yaw rate ``yaw_rate``. + + Returns + ------- + Function + Coefficient as a function of the independent variables + ``(alpha, beta, mach, reynolds, pitch_rate, yaw_rate, roll_rate)``. """ has_p = not getattr(c_p, "is_zero_coefficient", False) has_q = not getattr(c_q, "is_zero_coefficient", False) @@ -360,20 +418,20 @@ def total_coefficient( def compute_all_coefficients(self): """Compute all the aerodynamic coefficients from the derivatives.""" # pylint: disable=invalid-name - self.cLf = self.compute_forcing_coefficient( - self.cL_0, self.cL_alpha, self.cL_beta + self.cNf = self.compute_forcing_coefficient( + self.cN_0, self.cN_alpha, self.cN_beta ) - self.cLd = self.compute_damping_coefficient(self.cL_p, self.cL_q, self.cL_r) + self.cNd = self.compute_damping_coefficient(self.cN_p, self.cN_q, self.cN_r) - self.cQf = self.compute_forcing_coefficient( - self.cQ_0, self.cQ_alpha, self.cQ_beta + self.cYf = self.compute_forcing_coefficient( + self.cY_0, self.cY_alpha, self.cY_beta ) - self.cQd = self.compute_damping_coefficient(self.cQ_p, self.cQ_q, self.cQ_r) + self.cYd = self.compute_damping_coefficient(self.cY_p, self.cY_q, self.cY_r) - self.cDf = self.compute_forcing_coefficient( - self.cD_0, self.cD_alpha, self.cD_beta + self.cAf = self.compute_forcing_coefficient( + self.cA_0, self.cA_alpha, self.cA_beta ) - self.cDd = self.compute_damping_coefficient(self.cD_p, self.cD_q, self.cD_r) + self.cAd = self.compute_damping_coefficient(self.cA_p, self.cA_q, self.cA_r) self.cmf = self.compute_forcing_coefficient( self.cm_0, self.cm_alpha, self.cm_beta @@ -390,11 +448,12 @@ def compute_all_coefficients(self): ) self.cld = self.compute_damping_coefficient(self.cl_p, self.cl_q, self.cl_r) - self.cL = self.cLf - self.cQ = self.cQf - self.cD = self.cDf + self.cN = self.cNf + self.cY = self.cYf + self.cA = self.cAf self.cm = self.cmf self.cn = self.cnf + self.cl = self.clf def _compute_from_coefficients( self, @@ -436,38 +495,38 @@ def _compute_from_coefficients( Non-dimensional (reduced) yaw rate, ``r * L_ref / (2 * V)``. roll_rate : float Non-dimensional (reduced) roll rate, ``p * L_ref / (2 * V)``. + alpha_dot : float, optional + Non-dimensional angle-of-attack rate. Ignored by the linear model; + accepted for signature compatibility. Defaults to 0. + beta_dot : float, optional + Non-dimensional sideslip-angle rate. Ignored by the linear model; + accepted for signature compatibility. Defaults to 0. Returns ------- tuple of float - The aerodynamic forces (lift, side_force, drag) and moments - (pitch, yaw, roll) in the body frame. + The body-frame force components ``(R1, R2, R3)`` and the moments + ``(pitch, yaw, roll)``. """ - # Precompute common values. The angular rates arrive already - # non-dimensionalized (reduced rates, e.g. ``q* = q * L_ref / (2 * V)``), - # so the rate-damping terms use the same dynamic-pressure scaling as the - # forcing terms: the ``L_ref / (2 * V)`` factor now lives in the rate - # itself, not in the scaling. (Algebraically identical to the previous - # ``0.5 * rho * V * A * L / 2`` damping scaling applied to raw rates.) + # Precompute common values dyn_pressure_area = 0.5 * rho * stream_speed**2 * self.reference_area dyn_pressure_area_length = dyn_pressure_area * self.reference_length - - # Evaluate the composed coefficients through the fast, unvalidated - # ``get_value_opt`` path (the composed coefficients are callable-source - # Functions, so this calls the closure directly, skipping the per-call - # ``__call__``/``get_value`` argument validation in the hot loop). args = (alpha, beta, mach, reynolds, pitch_rate, yaw_rate, roll_rate) - # Compute aerodynamic forces (forcing + reduced-rate damping) - lift = dyn_pressure_area * ( - self.cLf.get_value_opt(*args) + self.cLd.get_value_opt(*args) + # Body-frame forces (forcing + reduced-rate damping), straight from the + # body-frame coefficients: normal cN, side cY, axial cA. + normal = dyn_pressure_area * ( + self.cNf.get_value_opt(*args) + self.cNd.get_value_opt(*args) ) - side = dyn_pressure_area * ( - self.cQf.get_value_opt(*args) + self.cQd.get_value_opt(*args) + yaw_side = dyn_pressure_area * ( + self.cYf.get_value_opt(*args) + self.cYd.get_value_opt(*args) ) - drag = dyn_pressure_area * ( - self.cDf.get_value_opt(*args) + self.cDd.get_value_opt(*args) + axial = dyn_pressure_area * ( + self.cAf.get_value_opt(*args) + self.cAd.get_value_opt(*args) ) + r1 = yaw_side + r2 = -normal + r3 = -axial # Compute aerodynamic moments (forcing + reduced-rate damping) pitch = dyn_pressure_area_length * ( @@ -480,4 +539,4 @@ def _compute_from_coefficients( self.clf.get_value_opt(*args) + self.cld.get_value_opt(*args) ) - return lift, side, drag, pitch, yaw, roll + return r1, r2, r3, pitch, yaw, roll diff --git a/rocketpy/rocket/aero_surface/nose_cone.py b/rocketpy/rocket/aero_surface/nose_cone.py index a0c0507e7..7f17c159b 100644 --- a/rocketpy/rocket/aero_surface/nose_cone.py +++ b/rocketpy/rocket/aero_surface/nose_cone.py @@ -65,11 +65,12 @@ class NoseCone(_BarrowmanSurface): Nose cone local center of pressure z coordinate. Has units of length and is given in meters. NoseCone.cl : Function - Function which defines the lift coefficient as a function of the angle - of attack and the Mach number. Takes as input the angle of attack in - radians and the Mach number. Returns the lift coefficient. + Roll-moment coefficient, inherited from the generic-surface model + (a function of the flow variables). Zero for a nose cone or tail; for a + fin set it carries the cant forcing and roll damping. The lift-curve + slope is ``clalpha``. NoseCone.clalpha : float - Lift coefficient slope. Has units of 1/rad. + Normal-force coefficient slope. Has units of 1/rad. NoseCone.plots : plots.aero_surface_plots._NoseConePlots This contains all the plots methods. Use help(NoseCone.plots) to know more about it. @@ -476,12 +477,7 @@ def evaluate_lift_coefficient(self): self.clalpha = Function( lambda mach: 2 * self.radius_ratio**2, "Mach", - f"Lift coefficient derivative for {self.name}", - ) - self.cl = Function( - lambda alpha, mach: self.clalpha(mach) * alpha, - ["Alpha (rad)", "Mach"], - "Cl", + f"Normal-force coefficient derivative for {self.name}", ) def evaluate_k(self): @@ -563,15 +559,10 @@ def to_dict(self, **kwargs): } if kwargs.get("include_outputs", False): clalpha = self.clalpha - cl = self.cl if kwargs.get("discretize", False): clalpha = clalpha.set_discrete(0, 4, 50) - cl = cl.set_discrete( - (-np.pi / 6, 0), (np.pi / 6, 2), (10, 10), mutate_self=False - ) data["cp"] = self.cp data["clalpha"] = clalpha - data["cl"] = cl return data diff --git a/rocketpy/rocket/aero_surface/tail.py b/rocketpy/rocket/aero_surface/tail.py index 0066bcf86..7ccd2e1e3 100644 --- a/rocketpy/rocket/aero_surface/tail.py +++ b/rocketpy/rocket/aero_surface/tail.py @@ -41,10 +41,12 @@ class Tail(_BarrowmanSurface): Tail.cp : tuple Tuple containing the coordinates of the center of pressure of the tail. Tail.cl : Function - Function that returns the lift coefficient of the tail. The function - is defined as a function of the angle of attack and the mach number. + Roll-moment coefficient, inherited from the generic-surface model + (a function of the flow variables). Zero for a nose cone or tail; for a + fin set it carries the cant forcing and roll damping. The lift-curve + slope is ``clalpha``. Tail.clalpha : float - Lift coefficient slope. Has the unit of 1/rad. + Normal-force coefficient slope. Has the unit of 1/rad. Tail.slant_length : float Slant length of the tail. The slant length is defined as the distance between the top and bottom of the tail. The slant length is measured @@ -184,12 +186,7 @@ def evaluate_lift_coefficient(self): ) ), "Mach", - f"Lift coefficient derivative for {self.name}", - ) - self.cl = Function( - lambda alpha, mach: self.clalpha(mach) * alpha, - ["Alpha (rad)", "Mach"], - "Cl", + f"Normal-force coefficient derivative for {self.name}", ) def evaluate_center_of_pressure(self): @@ -230,18 +227,13 @@ def to_dict(self, **kwargs): if kwargs.get("include_outputs", False): clalpha = self.clalpha - cl = self.cl if kwargs.get("discretize", False): clalpha = clalpha.set_discrete(0, 4, 50) - cl = cl.set_discrete( - (-np.pi / 6, 0), (np.pi / 6, 2), (10, 10), mutate_self=False - ) data.update( { "cp": self.cp, "clalpha": clalpha, - "cl": cl, "slant_length": self.slant_length, "surface_area": self.surface_area, } diff --git a/rocketpy/rocket/rocket.py b/rocketpy/rocket/rocket.py index 48ad2fa85..821eb884d 100644 --- a/rocketpy/rocket/rocket.py +++ b/rocketpy/rocket/rocket.py @@ -35,6 +35,22 @@ ) +def _stability_slope(derivative_coefficient, sign=1.0): + """Turn a surface's coefficient derivative (``cN_alpha``, ``cY_beta``, ...) + into the force-curve slope as a Function of Mach, evaluated at zero + alpha/beta and zero rates. ``sign`` flips it when needed (the yaw plane uses + ``-cY_beta``). + """ + return Function( + lambda mach: ( + sign + * derivative_coefficient.get_value_opt(0.0, 0.0, mach, 0.0, 0.0, 0.0, 0.0) + ), + "Mach", + "Force coefficient slope", + ) + + # pylint: disable=too-many-instance-attributes, too-many-public-methods, too-many-instance-attributes class Rocket: """Keeps rocket information. @@ -388,10 +404,7 @@ def __init__( # pylint: disable=too-many-statements outputs="Stability Margin - Yaw (c)", ) - # Define aerodynamic drag coefficients used during flight simulation. - # Drag is stored at its intrinsic dimensionality over the seven base - # independent variables; 1-D inputs are taken as Mach, and "constant" - # extrapolation is used (drag should not extrapolate beyond its range). + # Define aerodynamic drag coefficients used during flight simulation self.power_off_drag_7d = AeroCoefficient( power_off_drag, name="Drag Coefficient with Power Off", @@ -444,14 +457,10 @@ def __init__( # pylint: disable=too-many-statements self.evaluate_reduced_mass() self.evaluate_thrust_to_weight() - # The aerodynamic center and the margins are evaluated lazily (see the - # ``aerodynamic_center`` / ``static_margin`` properties); just flag them - # outdated here. They are rebuilt on first access, once all surfaces and - # the motor have been added. + # The aerodynamic center and the margins are evaluated lazily self._cp_outdated = True self._margin_outdated = True - # One-shot guard for the non-axisymmetric advisory (see - # ``evaluate_center_of_pressure``); warned at most once per rocket. + # Flag for rocket non-axisymmetric warning. Used to show warning once. self._axisymmetry_warned = False # Initialize plots and prints object @@ -647,16 +656,6 @@ def evaluate_thrust_to_weight(self): self.thrust_to_weight.set_title("Thrust to Weight ratio") # Lazily-evaluated aerodynamic outputs. - # - # The pitch/yaw aerodynamic centers and the static/stability margins are - # *derived* from the aerodynamic surfaces (and, for the margins, the center - # of mass). Rather than recompute them eagerly on every ``add_*`` call - an - # O(N^2) cost while building, repeated for every rocket in a Monte Carlo run - - # the mutating methods only flag them outdated; the value is rebuilt on first - # access and cached until the next change. ``_cp_outdated`` tracks the - # surface-dependent centers; ``_margin_outdated`` additionally tracks the - # center of mass, so adding a motor refreshes the margins without recomputing - # the surface-only aerodynamic center. def _ensure_aerodynamic_center(self): """Recompute the pitch/yaw aerodynamic centers if a surface changed.""" @@ -721,25 +720,21 @@ def stability_margin_yaw(self): return self._stability_margin_yaw def evaluate_center_of_pressure(self): - """Evaluates the rocket's **aerodynamic center** as a function of Mach - number, relative to the user-defined rocket reference system. + """Evaluates the rocket's aerodynamic center (and cp_position) as a + function of Mach number, relative to the user-defined rocket reference + system. - The aerodynamic center is the linearized (small-incidence, - :math:`\\alpha=\\beta=0`) center of pressure: the normal-force-slope- - weighted average of every aerodynamic surface's location. It is the - well-conditioned reference used by the static and stability margins. The - nonlinear center of pressure at a finite angle of attack/sideslip is a - separate, singular quantity (``x_cdm + csys * d * Cm / CN``) that can be - reconstructed from :meth:`aerodynamic_coefficients_full` when needed. + The aerodynamic center is the linearized (small-incidence, alpha=beta=0) + center of pressure: the normal-force-slope-weighted average of every + aerodynamic surface's location. It is computed independently for the **pitch** plane (``aerodynamic_center``, from the normal-force/pitch-moment slopes) and the **yaw** plane (``aerodynamic_center_yaw``, from the side-force/yaw-moment slopes). For an axisymmetric rocket the two - coincide; when they differ (a non-axisymmetric configuration, only - expressible through ``GenericSurface``), a warning is raised because the - scalar ``static_margin``/``stability_margin`` attributes describe the - pitch plane only. + coincide. When they differ (a non-axisymmetric configuration), a warning + is raised because the scalar ``static_margin``/``stability_margin`` + attributes describe the pitch plane only. Returns ------- @@ -764,8 +759,15 @@ def evaluate_center_of_pressure(self): # Calculate total force coefficient derivatives and aerodynamic center if len(self.aerodynamic_surfaces) > 0: for aero_surface, position in self.aerodynamic_surfaces: - lift_coeff_der = aero_surface.lift_coefficient_derivative + # Force-curve slopes as Functions of Mach, from the surface's + # coefficient derivatives evaluated at zero alpha/beta and zero + # rates. The yaw slope is the sign-flipped ``cY_beta`` so an + # axisymmetric surface gives the same signed weight as the pitch + # plane (their margins then coincide when symmetric). + lift_coeff_der = _stability_slope(aero_surface.cN_alpha) + side_coeff_der = _stability_slope(aero_surface.cY_beta, sign=-1.0) cp_z = aero_surface.center_of_pressure_z + cp_z_yaw = aero_surface.center_of_pressure_z_yaw # ref_factor corrects force for different reference areas ref_factor = aero_surface.reference_area / self.area self._total_lift_coeff_der += ref_factor * lift_coeff_der @@ -774,8 +776,6 @@ def evaluate_center_of_pressure(self): ) # Yaw plane. - side_coeff_der = aero_surface.side_coefficient_derivative - cp_z_yaw = aero_surface.center_of_pressure_z_yaw self._total_side_coeff_der += ref_factor * side_coeff_der self._aerodynamic_center_yaw += ( ref_factor * side_coeff_der * (position.z - self._csys * cp_z_yaw) @@ -786,12 +786,8 @@ def evaluate_center_of_pressure(self): if self._total_side_coeff_der.get_value(0) != 0: self._aerodynamic_center_yaw /= self._total_side_coeff_der - # One-shot non-axisymmetry advisory. Both plane centers are already built - # here, so detection costs only a Mach sweep -- no extra evaluation and no - # per-surface-add repetition. ``_cp_outdated`` was cleared at the top, so - # reading ``is_axisymmetric`` (which reads the centers) does not recurse. - # Emitted at most once per rocket, when the scalar pitch-plane margins - # first become potentially misleading. + # Non-axisymmetry advisory. Latched once per configuration: the flag is + # re-armed whenever a surface is added (see add_surfaces) if not self._axisymmetry_warned and not self.is_axisymmetric: self._axisymmetry_warned = True max_diff = self._cp_plane_max_difference() @@ -799,7 +795,7 @@ def evaluate_center_of_pressure(self): "Pitch- and yaw-plane aerodynamic centers differ " f"(max difference ~{max_diff:.4g} m): the rocket is not " "axisymmetric. 'aerodynamic_center', 'static_margin' and " - "'stability_margin' describe the PITCH plane; use " + "'stability_margin' describe the PITCH plane. Use " "'aerodynamic_center_yaw', 'static_margin_yaw' and " "'stability_margin_yaw' for the yaw plane.", stacklevel=2, @@ -809,19 +805,7 @@ def evaluate_center_of_pressure(self): def _cp_plane_max_difference(self): """Largest pitch- vs yaw-plane aerodynamic center difference, in meters. - - The difference is sampled densely across the subsonic, transonic and - supersonic regimes rather than at a few fixed Mach numbers. A - non-axisymmetric configuration (only possible through a ``GenericSurface`` - with non-mirror coefficients) can have its pitch/yaw aerodynamic centers - diverge in any Mach range, and the difference can vanish at isolated Mach - numbers; sparse fixed sampling (e.g. only 0, 0.5, 1) risks a *false - negative* -- silently reporting an asymmetric rocket as axisymmetric, so - the user trusts the pitch-plane-only static margin. The built-in - (Barrowman) surfaces are symmetric by construction, so this returns - exactly 0 for them at every Mach (no false positives). This runs once at - setup, not in the integration loop, so a dense sweep is cheap. - """ + The difference is sampled densely across the subsonic, transonic and""" # 0 to 3 in 0.2 steps covers RocketPy's flight regimes (sub/trans/ # supersonic) with enough resolution that a real asymmetry, which spans a # Mach *range*, cannot fall entirely between sample points. This only @@ -842,13 +826,9 @@ def is_axisymmetric(self): coincide (to caliber-scale tolerance). When ``False`` the rocket is not axisymmetric: ``aerodynamic_center``, ``static_margin`` and ``stability_margin`` describe the PITCH plane only and differ from their - ``*_yaw`` counterparts (``aerodynamic_center_yaw``, ``static_margin_yaw``, - ``stability_margin_yaw``).""" - # Fast path: the built-in nose, tail and fin sets contribute identically - # to the pitch and yaw planes by construction, so a rocket made only of - # surfaces that are axisymmetric-by-construction is axisymmetric without - # evaluating anything. Only a generic surface or an individual fin can - # break it, in which case fall back to the numeric Mach sweep below. + ``*_yaw`` counterparts (``aerodynamic_center_yaw``, + ``static_margin_yaw``, ``stability_margin_yaw``).""" + # Nose, tail and fin sets contribute identically to both planes. if all( getattr(surface, "is_axisymmetric", False) for surface, _ in self.aerodynamic_surfaces @@ -865,13 +845,15 @@ def cp_position(self): Mach-dependent aerodynamic center -- the slope-weighted (Barrowman) quantity the rocketry community conventionally calls the CP, and the well-conditioned reference used by the static and stability margins. - The genuine, force-application center of pressure at a finite incidence - is ``x_cdm + csys * d * Cm / CN`` and can be reconstructed on demand from - :meth:`aerodynamic_coefficients_full`; it is intentionally not exposed as - a method because it is singular at zero normal force (zero incidence). """ + # TODO: review note: I guess having the full, real nonlinear cp would be cool and good for completeness, althouhg not that useful ... should try it return self.aerodynamic_center + # TODO: review note: why are the bellow functions related to aerodynamic forces + # not using rates? What I want from this is to define a complete set of the + # entire vehicle aerodynamic coefficients. And make it as complete as possible + # than make some helper analysis functions to get something like the version + # without rates, etc. Could that be done? def _aerodynamic_forces_and_moments(self, alpha, beta, mach, reynolds=0.0): """Total body-frame aerodynamic force ``(R1, R2, R3)`` and moment ``(M1, M2, M3)`` about the center of dry mass, summed over every @@ -964,15 +946,16 @@ def aerodynamic_coefficients_full(self, alpha, beta, mach, reynolds=0.0): coefficients, referenced to the rocket cross-section area and diameter and taken about the center of dry mass, in the body aerodynamic frame: - - ``cL`` (lift), ``cQ`` (side force), ``cD`` (drag); + - ``cN`` (normal force), ``cY`` (side force), ``cA`` (axial force); - ``cm`` (pitch), ``cn`` (yaw), ``cl`` (roll). - Unlike :meth:`aerodynamic_coefficients` (which returns unsigned - normal-force and pitch-moment magnitudes) these are signed and complete. - The drag coefficient ``cD`` is taken from the vehicle drag curve - (``power_off_drag``), since the geometric surfaces carry no drag - coefficient; this unifies the per-surface lift/moment model with the - separately supplied drag curve into a single coefficient set. + These are body-frame, signed and complete, unlike + :meth:`aerodynamic_coefficients` (which returns unsigned normal-force and + pitch-moment magnitudes). The axial coefficient ``cA`` is taken from the + vehicle drag curve (``power_off_drag``), since the geometric surfaces + carry no axial coefficient; this unifies the per-surface normal-force / + moment model with the separately supplied drag curve into a single + coefficient set. Parameters ---------- @@ -986,23 +969,22 @@ def aerodynamic_coefficients_full(self, alpha, beta, mach, reynolds=0.0): Returns ------- dict - ``{"cL", "cQ", "cD", "cm", "cn", "cl"}``. + ``{"cN", "cY", "cA", "cm", "cn", "cl"}``. """ r1, r2, r3, m1, m2, m3, stream_speed = self._aerodynamic_forces_and_moments( alpha, beta, mach, reynolds ) dynamic_pressure_area = 0.5 * stream_speed**2 * self.area if dynamic_pressure_area == 0: - return {c: 0.0 for c in ("cL", "cQ", "cD", "cm", "cn", "cl")} + return {c: 0.0 for c in ("cN", "cY", "cA", "cm", "cn", "cl")} reference_length = 2 * self.radius dynamic_pressure_area_length = dynamic_pressure_area * reference_length - # Body-frame force/moment components map to the aerodynamic-frame - # coefficients (see GenericSurface.compute_forces_and_moments, which - # builds the body force from Vector([side, -lift, -drag])). + # Body-frame force components: R1 = cY, R2 = -cN, R3 = -cA (see + # GenericSurface.compute_forces_and_moments). return { - "cL": -r2 / dynamic_pressure_area, - "cQ": r1 / dynamic_pressure_area, - "cD": self.power_off_drag_by_mach.get_value_opt(mach), + "cN": -r2 / dynamic_pressure_area, + "cY": r1 / dynamic_pressure_area, + "cA": self.power_off_drag_by_mach.get_value_opt(mach), "cm": m1 / dynamic_pressure_area_length, "cn": m2 / dynamic_pressure_area_length, "cl": m3 / dynamic_pressure_area_length, @@ -1035,10 +1017,11 @@ def __evaluate_single_surface_cp_to_cdm(self, surface, position): (position.z - self.center_of_dry_mass_position) * self._csys, ] ) - # position of the force application point in body frame. Surfaces that - # carry their center-of-pressure offset in the moment coefficients - # (Barrowman surfaces) apply the force at the origin; surfaces that - # transport the moment geometrically use their center of pressure. + # position of the force application point in body frame. Every surface + # applies its resultant force at its center of pressure and transports + # the moment geometrically; the surface-local application point is mapped + # into the body frame by ``_rotation_surface_to_body`` (identity for a + # generic surface, a 180-degree flip for Barrowman surfaces). application_point = getattr( surface, "force_application_point", @@ -1465,8 +1448,7 @@ def add_motor(self, motor, position): # pylint: disable=too-many-statements self.evaluate_reduced_mass() self.evaluate_thrust_to_weight() self.evaluate_surfaces_cp_to_cdm() - # The motor changes the center of mass (and thus the margins) but not the - # surface-only aerodynamic center; flag only the margins for lazy rebuild. + # The motor changes the CM (and the margins) self._margin_outdated = True self.evaluate_com_to_cdm_function() self.evaluate_nozzle_gyration_tensor() @@ -1546,14 +1528,18 @@ def add_surfaces(self, surfaces, positions): else: self.__add_single_surface(surfaces, positions) - # Adding a surface changes both the aerodynamic center and the margins; - # flag them for lazy rebuild on next access (see the properties). The - # non-axisymmetry advisory is emitted (once) from - # ``evaluate_center_of_pressure`` on that first rebuild, rather than - # eagerly here on every add. + # Adding a surface changes both the aerodynamic center and the margins self._cp_outdated = True self._margin_outdated = True + # Re-arm the non-axisymmetry advisory: the warning is latched once per + # configuration (see evaluate_center_of_pressure), so a new surface may + # legitimately warn again about the new configuration. + self._axisymmetry_warned = False + # TODO: review note: several issues with this. First, the name is bad + # second, there should be an overwrite option, so it overwrites any existing + # surfaces on the rocket (even if a surface is added AFTER this method is called). + # TODO: review note: how can power on/power off be considered here? def add_vehicle_aerodynamic_surface( self, coefficients, reference_position=None, name="Vehicle Aerodynamics" ): diff --git a/rocketpy/simulation/flight.py b/rocketpy/simulation/flight.py index 7c631753b..b5b8e7723 100644 --- a/rocketpy/simulation/flight.py +++ b/rocketpy/simulation/flight.py @@ -609,6 +609,10 @@ def __init__( # pylint: disable=too-many-arguments,too-many-statements A custom ``scipy.integrate.OdeSolver`` can be passed as well. For more information on the integration methods, see the scipy documentation [1]_. + simulation_mode : str, optional + Degrees of freedom used to integrate the trajectory. Either "6DOF" + (full translational and rotational dynamics) or "3DOF" (point-mass + translation only). Default is "6DOF". custom_events : Event or list[Event], optional Event or list of Events to be monitored during flight. See Event class for more details. Default is None. @@ -2307,19 +2311,16 @@ def static_margin(self): def stability_margin(self): """Linear stability margin along the flight, in calibers. - This is the classical (aerodynamic-center) margin: it evaluates the - rocket's linearized stability margin - (:meth:`Rocket.stability_margin`) at the realized flight Mach and time at - each instant, capturing the Mach variation of the aerodynamic center - together with the center-of-mass shift as propellant burns. It is - well-conditioned and never spikes. + This is the classical margin: it evaluates the rocket's linearized + stability margin (:meth:`Rocket.stability_margin`) at the realized + flight Mach and time at each instant, capturing the Mach variation of + the aerodynamic center together with the center-of-mass shift as + propellant burns. Returns ------- stability : rocketpy.Function - Stability margin in calibers as a function of time. A positive - margin (aerodynamic center behind the center of mass) is the classic - passive-stability condition. + Stability margin in calibers as a function of time. """ return [(t, self.rocket.stability_margin(m, t)) for t, m in self.mach_number] @@ -2330,8 +2331,8 @@ def stability_margin_yaw(self): Yaw-plane counterpart of :meth:`stability_margin`, using the rocket's yaw-plane aerodynamic center (:meth:`Rocket.stability_margin_yaw`). Equals :meth:`stability_margin` for an axisymmetric rocket; for a - non-axisymmetric rocket (e.g. single-plane canards) it differs, since the - pitch and yaw aerodynamic centers no longer coincide. + non-axisymmetric rocket (e.g. single-plane canards) it differs, since + the pitch and yaw aerodynamic centers no longer coincide. Returns ------- @@ -2343,6 +2344,8 @@ def stability_margin_yaw(self): ] # Dynamic stability + # TODO: review note: the two methods below this comment needs to have its + # equations documented in a .rst file def _lateral_inertia(self, dry_lateral_inertia, motor_lateral_inertia): """Lateral moment of inertia about the instantaneous center of mass, as an array over ``self.time``. Uses the reduced-mass formulation of the @@ -2369,9 +2372,9 @@ def _lateral_inertia(self, dry_lateral_inertia, motor_lateral_inertia): def _dynamic_stability(self, lift_slope, stability_margin, lateral_inertia): """Linearized oscillator coefficients for one plane, as arrays over - ``self.time``: corrective moment coefficient ``C1`` (restoring moment per - radian), damping moment coefficient ``C2`` (aerodynamic + jet), undamped - natural frequency ``omega_n`` and damping ratio ``zeta``. + ``self.time``: corrective moment coefficient ``C1`` (restoring moment + per radian), damping moment coefficient ``C2`` (aerodynamic + jet), + undamped natural frequency ``omega_n`` and damping ratio ``zeta``. ``lift_slope`` is the rocket's total normal-force-curve slope for the plane (``total_lift_coeff_der`` for pitch, ``total_side_coeff_der`` for @@ -2407,7 +2410,9 @@ def _dynamic_stability(self, lift_slope, stability_margin, lateral_inertia): # Aerodynamic damping: 0.5 rho V A sum_i (A_i/A) C_Nalpha_i arm_i^2. damping_aero = 0.0 for surface, position in self.rocket.aerodynamic_surfaces: - slope = surface.lift_coefficient_derivative.get_value_opt(mach) + slope = surface.cN_alpha.get_value_opt( + 0.0, 0.0, mach, 0.0, 0.0, 0.0, 0.0 + ) cp_position = ( position.z - csys * surface.center_of_pressure_z.get_value_opt(mach) ) diff --git a/tests/fixtures/generic_surfaces/linear_generic_surfaces_fixtures.py b/tests/fixtures/generic_surfaces/linear_generic_surfaces_fixtures.py index 35ab9c1a2..600325391 100644 --- a/tests/fixtures/generic_surfaces/linear_generic_surfaces_fixtures.py +++ b/tests/fixtures/generic_surfaces/linear_generic_surfaces_fixtures.py @@ -13,7 +13,7 @@ def filename_valid_coeff_linear_generic_surface(tmpdir_factory): { "alpha": [0, 1, 2, 3, 0.1], "mach": [3, 2, 1, 0, 0.2], - "cL_0": [4, 2, 2, 4, 5], + "cN_0": [4, 2, 2, 4, 5], } ).to_csv(filename, index=False) @@ -24,7 +24,7 @@ def filename_valid_coeff_linear_generic_surface(tmpdir_factory): params=( { "alpha": [0, 1, 2, 3, 0.1], - "cL_0": [4, 2, 2, 4, 5], + "cN_0": [4, 2, 2, 4, 5], "mach": [3, 2, 1, 0, 0.2], }, { diff --git a/tests/unit/mathutils/test_function.py b/tests/unit/mathutils/test_function.py index 93c439def..e2853f1df 100644 --- a/tests/unit/mathutils/test_function.py +++ b/tests/unit/mathutils/test_function.py @@ -1505,3 +1505,90 @@ def test_regular_grid_invalid_source_raises(bad_source, match): outputs=["z"], interpolation="regular_grid", ) + + +def test_regular_grid_sorts_unsorted_axes(): + """A descending (or shuffled) axis is sorted, with the grid data reordered + to match, so the resulting Function matches the equivalent ascending grid.""" + x_axis = np.array([0.0, 1.0, 2.0]) + y_axis = np.array([0.0, 1.0, 2.0]) + x_grid, y_grid = np.meshgrid(x_axis, y_axis, indexing="ij") + data = 2.0 * x_grid + 3.0 * y_grid + + ascending = Function( + ([x_axis, y_axis], data), interpolation="regular_grid", extrapolation="natural" + ) + # First axis descending, data reversed along that axis to describe the SAME + # surface. It must be normalized to ascending and yield identical values. + descending = Function( + ([x_axis[::-1], y_axis], data[::-1, :]), + interpolation="regular_grid", + extrapolation="natural", + ) + assert np.all(np.diff(descending._grid_axes[0]) > 0) + assert np.isclose(descending(1.5, 0.5), ascending(1.5, 0.5)) + + +def test_regular_grid_repeated_axis_coordinate_raises(): + """An axis with duplicate coordinates cannot form a grid and raises a clear + error instead of a cryptic SciPy failure.""" + with pytest.raises(ValueError, match="repeated coordinates"): + Function( + ([np.array([0.0, 1.0, 1.0]), np.array([0.0, 1.0, 2.0])], np.ones((3, 3))), + interpolation="regular_grid", + ) + + +def test_from_regular_grid_csv_falls_back_when_too_few_points(tmp_path): + """A smooth grid method (e.g. cubic) needs enough points per axis; when the + grid is too coarse, from_regular_grid_csv warns and falls back to linear.""" + filename = tmp_path / "coarse_grid.csv" + # 2x2 grid: too few points for cubic (which needs 4 per axis). + filename.write_text("mach,alpha,cL\n0,0,0\n0,1,1\n1,0,1\n1,1,2\n", encoding="utf-8") + with pytest.warns(UserWarning, match="falling back to 'linear'"): + func = Function.from_regular_grid_csv( + str(filename), + ["mach", "alpha"], + "cL", + extrapolation="constant", + interpolation="cubic", + ) + assert func is not None + assert getattr(func, "_grid_method", "linear") == "linear" + + +def test_regular_grid_caches_domain_bounds(bilinear_grid_2d): + """The N-D hot path caches per-dimension domain bounds at source time.""" + assert np.allclose(bilinear_grid_2d._domain_min, [0.0, 0.0]) + assert np.allclose(bilinear_grid_2d._domain_max, [2.0, 2.0]) + + +def test_regular_grid_dict_round_trip(bilinear_grid_2d): + """A regular_grid Function round-trips through to_dict/from_dict, rebuilding + from the (axes, grid_data) structure rather than the flat scatter source.""" + restored = Function.from_dict(bilinear_grid_2d.to_dict()) + + assert restored.get_interpolation_method() == "regular_grid" + assert restored.get_domain_dim() == 2 + for x, y in [(0.5, 1.5), (1.25, 0.75), (2.0, 2.0)]: + assert np.isclose(restored(x, y), bilinear_grid_2d(x, y)) + + +def test_regular_grid_dict_round_trip_preserves_method(tmp_path): + """A non-default grid method (e.g. pchip) survives to_dict/from_dict.""" + filename = tmp_path / "grid.csv" + rows = ["x,y,z"] + for x in (0, 1, 2, 3): + for y in (0, 1, 2, 3): + rows.append(f"{x},{y},{x + 10 * y**2}") + filename.write_text("\n".join(rows) + "\n", encoding="utf-8") + + original = Function.from_regular_grid_csv( + str(filename), ["x", "y"], "z", extrapolation="constant", interpolation="akima" + ) + assert original._grid_method == "pchip" + + restored = Function.from_dict(original.to_dict()) + assert restored.get_interpolation_method() == "regular_grid" + assert getattr(restored, "_grid_method", "linear") == "pchip" + assert np.isclose(restored(0.5, 1.5), original(0.5, 1.5)) diff --git a/tests/unit/rocket/aero_surface/test_barrowman_generic_equivalence.py b/tests/unit/rocket/aero_surface/test_barrowman_generic_equivalence.py index f9828d205..ebc00c72a 100644 --- a/tests/unit/rocket/aero_surface/test_barrowman_generic_equivalence.py +++ b/tests/unit/rocket/aero_surface/test_barrowman_generic_equivalence.py @@ -1,10 +1,11 @@ """Regression tests for the GenericSurface-rooted aerodynamic hierarchy. -After the refactor, every aerodynamic surface (Barrowman or generic) is -described by the generic coefficient model and exposes the diagnostic accessors -``lift_coefficient_derivative`` and ``center_of_pressure_z`` used by the rocket's -center-of-pressure / stability-margin computation. These tests pin the -properties that the refactor is meant to guarantee. +After the refactor, every aerodynamic surface (Barrowman or generic) exposes +the coefficient derivatives ``cN_alpha``/``cY_beta`` and the +``center_of_pressure_z`` accessor used by the rocket's center-of-pressure / +stability-margin computation. (Barrowman surfaces still compute their flight +forces with the classic geometric method; the derivatives feed only the +stability diagnostics.) These tests pin the properties the refactor guarantees. """ import warnings @@ -16,9 +17,8 @@ def test_barrowman_derived_cp_matches_geometric_cp(): - """The derived ``center_of_pressure_z`` must reproduce the geometric cp of - each Barrowman surface (the moment is carried by ``cm`` but the diagnostic - must recover the original location).""" + """The derived ``center_of_pressure_z`` diagnostic must reproduce the + geometric cp of each Barrowman surface.""" nose = NoseCone( length=0.55829, kind="vonkarman", base_radius=0.0635, rocket_radius=0.0635 ) @@ -37,9 +37,9 @@ def test_barrowman_derived_cp_matches_geometric_cp(): ) == surface.cpz ) - # The normal-force slope diagnostic must equal the Barrowman clalpha. + # The normal-force slope derivative must equal the Barrowman clalpha. assert pytest.approx( - nose.lift_coefficient_derivative.get_value_opt(0.0) + nose.cN_alpha.get_value_opt(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0) ) == nose.clalpha.get_value_opt(0.0) @@ -56,7 +56,7 @@ def test_generic_surface_contributes_to_static_margin(calisto_motorless): reference_area=rocket.area, reference_length=2 * rocket.radius, coefficients={ - "cL_alpha": lambda a, b, m, re, p, q, r: 2.0, + "cN_alpha": lambda a, b, m, re, p, q, r: 2.0, "cm_alpha": lambda a, b, m, re, p, q, r: -1.0, }, name="generic_fins", @@ -78,7 +78,7 @@ def test_zero_lift_surface_does_not_break_cp(calisto_motorless): drag_only = LinearGenericSurface( reference_area=rocket.area, reference_length=2 * rocket.radius, - coefficients={"cD_0": lambda a, b, m, re, p, q, r: 0.5}, + coefficients={"cA_0": lambda a, b, m, re, p, q, r: 0.5}, name="drag_only", ) rocket.add_surfaces(drag_only, positions=-1.0) @@ -123,9 +123,9 @@ def test_non_axisymmetric_rocket_splits_margins_and_warns(calisto_motorless): reference_area=rocket.area, reference_length=2 * rocket.radius, coefficients={ - "cL_alpha": lambda a, b, m, re, p, q, r: 2.0, + "cN_alpha": lambda a, b, m, re, p, q, r: 2.0, "cm_alpha": lambda a, b, m, re, p, q, r: -1.0, - "cQ_beta": lambda a, b, m, re, p, q, r: -2.0, + "cY_beta": lambda a, b, m, re, p, q, r: -2.0, "cn_beta": lambda a, b, m, re, p, q, r: 2.0, }, name="asym", @@ -137,27 +137,28 @@ def test_non_axisymmetric_rocket_splits_margins_and_warns(calisto_motorless): with pytest.warns(UserWarning, match="not\\s+axisymmetric"): ac_pitch = rocket.aerodynamic_center.get_value_opt(0.2) - assert ac_pitch != pytest.approx( - rocket.aerodynamic_center_yaw.get_value_opt(0.2) - ) + assert ac_pitch != pytest.approx(rocket.aerodynamic_center_yaw.get_value_opt(0.2)) assert rocket.static_margin.get_value_opt(0) != pytest.approx( rocket.static_margin_yaw.get_value_opt(0) ) -def test_barrowman_surface_uses_generic_compute_path(): - """Barrowman surfaces must route through the shared generic - ``compute_forces_and_moments`` (no bespoke override) and apply their force - at the origin (moment carried by the coefficients).""" +def test_barrowman_surface_uses_geometric_compute_path(): + """Barrowman surfaces compute their normal force and moment with the classic + Barrowman method (their own ``compute_forces_and_moments``): the resultant + force is reported at the geometric center of pressure and its moment is + transported geometrically from there.""" + from rocketpy.rocket.aero_surface._barrowman_surface import _BarrowmanSurface from rocketpy.rocket.aero_surface.generic_surface import GenericSurface nose = NoseCone( length=0.55829, kind="vonkarman", base_radius=0.0635, rocket_radius=0.0635 ) assert isinstance(nose, GenericSurface) - # Force is applied at the origin; the cp offset lives in cm/cn. - assert tuple(nose.force_application_point) == (0, 0, 0) + # Force is reported at the surface's geometric center of pressure. + assert tuple(nose.force_application_point) == (nose.cpx, nose.cpy, nose.cpz) + # Uses the Barrowman geometric compute, not the generic coefficient path. assert ( nose.compute_forces_and_moments.__func__ - is GenericSurface.compute_forces_and_moments + is _BarrowmanSurface.compute_forces_and_moments ) diff --git a/tests/unit/rocket/aero_surface/test_generic_surfaces.py b/tests/unit/rocket/aero_surface/test_generic_surfaces.py index 43543a50e..86e2269a6 100644 --- a/tests/unit/rocket/aero_surface/test_generic_surfaces.py +++ b/tests/unit/rocket/aero_surface/test_generic_surfaces.py @@ -10,12 +10,12 @@ @pytest.mark.parametrize( "coefficients", [ - "cL", + "cN", {"invalid_name": 0}, - {"cL": "inexistent_file.csv"}, - {"cL": Function(lambda x1, x2, x3, x4, x5, x6: 0)}, - {"cL": lambda x1: 0}, - {"cL": {}}, + {"cN": "inexistent_file.csv"}, + {"cN": Function(lambda x1, x2, x3, x4, x5, x6: 0)}, + {"cN": lambda x1: 0}, + {"cN": {}}, ], ) def test_invalid_initialization(coefficients): @@ -37,7 +37,7 @@ def test_invalid_initialization_from_csv(filename_invalid_coeff): GenericSurface( reference_area=REFERENCE_AREA, reference_length=REFERENCE_LENGTH, - coefficients={"cL": str(filename_invalid_coeff)}, + coefficients={"cN": str(filename_invalid_coeff)}, ) @@ -45,11 +45,11 @@ def test_invalid_initialization_from_csv(filename_invalid_coeff): "coefficients", [ {}, - {"cL": 0}, + {"cN": 0}, { - "cL": 0, - "cQ": Function(lambda x1, x2, x3, x4, x5, x6, x7: 0), - "cD": lambda x1, x2, x3, x4, x5, x6, x7: 0, + "cN": 0, + "cY": Function(lambda x1, x2, x3, x4, x5, x6, x7: 0), + "cA": lambda x1, x2, x3, x4, x5, x6, x7: 0, }, ], ) @@ -70,7 +70,7 @@ def test_valid_initialization_from_csv(filename_valid_coeff): GenericSurface( reference_area=REFERENCE_AREA, reference_length=REFERENCE_LENGTH, - coefficients={"cL": str(filename_valid_coeff)}, + coefficients={"cN": str(filename_valid_coeff)}, ) @@ -79,25 +79,146 @@ def test_csv_independent_variables_accept_any_order(tmp_path): regardless of independent variable column order.""" filename = tmp_path / "valid_coefficients_shuffled_order.csv" filename.write_text( - "mach,alpha,cL\n0,0,0\n0,1,10\n2,0,2\n2,1,12\n", + "mach,alpha,cN\n0,0,0\n0,1,10\n2,0,2\n2,1,12\n", encoding="utf-8", ) generic_surface = GenericSurface( reference_area=REFERENCE_AREA, reference_length=REFERENCE_LENGTH, - coefficients={"cL": str(filename)}, + coefficients={"cN": str(filename)}, ) # The coefficient is stored at minimal dimension over its CSV columns, in # header order; AeroCoefficient maps the full argument tuple onto them. - assert generic_surface.cL.depends_on == ("mach", "alpha") - csv_function = generic_surface.cL.function + assert generic_surface.cN.depends_on == ("mach", "alpha") + csv_function = generic_surface.cN.function - assert generic_surface.cL(1, 0, 2, 0, 0, 0, 0) == pytest.approx(12) + assert generic_surface.cN(1, 0, 2, 0, 0, 0, 0) == pytest.approx(12) assert csv_function.get_interpolation_method() == "regular_grid" +POINTS = [[0, 0], [1, 1], [2, 4], [3, 9]] + + +def test_interpolation_extrapolation_scalar_applies_to_all(): + """A single interpolation/extrapolation string is applied to every + tabulated coefficient.""" + gs = GenericSurface( + reference_area=REFERENCE_AREA, + reference_length=REFERENCE_LENGTH, + coefficients={"cN": POINTS, "cA": POINTS}, + extrapolation="constant", + interpolation="akima", + ) + for coeff in (gs.cN, gs.cA): + assert coeff.function.get_interpolation_method() == "akima" + assert coeff.function.get_extrapolation_method() == "constant" + + +def test_interpolation_extrapolation_per_coefficient_dict(): + """A dict configures interpolation/extrapolation per coefficient; omitted + coefficients keep the default.""" + gs = GenericSurface( + reference_area=REFERENCE_AREA, + reference_length=REFERENCE_LENGTH, + coefficients={"cN": POINTS, "cA": POINTS}, + extrapolation={"cA": "constant"}, + interpolation={"cN": "akima"}, + ) + assert gs.cN.function.get_interpolation_method() == "akima" + assert gs.cA.function.get_extrapolation_method() == "constant" + # cN was not in the extrapolation dict, so it keeps the tabulated default. + assert gs.cN.function.get_interpolation_method() == "akima" + assert gs.cA.function.get_interpolation_method() == "linear" + + +def test_prebuilt_function_interpolation_left_unchanged(): + """A pre-built Function keeps its own interpolation/extrapolation when none + is requested, and is copied (not mutated) when they are overridden.""" + source = Function(POINTS, interpolation="spline", extrapolation="zero") + + unchanged = GenericSurface(REFERENCE_AREA, REFERENCE_LENGTH, {"cN": source}) + assert unchanged.cN.function.get_interpolation_method() == "spline" + assert unchanged.cN.function.get_extrapolation_method() == "zero" + + overridden = GenericSurface( + REFERENCE_AREA, + REFERENCE_LENGTH, + {"cN": source}, + interpolation="linear", + extrapolation="constant", + ) + assert overridden.cN.function.get_interpolation_method() == "linear" + # The original Function must not have been mutated in place. + assert source.get_interpolation_method() == "spline" + + +def test_tabulated_coefficient_defaults_to_constant_extrapolation(): + """Tabulated coefficients default to constant extrapolation, so they do not + run to non-physical values past their data.""" + gs = GenericSurface( + reference_area=REFERENCE_AREA, + reference_length=REFERENCE_LENGTH, + coefficients={"cN": POINTS}, + ) + assert gs.cN.function.get_extrapolation_method() == "constant" + + +def _write_grid_csv(path): + """A 4x4 (mach, alpha) Cartesian grid, nonlinear in alpha so interpolation + methods produce distinguishable values. 4 points per axis lets "cubic" fit. + """ + rows = ["mach,alpha,cN"] + for mach in (0, 1, 2, 3): + for alpha in (0.0, 0.1, 0.2, 0.3): + rows.append(f"{mach},{alpha},{mach + 10 * alpha**2}") + path.write_text("\n".join(rows) + "\n", encoding="utf-8") + return str(path) + + +@pytest.mark.parametrize( + "interpolation, expected_grid_method", + [("linear", "linear"), ("spline", "cubic"), ("akima", "pchip")], +) +def test_grid_csv_interpolation_maps_to_scipy_method( + tmp_path, interpolation, expected_grid_method +): + """A gridded CSV honors the interpolation argument by mapping it onto the + RegularGridInterpolator method (no silent fallback to shepard).""" + filename = _write_grid_csv(tmp_path / "grid.csv") + + gs = GenericSurface( + reference_area=REFERENCE_AREA, + reference_length=REFERENCE_LENGTH, + coefficients={"cN": filename}, + interpolation=interpolation, + ) + function = gs.cN.function + # The Function stays a regular grid (not clobbered to shepard) ... + assert function.get_interpolation_method() == "regular_grid" + # ... with the mapped scipy method threaded through. + assert getattr(function, "_grid_method", "linear") == expected_grid_method + + +def test_grid_csv_cubic_differs_from_linear(tmp_path): + """The mapped grid method actually changes interpolation off the grid nodes, + confirming it is not ignored.""" + filename = _write_grid_csv(tmp_path / "grid.csv") + + linear = GenericSurface( + REFERENCE_AREA, REFERENCE_LENGTH, {"cN": filename}, interpolation="linear" + ) + cubic = GenericSurface( + REFERENCE_AREA, REFERENCE_LENGTH, {"cN": filename}, interpolation="spline" + ) + # Interior off-node point at alpha=0.15, mach=0.5 (argument order is + # alpha, beta, mach, ...): the nonlinear-in-alpha grid makes cubic and + # linear disagree there. + args = (0.15, 0.0, 0.5, 0, 0, 0, 0) + assert linear.cN(*args) != pytest.approx(cubic.cN(*args)) + + def test_compute_forces_and_moments(): """Checks if there are not logical errors in compute forces and moments""" diff --git a/tests/unit/rocket/aero_surface/test_linear_generic_surfaces.py b/tests/unit/rocket/aero_surface/test_linear_generic_surfaces.py index 88d7973cb..8f3095fd9 100644 --- a/tests/unit/rocket/aero_surface/test_linear_generic_surfaces.py +++ b/tests/unit/rocket/aero_surface/test_linear_generic_surfaces.py @@ -10,12 +10,12 @@ @pytest.mark.parametrize( "coefficients", [ - "cL_0", + "cN_0", {"invalid_name": 0}, - {"cL_0": "inexistent_file.csv"}, - {"cL_0": Function(lambda x1, x2, x3, x4, x5, x6: 0)}, - {"cL_0": lambda x1: 0}, - {"cL_0": {}}, + {"cN_0": "inexistent_file.csv"}, + {"cN_0": Function(lambda x1, x2, x3, x4, x5, x6: 0)}, + {"cN_0": lambda x1: 0}, + {"cN_0": {}}, ], ) def test_invalid_initialization(coefficients): @@ -37,7 +37,7 @@ def test_invalid_initialization_from_csv(filename_invalid_coeff_linear_generic_s LinearGenericSurface( reference_area=REFERENCE_AREA, reference_length=REFERENCE_LENGTH, - coefficients={"cL_0": str(filename_invalid_coeff_linear_generic_surface)}, + coefficients={"cN_0": str(filename_invalid_coeff_linear_generic_surface)}, ) @@ -45,11 +45,11 @@ def test_invalid_initialization_from_csv(filename_invalid_coeff_linear_generic_s "coefficients", [ {}, - {"cL_0": 0}, + {"cN_0": 0}, { - "cL_0": 0, - "cQ_0": Function(lambda x1, x2, x3, x4, x5, x6, x7: 0), - "cD_0": lambda x1, x2, x3, x4, x5, x6, x7: 0, + "cN_0": 0, + "cY_0": Function(lambda x1, x2, x3, x4, x5, x6, x7: 0), + "cA_0": lambda x1, x2, x3, x4, x5, x6, x7: 0, }, ], ) @@ -70,7 +70,7 @@ def test_valid_initialization_from_csv(filename_valid_coeff_linear_generic_surfa LinearGenericSurface( reference_area=REFERENCE_AREA, reference_length=REFERENCE_LENGTH, - coefficients={"cL_0": str(filename_valid_coeff_linear_generic_surface)}, + coefficients={"cN_0": str(filename_valid_coeff_linear_generic_surface)}, ) diff --git a/tests/unit/rocket/aero_surface/test_surface_coefficient_completeness.py b/tests/unit/rocket/aero_surface/test_surface_coefficient_completeness.py new file mode 100644 index 000000000..674e583b7 --- /dev/null +++ b/tests/unit/rocket/aero_surface/test_surface_coefficient_completeness.py @@ -0,0 +1,206 @@ +"""Guard tests ensuring every concrete aerodynamic surface exposes the full +coefficient contract the rocket and flight code rely on. + +Each surface must provide the six force/moment coefficients (``cL``, ``cQ``, +``cD``, ``cm``, ``cn``, ``cl``) and the four stability derivatives +(``cN_alpha``, ``cY_beta``, ``cm_alpha``, ``cn_beta``) as callables over its +independent-variable tuple, plus the pitch and yaw center-of-pressure accessors. +These tests catch a subclass silently omitting one. +""" + +import numpy as np +import pytest + +from rocketpy import ( + AirBrakes, + ControllableGenericSurface, + EllipticalFin, + EllipticalFins, + FreeFormFin, + FreeFormFins, + GenericSurface, + LinearGenericSurface, + NoseCone, + Tail, + TrapezoidalFin, + TrapezoidalFins, +) + +R = 0.0635 # a representative rocket radius, in meters +_SHAPE = [(0, 0), (0.08, 0.1), (0.12, 0.1), (0.12, 0)] + + +def _make_surfaces(): + """One instance of every concrete aerodynamic surface class.""" + area, length = np.pi * R**2, 2 * R + return { + "GenericSurface": GenericSurface( + reference_area=area, reference_length=length, coefficients={"cL": 1.0} + ), + "LinearGenericSurface": LinearGenericSurface( + reference_area=area, + reference_length=length, + coefficients={"cN_alpha": 2.0}, + ), + "ControllableGenericSurface": ControllableGenericSurface( + reference_area=area, + reference_length=length, + coefficients={"cL": lambda a, b, m, re, p, q, r, d: 1.0}, + ), + "AirBrakes": AirBrakes( + drag_coefficient_curve=lambda deployment, mach: 0.5, + reference_area=area, + ), + "NoseCone": NoseCone( + length=0.55829, kind="vonkarman", base_radius=R, rocket_radius=R + ), + "Tail": Tail(top_radius=R, bottom_radius=0.0435, length=0.06, rocket_radius=R), + "TrapezoidalFins": TrapezoidalFins( + n=4, span=0.1, root_chord=0.12, tip_chord=0.04, rocket_radius=R + ), + "EllipticalFins": EllipticalFins( + n=4, span=0.1, root_chord=0.12, rocket_radius=R + ), + "FreeFormFins": FreeFormFins(n=4, shape_points=_SHAPE, rocket_radius=R), + "TrapezoidalFin": TrapezoidalFin( + angular_position=0, + span=0.1, + root_chord=0.12, + tip_chord=0.04, + rocket_radius=R, + ), + "EllipticalFin": EllipticalFin( + angular_position=0, span=0.1, root_chord=0.12, rocket_radius=R + ), + "FreeFormFin": FreeFormFin( + angular_position=0, shape_points=_SHAPE, rocket_radius=R + ), + } + + +SURFACES = _make_surfaces() + +# All nine force/moment coefficients: the wind-frame forces (lift cL, side cQ, +# drag cD), the body-frame forces (normal cN, side cY, axial cA), and the moments +# (pitch cm, yaw cn, roll cl). The body-frame trio is derived from the wind trio +# (or vice versa) by the angle-of-attack/sideslip rotation. Note the +# case-sensitive distinction between ``cL`` (lift) and ``cl`` (roll). +FORCE_MOMENT_COEFFICIENTS = ( + "cL", + "cQ", + "cD", + "cN", + "cY", + "cA", + "cm", + "cn", + "cl", +) +STABILITY_DERIVATIVES = ("cN_alpha", "cY_beta", "cm_alpha", "cn_beta") + + +def _surface_params(): + return [pytest.param(name, id=name) for name in SURFACES] + + +def _coefficient_arguments(surface): + """A representative independent-variable tuple for the surface: the seven + base variables (alpha, beta, mach, reynolds, and the three rates) plus any + unsteady / control axes the surface adds, filled with zeros.""" + base = [0.05, 0.02, 0.5, 1e6, 0.0, 0.0, 0.0] + extra = len(surface.independent_vars) - len(base) + return tuple(base + [0.0] * max(0, extra)) + + +@pytest.mark.parametrize("name", _surface_params()) +@pytest.mark.parametrize("coefficient", FORCE_MOMENT_COEFFICIENTS) +def test_force_moment_coefficient_is_callable(name, coefficient): + """Every surface exposes all nine force/moment coefficients (wind cL/cQ/cD, + body cN/cY/cA, moments cm/cn/cl) as coefficients callable over the + independent-variable tuple that return a finite value.""" + surface = SURFACES[name] + coeff = getattr(surface, coefficient, None) + assert coeff is not None, f"{name} is missing coefficient {coefficient}" + value = coeff.get_value_opt(*_coefficient_arguments(surface)) + assert np.isfinite(value), f"{name}.{coefficient} returned {value}" + + +@pytest.mark.parametrize("name", _surface_params()) +@pytest.mark.parametrize("derivative", STABILITY_DERIVATIVES) +def test_stability_derivative_is_callable(name, derivative): + """Every surface exposes the stability derivatives cN_alpha, cY_beta, + cm_alpha and cn_beta used by the rocket's center-of-pressure computation.""" + surface = SURFACES[name] + coeff = getattr(surface, derivative, None) + assert coeff is not None, f"{name} is missing derivative {derivative}" + value = coeff.get_value_opt(*_coefficient_arguments(surface)) + assert np.isfinite(value), f"{name}.{derivative} returned {value}" + + +@pytest.mark.parametrize("name", _surface_params()) +def test_center_of_pressure_accessors(name): + """Every surface exposes the pitch and yaw center-of-pressure accessors used + by the rocket's aerodynamic-center computation.""" + surface = SURFACES[name] + for attr in ("center_of_pressure_z", "center_of_pressure_z_yaw"): + accessor = getattr(surface, attr, None) + assert accessor is not None, f"{name} is missing {attr}" + assert np.isfinite(accessor.get_value_opt(0.5)) + + +_ARGS = (0.15, 0.08, 0.5, 1e6, 0.0, 0.0, 0.0) + + +def test_body_input_is_recovered_by_body_accessors(): + """Coefficients supplied in the body frame are recovered by the body-frame + accessors (they round-trip through the canonical wind-frame storage).""" + from rocketpy import GenericSurface + + surface = GenericSurface( + reference_area=0.01, + reference_length=0.1, + coefficients={ + "cN": lambda a, b, m, re, p, q, r: 2.0 * a, + "cA": lambda a, b, m, re, p, q, r: 0.5, + "cY": lambda a, b, m, re, p, q, r: 1.5 * b, + }, + ) + assert surface.force_convention == "body" + assert surface.cN.get_value_opt(*_ARGS) == pytest.approx(2.0 * _ARGS[0]) + assert surface.cA.get_value_opt(*_ARGS) == pytest.approx(0.5) + assert surface.cY.get_value_opt(*_ARGS) == pytest.approx(1.5 * _ARGS[1]) + + +def test_wind_and_body_input_agree_at_zero_angle(): + """cL == cN, cD == cA and cQ == cY at zero angle of attack and sideslip, + regardless of the frame the coefficients were supplied in.""" + from rocketpy import GenericSurface + + wind = GenericSurface( + reference_area=0.01, + reference_length=0.1, + coefficients={"cL": lambda a, b, m, re, p, q, r: 2.0}, + force_convention="wind", + ) + body = GenericSurface( + reference_area=0.01, + reference_length=0.1, + coefficients={"cN": lambda a, b, m, re, p, q, r: 2.0}, + force_convention="body", + ) + zero = (0.0, 0.0, 0.5, 1e6, 0.0, 0.0, 0.0) + assert wind.cN.get_value_opt(*zero) == pytest.approx(2.0) + assert body.cL.get_value_opt(*zero) == pytest.approx(2.0) + + +def test_mixed_frame_input_raises(): + """Supplying both wind and body force coefficients without declaring the + frame is rejected.""" + from rocketpy import GenericSurface + + with pytest.raises(ValueError, match="[Mm]ixed"): + GenericSurface( + reference_area=0.01, + reference_length=0.1, + coefficients={"cL": 1.0, "cN": 1.0}, + ) diff --git a/tests/unit/rocket/test_rocket.py b/tests/unit/rocket/test_rocket.py index 623eebf1a..02fbed668 100644 --- a/tests/unit/rocket/test_rocket.py +++ b/tests/unit/rocket/test_rocket.py @@ -142,7 +142,7 @@ def test_add_trapezoidal_fins_sweep_angle( assert translate - cpz == pytest.approx(expected_fin_cpz, 0.01) # Check lift coefficient derivative - cl_alpha = fin_set.cl(1, 0.0) + cl_alpha = fin_set.clalpha(0.0) assert cl_alpha == pytest.approx(expected_clalpha, 0.01) # Check rocket's center of pressure (just double checking) @@ -184,7 +184,7 @@ def test_add_trapezoidal_fins_sweep_length( assert translate - cpz == pytest.approx(expected_fin_cpz, 0.01) # Check lift coefficient derivative - cl_alpha = fin_set.cl(1, 0.0) + cl_alpha = fin_set.clalpha(0.0) assert cl_alpha == pytest.approx(expected_clalpha, 0.01) # Check rocket's center of pressure (just double checking) diff --git a/tests/unit/rocket/test_stability_rework.py b/tests/unit/rocket/test_stability_rework.py index e911b9feb..b2995a514 100644 --- a/tests/unit/rocket/test_stability_rework.py +++ b/tests/unit/rocket/test_stability_rework.py @@ -30,7 +30,7 @@ def test_reconstructed_center_of_pressure_converges_to_aerodynamic_center( cdm = rocket.center_of_dry_mass_position coeffs = rocket.aerodynamic_coefficients_full(np.radians(0.1), 0.0, mach) - reconstructed_cp = cdm + csys * diameter * coeffs["cm"] / coeffs["cL"] + reconstructed_cp = cdm + csys * diameter * coeffs["cm"] / coeffs["cN"] assert reconstructed_cp == pytest.approx(aerodynamic_center, abs=1e-3) @@ -57,17 +57,18 @@ def test_axisymmetric_rocket_planes_coincide(calisto_robust): def test_aerodynamic_coefficients_full_signed_set(calisto_robust): - """The full rocket coefficient set returns all six signed coefficients; - lift grows with alpha, drag comes from the vehicle drag curve, and the pitch - moment is restoring (negative) for a stable rocket.""" + """The full rocket coefficient set returns all six signed body-frame + coefficients; normal force grows with alpha, axial force comes from the + vehicle drag curve, and the pitch moment is restoring (negative) for a + stable rocket.""" rocket = calisto_robust coeffs = rocket.aerodynamic_coefficients_full(np.radians(5), 0.0, 0.3) - assert set(coeffs) == {"cL", "cQ", "cD", "cm", "cn", "cl"} + assert set(coeffs) == {"cN", "cY", "cA", "cm", "cn", "cl"} low = rocket.aerodynamic_coefficients_full(np.radians(2), 0.0, 0.3) - assert coeffs["cL"] > low["cL"] > 0 + assert coeffs["cN"] > low["cN"] > 0 assert coeffs["cm"] < 0 # restoring pitch moment about the center of dry mass - assert coeffs["cD"] == pytest.approx( + assert coeffs["cA"] == pytest.approx( rocket.power_off_drag_by_mach.get_value_opt(0.3) ) @@ -76,18 +77,18 @@ def test_add_vehicle_aerodynamic_surface(calisto_robust): """A supplied full-vehicle coefficient set is added as a single generic surface and contributes to the rocket aggregate (rocket-as-GenericSurface).""" rocket = calisto_robust - base_cl = rocket.aerodynamic_coefficients_full(np.radians(5), 0.0, 0.3)["cL"] + base_cn = rocket.aerodynamic_coefficients_full(np.radians(5), 0.0, 0.3)["cN"] n_before = len(rocket.aerodynamic_surfaces) surface = rocket.add_vehicle_aerodynamic_surface( - coefficients={"cL": lambda a, b, m, re, p, q, r: 2.0 * a} + coefficients={"cN": lambda a, b, m, re, p, q, r: 2.0 * a} ) assert len(rocket.aerodynamic_surfaces) == n_before + 1 # The vehicle surface exposes the uniform coefficient accessors. - assert surface.cL(np.radians(5), 0, 0.3, 0, 0, 0, 0) == pytest.approx( + assert surface.cN(np.radians(5), 0, 0.3, 0, 0, 0, 0) == pytest.approx( 2.0 * np.radians(5) ) - # Its lift adds to the rocket aggregate. - new_cl = rocket.aerodynamic_coefficients_full(np.radians(5), 0.0, 0.3)["cL"] - assert new_cl > base_cl + # Its normal force adds to the rocket aggregate. + new_cn = rocket.aerodynamic_coefficients_full(np.radians(5), 0.0, 0.3)["cN"] + assert new_cn > base_cn diff --git a/tests/unit/simulation/test_flight.py b/tests/unit/simulation/test_flight.py index 0d42ee3a3..eacd2f3e1 100644 --- a/tests/unit/simulation/test_flight.py +++ b/tests/unit/simulation/test_flight.py @@ -240,8 +240,8 @@ def test_export_sensor_data(flight_calisto_with_sensors): @pytest.mark.parametrize( "flight_time, expected_values", [ - ("t_initial", (-0.256474, -0.221748, 0)), - ("out_of_rail_time", (0.780787, -1.967135, 0)), + ("t_initial", (0.25886, -0.649623, 0)), + ("out_of_rail_time", (0.792028, -1.987634, 0)), ("apogee_time", (-0.509420, -0.732933, -2.089120e-14)), ("t_final", (0, 0, 0)), ], @@ -279,9 +279,9 @@ def test_aerodynamic_moments(flight_calisto_custom_wind, flight_time, expected_v @pytest.mark.parametrize( "flight_time, expected_values", [ - ("t_initial", (-0.062135, -1.936030, 1.612160)), - ("out_of_rail_time", (4.968766, 1.957238, -0.629070)), - ("apogee_time", (2.343357, -1.606424, -0.377026)), + ("t_initial", (1.654150, 0.659142, -0.067103)), + ("out_of_rail_time", (5.052628, 2.013361, -1.75370)), + ("apogee_time", (2.321838, -1.613641, -0.962108)), ("t_final", (-0.019802, 0.012030, 159.051604)), ], ) From f6dd040970dcfd3e7c33da480aae712373df5c23 Mon Sep 17 00:00:00 2001 From: MateusStano Date: Fri, 10 Jul 2026 19:19:01 -0300 Subject: [PATCH 06/22] DOC: improve generic_surfaces rst --- docs/user/rocket/generic_surface.rst | 187 ++++++++++++++++++++++++++- requirements.txt | 2 +- 2 files changed, 187 insertions(+), 2 deletions(-) diff --git a/docs/user/rocket/generic_surface.rst b/docs/user/rocket/generic_surface.rst index bf9bdcfd0..70cbd4d36 100644 --- a/docs/user/rocket/generic_surface.rst +++ b/docs/user/rocket/generic_surface.rst @@ -111,6 +111,72 @@ Where: Commonly the rocket's diameter is used as the reference length. +Wind-frame and body-frame force coefficients +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +The three force coefficients above are given in the **aerodynamic (wind) frame**, +relative to the velocity vector: + +- :math:`C_L` (lift), :math:`C_Q` (side force) and :math:`C_D` (drag). + +The same force can be expressed in the **body frame**, relative to the rocket's +axes, which is what tools such as Missile DATCOM, wind tunnels and Barrowman +report: + +- :math:`C_N` (normal force, perpendicular to the body axis), +- :math:`C_Y` (body side force), +- :math:`C_A` (axial force, along the body axis). + +The two sets are the same force in different frames, related by the +angle-of-attack/sideslip rotation :math:`\mathbf{M}_{BA}`: + +.. math:: + \begin{aligned} + C_N &= \cos\alpha\, C_L + \sin\alpha\,(\sin\beta\, C_Q + \cos\beta\, C_D) \\ + C_Y &= \cos\beta\, C_Q - \sin\beta\, C_D \\ + C_A &= -\sin\alpha\, C_L + \cos\alpha\,(\sin\beta\, C_Q + \cos\beta\, C_D) + \end{aligned} + +At small angles these reduce to :math:`C_N \approx C_L`, :math:`C_Y \approx C_Q` +and :math:`C_A \approx C_D`. + +Every aerodynamic surface exposes **all nine** coefficients as attributes +(``cL``, ``cQ``, ``cD``, ``cN``, ``cY``, ``cA``, ``cm``, ``cn``, ``cl``). The +coefficients you did not provide are computed on demand from the ones you did, +so you can always read a surface's forces in whichever frame you need, for +example ``surface.cN`` for the normal-force coefficient. + +**Choosing the input frame.** Because rocket aerodynamic data (DATCOM, wind +tunnel, CFD, Barrowman) is usually reported in the body frame, you can supply +your coefficients in either frame and RocketPy converts them for you. Provide the +wind-frame names (``cL``/``cQ``/``cD``) or the body-frame names +(``cN``/``cY``/``cA``); the moment coefficients (``cm``/``cn``/``cl``) are the +same in both. + +Moment reference point +~~~~~~~~~~~~~~~~~~~~~~~~ + +The moment coefficients :math:`C_m`, :math:`C_n` and :math:`C_l` are taken about +the surface's own reference point (its ``center_of_pressure``). When the rocket +assembles the total aerodynamic moment it transports each surface's force from +that point to the rocket's **center of dry mass**, adding the +:math:`\vec{r}_{\text{cp} \to \text{cdm}} \times \vec{F}` term, so the rocket's +reported pitch/yaw moment and static margin are about the center of dry mass. + +This matters when your coefficients come from a source that uses a different +reference. Aerodynamic decks frequently give the pitch moment **about the nose +tip** (or another fixed station) rather than about the center of dry mass. A +pitch-moment coefficient referenced to a point a distance :math:`d` ahead of the +surface's center of pressure must be shifted before use: + +.. math:: + C_{m,\,\text{cp}} = C_{m,\,\text{ref}} + \frac{d}{L_{ref}}\, C_N + +Provide the coefficient about the surface's center of pressure (or set +``center_of_pressure`` so the transport lands the moment at the intended point); +otherwise the static margin will be off by the reference-point offset. + + Aerodynamic angles ~~~~~~~~~~~~~~~~~~ @@ -263,7 +329,18 @@ independent variables: - ``yaw_rate``: Yaw rate. - ``roll_rate``: Roll rate. -The last column must be the coefficient value, and must contain a header, +When the surface is created with ``unsteady_aero=True``, the coefficients may +additionally depend on the time derivatives of the flow angles, appended after +``roll_rate``: + +- ``alpha_dot``: Rate of change of the angle of attack. +- ``beta_dot``: Rate of change of the side slip angle. + +Callables must then accept the two extra trailing arguments +(``coefficient(alpha, beta, Ma, Re, q, r, p, alpha_dot, beta_dot)``) and +``.csv`` files may include ``alpha_dot``/``beta_dot`` columns. + +The last column must be the coefficient value, and must contain a header, though the header name can be anything. .. important:: @@ -451,3 +528,111 @@ shown below: rocket.add_surfaces(linear_generic_surface, position=(0,0,0)) +.. _generic_surface_interpolation: + +Interpolation and Extrapolation of Tabulated Coefficients +--------------------------------------------------------- + +When a coefficient is provided as tabulated data (a ``.csv`` file or a list of +points), RocketPy stores it as a :class:`rocketpy.Function` and must decide two +things: how to **interpolate** *between* the tabulated points, and how to +**extrapolate** *outside* the tabulated range. Both :class:`rocketpy.GenericSurface` +and :class:`rocketpy.LinearGenericSurface` (and +:class:`rocketpy.ControllableGenericSurface`) expose these as the +``interpolation`` and ``extrapolation`` arguments. + +.. note:: + Interpolation and extrapolation only apply to **tabulated** coefficients. + A coefficient given as a constant or a callable is evaluated directly, so + these settings have no effect on it (a callable is assumed valid over its + whole domain). + +Each argument accepts either: + +- a **single string**, applied to every coefficient of the surface; or +- a **dictionary** keyed by coefficient name, setting the method per + coefficient. Coefficients omitted from the dictionary keep the default. + +.. code-block:: python + + from rocketpy import GenericSurface + + radius = 0.0635 + generic_surface = GenericSurface( + reference_area=np.pi * radius**2, + reference_length=2 * radius, + coefficients={ + "cD": "cD.csv", + "cL": "cL.csv", + }, + # A single method applied to every coefficient: + extrapolation="constant", + # ... or per coefficient (unlisted ones keep the default): + interpolation={"cD": "linear", "cL": "akima"}, + ) + +Choosing an interpolation method +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Interpolation controls the behavior *between* tabulated points. For 1-D tables +the options are ``"linear"``, ``"akima"``, ``"spline"`` and ``"polynomial"``. + +- ``"linear"`` (**default**) is the safe choice. It never overshoots and + introduces no spurious oscillations, which matters most across the + **transonic drag rise** (:math:`Ma \approx 0.8`–:math:`1.2`), where a spline + will oscillate and invent non-physical wiggles in :math:`C_D`. Prefer it for + coarse tables and for anything with a sharp feature. +- ``"akima"`` gives continuous first derivatives (smoother + :math:`C_{m_\alpha}`, cleaner stability curves) while resisting the overshoot + of a natural cubic spline near kinks. It is the best "smooth" option for + **dense, smooth** data, such as lift/moment slopes in the attached-flow + region. +- ``"spline"`` produces the smoothest derivatives but overshoots near sharp + features (stall, :math:`Ma = 1`). Use it only for genuinely smooth, + well-resolved data. + +A practical rule of thumb: use ``"linear"`` against Mach (transonic kinks) and +``"akima"`` against angle of attack / sideslip when you have fine data and care +about smooth derivatives. + +.. note:: + Multi-dimensional CSV tables that form a strict Cartesian grid are read with + a :class:`scipy.interpolate.RegularGridInterpolator`. The ``interpolation`` + argument still applies: it is mapped onto the interpolator's method, with + ``"spline"`` becoming ``"cubic"`` and ``"akima"`` becoming the + shape-preserving ``"pchip"`` (``"linear"`` stays linear). Smooth methods need + enough samples per axis (``"cubic"`` needs at least 4), otherwise SciPy + raises. + +Choosing an extrapolation method +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Extrapolation controls the behavior *outside* the tabulated range. The options +are ``"constant"``, ``"natural"`` and ``"zero"``. This choice matters more than +interpolation, because a bad one fails silently, precisely when the rocket is at +an extreme condition beyond your data. + +- ``"constant"`` holds the value at the nearest edge of the data. This is the + **default for tabulated coefficients**, and the right choice for essentially + all of them: a rocket can briefly exceed your tabulated Mach/angle range, and + holding the last value is bounded and physically conservative. +- ``"zero"`` returns 0 outside the range. Occasionally reasonable for force or + moment *slopes* if you want contributions to vanish past the modeled envelope, + but it introduces a discontinuity at the edge. +- ``"natural"`` continues the fitted curve past the data. **Avoid this for + tabulated coefficients**: extrapolating a linear or spline fit can send + :math:`C_D` or a moment slope to large, non-physical values right when the + rocket is at an extreme condition. + +.. tip:: + Tabulated coefficients default to ``extrapolation="constant"`` so they never + run to non-physical values past the tabulated envelope. Override it only when + you have a specific reason (e.g. ``"zero"`` to make a contribution vanish + outside the modeled range). + +.. seealso:: + These arguments are forwarded to each :class:`rocketpy.Function`; see + :meth:`rocketpy.Function.set_interpolation` and + :meth:`rocketpy.Function.set_extrapolation` for the full list of methods. + + diff --git a/requirements.txt b/requirements.txt index 61a594320..4206c8c15 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,5 +1,5 @@ numpy>=1.13 -scipy>=1.0 +scipy>=1.13.0 # RegularGridInterpolator "pchip"/spline methods (Apr 2024) matplotlib>=3.9.0 # Released May 15th 2024 netCDF4>=1.6.4 requests From a2c617c38ce178c94d782a5f24a784432db6c23e Mon Sep 17 00:00:00 2001 From: MateusStano Date: Fri, 10 Jul 2026 19:45:04 -0300 Subject: [PATCH 07/22] TST: rebuild calisto_linear_generic from extracted Barrowman curves Replace the poorly defined calisto_linear_generic fixture, which kept the Barrowman nose cone and tail and swapped only the fins for a LinearGenericSurface with arbitrary made-up coefficients. The new fixture is a standalone Calisto whose nose cone, tail and fins are all LinearGenericSurfaces built from coefficient curves extracted off the standard Barrowman surfaces (normal-force-curve slope, center of pressure, fin roll damping). Each linear surface applies its force at its own origin and is placed at the source surface's center-of-pressure station, so both the static-margin path and the flight-moment path land at the same point as calisto_robust. The resulting flight matches the standard Calisto (identical apogee, out-of-rail time and ascent angle of attack), so the fixture now exercises the linear generic-surface path against a known-good reference. Also fix test_linear_generic_surface_flight_is_stable to check the angle of attack only during the ascent off the rail: on the rail the freestream speed is ~0 and the angle of attack is reported as a degenerate 90 degrees for any launcher, which previously failed the < 45 assertion. Co-Authored-By: Claude Opus 4.8 --- tests/fixtures/rockets/rocket_fixtures.py | 137 +++++++++++++++++++++- tests/unit/simulation/test_flight.py | 42 ++++++- 2 files changed, 177 insertions(+), 2 deletions(-) diff --git a/tests/fixtures/rockets/rocket_fixtures.py b/tests/fixtures/rockets/rocket_fixtures.py index 9cb3caa3c..6ceabf589 100644 --- a/tests/fixtures/rockets/rocket_fixtures.py +++ b/tests/fixtures/rockets/rocket_fixtures.py @@ -1,7 +1,59 @@ import numpy as np import pytest -from rocketpy import Rocket +from rocketpy import LinearGenericSurface, Rocket + +# TODO: review note: gotta test execution speed of changes in this branch + +def _linear_surface_from_barrowman(surface): + """Build a LinearGenericSurface that reproduces a Barrowman surface's aero. + + Reads the coefficient curves off a standard (Barrowman) aerodynamic surface + -- its normal-force-curve slope ``clalpha`` as a function of Mach and, for fin + sets, its roll cant and damping coefficients -- and packs them into the + body-frame coefficient derivatives of an equivalent + :class:`LinearGenericSurface`. The pitch- and yaw-plane slopes follow the + Barrowman sign convention (``cN_alpha = clalpha`` and ``cY_beta = -clalpha``). + + The returned surface applies its force at its own origin (center of pressure + ``(0, 0, 0)``); the caller is expected to add it at the surface's center of + pressure station (``barrowman_station - clalpha_cp``) so that its force + lands, and its static margin reads, at the same point as the Barrowman + surface. Its ``cpz`` (the distance from the surface origin to its center of + pressure) is exposed on the returned object as ``barrowman_cpz`` to make that + offset easy for the caller. + + Parameters + ---------- + surface : rocketpy.NoseCone, rocketpy.Tail or rocketpy fin set + A standard Barrowman aerodynamic surface to copy the aero curves from. + + Returns + ------- + rocketpy.LinearGenericSurface + A linear generic surface with the same normal force and (for fins) roll + behaviour as ``surface``, carrying the source surface's ``cpz`` as + ``barrowman_cpz``. + """ + clalpha = surface.clalpha # normal-force-curve slope, a Function of Mach + coefficients = { + "cN_alpha": clalpha, + "cY_beta": lambda mach: -clalpha.get_value_opt(mach), + } + # Fin sets carry roll coefficients: cant forcing (zero when uncanted) and + # roll-rate damping. Other surfaces have no roll_parameters. + if getattr(surface, "roll_parameters", None) is not None: + coefficients["cl_0"] = surface.cl_0 + coefficients["cl_p"] = surface.cl_p + linear_surface = LinearGenericSurface( + reference_area=surface.reference_area, + reference_length=surface.reference_length, + coefficients=coefficients, + center_of_pressure=(0, 0, 0), + name=f"{surface.name}_linear", + ) + linear_surface.barrowman_cpz = surface.cpz + return linear_surface @pytest.fixture @@ -184,6 +236,89 @@ def calisto_robust( return calisto +@pytest.fixture +def calisto_linear_generic( + cesaroni_m1670, + calisto_nose_cone, + calisto_tail, + calisto_trapezoidal_fins, + calisto_main_chute, + calisto_drogue_chute, +): + """Calisto built entirely from LinearGenericSurfaces instead of Barrowman ones. + + This is the same rocket as ``calisto_robust`` -- same body, motor, rail + buttons and parachutes at the same stations -- but its nose cone, tail and + fin set are each replaced by a body-frame ``LinearGenericSurface``. The + coefficient curves of those linear surfaces are extracted from the matching + standard (Barrowman) surfaces: each linear surface reuses the standard + surface's normal-force-curve slope, center of pressure and (for the fins) + roll damping. Because the aero data is identical, this rocket's flight + closely tracks the standard Calisto's while exercising the linear + generic-surface aerodynamic path -- the one whose forces and moments are + built directly in the body frame from the coefficient derivatives, with no + wind-to-body rotation. It is a standalone rocket (it does not reuse the + shared ``calisto`` fixture), so a test may build both it and ``calisto_robust`` + and compare their flights. + + Parameters + ---------- + cesaroni_m1670 : rocketpy.SolidMotor + The Calisto motor. This is a pytest fixture too. + calisto_nose_cone : rocketpy.NoseCone + The standard nose cone whose aero curves are copied. This is a pytest + fixture too. + calisto_tail : rocketpy.Tail + The standard boat tail whose aero curves are copied. This is a pytest + fixture too. + calisto_trapezoidal_fins : rocketpy.TrapezoidalFins + The standard fin set whose aero curves are copied. This is a pytest + fixture too. + calisto_main_chute : rocketpy.Parachute + The main parachute of the Calisto rocket. This is a pytest fixture too. + calisto_drogue_chute : rocketpy.Parachute + The drogue parachute of the Calisto rocket. This is a pytest fixture too. + + Returns + ------- + rocketpy.Rocket + The Calisto rocket whose nose cone, tail and fins are all + LinearGenericSurfaces. + """ + calisto = Rocket( + radius=0.0635, + mass=14.426, + inertia=(6.321, 6.321, 0.034), + power_off_drag="data/rockets/calisto/powerOffDragCurve.csv", + power_on_drag="data/rockets/calisto/powerOnDragCurve.csv", + center_of_mass_without_motor=0, + coordinate_system_orientation="tail_to_nose", + ) + calisto.add_motor(cesaroni_m1670, position=-1.373) + # Replace each Barrowman surface with an equivalent LinearGenericSurface. A + # Barrowman surface is placed by its origin and carries its center of + # pressure at ``cpz`` aft of that origin; a generic surface applies its force + # at its own origin. So each linear surface is added at the Barrowman + # surface's center-of-pressure station (station - cpz, with tail_to_nose + # csys = +1), which lands its force -- and its static margin -- at the same + # point as calisto_robust. + for surface, station in ( + (calisto_nose_cone, 1.160), + (calisto_tail, -1.313), + (calisto_trapezoidal_fins, -1.168), + ): + linear_surface = _linear_surface_from_barrowman(surface) + calisto.add_surfaces(linear_surface, station - linear_surface.barrowman_cpz) + calisto.set_rail_buttons( + upper_button_position=0.082, + lower_button_position=-0.618, + angular_position=0, + ) + calisto.parachutes.append(calisto_main_chute) + calisto.parachutes.append(calisto_drogue_chute) + return calisto + + @pytest.fixture def calisto_nose_to_tail_robust( calisto_nose_to_tail, diff --git a/tests/unit/simulation/test_flight.py b/tests/unit/simulation/test_flight.py index eacd2f3e1..01c9e9f51 100644 --- a/tests/unit/simulation/test_flight.py +++ b/tests/unit/simulation/test_flight.py @@ -7,7 +7,7 @@ import pytest from scipy import optimize -from rocketpy import Components, Flight, Function, Rocket +from rocketpy import Components, Flight, Function, LinearGenericSurface, Rocket plt.rcParams.update({"figure.max_open_warning": 0}) @@ -648,6 +648,46 @@ def test_stability_static_margins( assert np.all(np.abs(moments) <= 1e-10) +def test_linear_generic_surface_flight_is_stable( + calisto_linear_generic, example_plain_env +): + """A Calisto whose fin set is a body-frame LinearGenericSurface flies stably. + + The linear surface builds its forces and moments directly in the body frame + from the coefficient derivatives (no wind-to-body rotation). With a positive + normal-force slope placed aft it must give a positive static margin and the + rocket must reach a finite apogee while staying aligned with the flow (a + small angle of attack, i.e. no tumbling). + """ + rocket = calisto_linear_generic + assert any( + isinstance(surface, LinearGenericSurface) + for surface, _ in rocket.aerodynamic_surfaces + ) + assert rocket.static_margin(0) > 0 + + test_flight = Flight( + environment=example_plain_env, + rocket=rocket, + rail_length=5.2, + inclination=85, + heading=0, + terminate_on_apogee=True, + ) + + assert test_flight.apogee_time > test_flight.out_of_rail_time + assert np.isfinite(test_flight.apogee) + assert test_flight.apogee > example_plain_env.elevation + # A stable rocket keeps a small angle of attack throughout the ascent. Only + # the ascent off the rail is checked: while the rocket is still on the rail + # its speed is ~0, so the angle of attack is reported as a degenerate 90 + # degrees (arccos of 0) for every launcher, stable or not. + aoa_source = test_flight.angle_of_attack.get_source() + ascent = aoa_source[:, 0] > test_flight.out_of_rail_time + angle_of_attack = aoa_source[ascent, 1] + assert np.nanmax(np.abs(angle_of_attack)) < 45 + + def test_max_acceleration_power_off_time_with_controllers( flight_calisto_air_brakes, ): From fb236c6529be3dc9bf700662c367cab2f867c78c Mon Sep 17 00:00:00 2001 From: MateusStano Date: Sat, 11 Jul 2026 10:07:21 -0300 Subject: [PATCH 08/22] ENH: add force convention to LinearGenericSurface --- .../rocket/aero_surface/generic_surface.py | 36 +++-- .../aero_surface/linear_generic_surface.py | 152 +++++++++++++++++- .../test_linear_generic_surfaces.py | 110 ++++++++++++- 3 files changed, 283 insertions(+), 15 deletions(-) diff --git a/rocketpy/rocket/aero_surface/generic_surface.py b/rocketpy/rocket/aero_surface/generic_surface.py index eb1dfac30..decb832b6 100644 --- a/rocketpy/rocket/aero_surface/generic_surface.py +++ b/rocketpy/rocket/aero_surface/generic_surface.py @@ -242,15 +242,13 @@ def __init__( self.force_convention = self._resolve_force_convention( coefficients, force_convention ) - # The wind->body conversion only applies to surfaces whose coefficients - # are the full body-frame forces (cN/cY/cA). The linear model uses - # coefficient derivatives (cN_alpha, ...) whose frame is fixed by name. + # Wind-frame force input (cL/cQ/cD) is converted once to the canonical + # body-frame coefficients before validation. Each surface supplies the + # conversion appropriate to its coefficients: the generic surface rotates + # the full force coefficients, while the linear model recombines the + # coefficient derivatives (see LinearGenericSurface._wind_input_to_body). # A non-dict input falls through to _check_coefficients, which rejects it. - if ( - self.force_convention == "wind" - and "cN" in default_coefficients - and isinstance(coefficients, dict) - ): + if self.force_convention == "wind" and isinstance(coefficients, dict): coefficients = self._wind_input_to_body(coefficients) self._check_coefficients(coefficients, default_coefficients) coefficients = self._complete_coefficients(coefficients, default_coefficients) @@ -505,23 +503,35 @@ def _coefficient_option(option, coeff_name): _WIND_FORCE_NAMES = ("cL", "cQ", "cD") _BODY_FORCE_NAMES = ("cN", "cY", "cA") + def _force_frames_present(self, coefficients): + """Report which force frames the input coefficient names belong to, as + ``(has_wind, has_body)``. + + A generic surface matches the plain force names (``cL``/``cQ``/``cD`` for + wind, ``cN``/``cY``/``cA`` for body). The linear model overrides this to + match those same names as derivative prefixes (``cL_alpha`` ...). + """ + keys = set(coefficients) + has_wind = bool(keys & set(self._WIND_FORCE_NAMES)) + has_body = bool(keys & set(self._BODY_FORCE_NAMES)) + return has_wind, has_body + def _resolve_force_convention(self, coefficients, force_convention): """Decide whether the input force coefficients are given in the wind frame (``cL``/``cQ``/``cD``) or the body frame (``cN``/``cY``/``cA``). When ``force_convention`` is ``None`` the frame is inferred from the - coefficient names; mixing the two frames is rejected. + coefficient names; mixing the two frames is rejected. With no force + coefficients to infer from, the canonical body frame is assumed. """ - keys = set(coefficients) - has_wind = bool(keys & set(self._WIND_FORCE_NAMES)) - has_body = bool(keys & set(self._BODY_FORCE_NAMES)) + has_wind, has_body = self._force_frames_present(coefficients) if force_convention is None: if has_wind and has_body: raise ValueError( "Mixed wind (cL/cQ/cD) and body (cN/cY/cA) force " "coefficients; pass force_convention='wind' or 'body'." ) - return "body" if has_body else "wind" + return "wind" if has_wind else "body" if force_convention not in ("wind", "body"): raise ValueError( f"force_convention must be 'wind' or 'body', got {force_convention!r}." diff --git a/rocketpy/rocket/aero_surface/linear_generic_surface.py b/rocketpy/rocket/aero_surface/linear_generic_surface.py index ee92f5cf2..69aa5442e 100644 --- a/rocketpy/rocket/aero_surface/linear_generic_surface.py +++ b/rocketpy/rocket/aero_surface/linear_generic_surface.py @@ -1,6 +1,9 @@ +import inspect + from rocketpy.mathutils import Function from rocketpy.plots.aero_surface_plots import _LinearGenericSurfacePlots from rocketpy.prints.aero_surface_prints import _LinearGenericSurfacePrints +from rocketpy.rocket.aero_surface.aero_coefficient import AeroCoefficient from rocketpy.rocket.aero_surface.generic_surface import GenericSurface @@ -22,6 +25,7 @@ def __init__( name="Generic Linear Surface", interpolation=None, extrapolation=None, + force_convention=None, ): """Create a generic linear aerodynamic surface, defined by its aerodynamic coefficients derivatives. This surface is used to model any @@ -35,6 +39,13 @@ def __init__( contain at least one of the following: "alpha", "beta", "mach", "reynolds", "pitch_rate", "yaw_rate" and "roll_rate". + By default the force-coefficient derivatives are the body-frame ones + (``cN_*`` normal, ``cY_*`` side, ``cA_*`` axial; see + ``force_convention``). You may instead give the wind-frame derivatives + ``cL_*`` (lift), ``cQ_*`` (side) and ``cD_*`` (drag) -- for example + ``cL_alpha`` in place of ``cN_alpha``; they are converted once to the + body-frame set at construction. + See Also -------- :ref:`genericsurfaces`. @@ -57,7 +68,10 @@ def __init__( yaw moment ``cn`` or roll moment ``cl``; the variable is ``0`` (the value at zero angle of attack, zero sideslip and zero rates), ``alpha``, ``beta``, ``p`` (roll rate), ``q`` (pitch rate) or ``r`` - (yaw rate). The full list is:\n + (yaw rate). With ``force_convention="wind"`` the force derivatives are + named after the wind-frame coefficients instead (lift ``cL``, side + ``cQ``, drag ``cD`` -- e.g. ``cL_alpha``, ``cD_0``, ``cQ_beta``); the + moment names are unchanged. The full (body-frame) list is:\n cN_0: callable, str, optional Coefficient of normal force at zero angle of attack. Default is 0.\n cN_alpha: callable, str, optional @@ -193,6 +207,21 @@ def __init__( uses ``"constant"`` for tables built here and keeps whatever a pre-built ``Function`` already carries. Only affects tabulated sources (constants and callables are evaluated directly). + force_convention : str, optional + The frame your force-coefficient derivatives are given in. ``"body"`` + for the body-frame derivatives ``cN_*`` (normal), ``cY_*`` (side) and + ``cA_*`` (axial); ``"wind"`` for the aerodynamic-frame derivatives + ``cL_*`` (lift), ``cQ_*`` (side) and ``cD_*`` (drag). The moment + derivatives (``cm_*``, ``cn_*``, ``cl_*``) are the same in both. + ``None`` (the default) infers the frame from the coefficient names you + pass and assumes body when none are given. A wind-frame input is + converted once to the body-frame derivatives the surface stores, by + linearizing the angle-of-attack/sideslip rotation about zero: the + straight renames ``cN_0 = cL_0``, ``cN_beta = cL_beta``, the rate + derivatives, and the cross terms ``cN_alpha = cL_alpha + cD_0``, + ``cY_beta = cQ_beta - cD_0``, ``cA_alpha = cD_alpha - cL_0`` and + ``cA_beta = cD_beta + cQ_0``. At zero angle this reduces to + ``cN = cL``, ``cY = cQ``, ``cA = cD``. """ super().__init__( @@ -203,6 +232,7 @@ def __init__( name=name, extrapolation=extrapolation, interpolation=interpolation, + force_convention=force_convention, ) self.compute_all_coefficients() @@ -271,6 +301,126 @@ def _get_default_coefficients(self): } return default_coefficients + # Body force-coefficient prefix -> wind force-coefficient prefix, used to + # name the accepted wind-frame inputs. The per-plane suffixes (_0, _alpha, + # _beta, _p, _q, _r) and the moment coefficients (cm/cn/cl) are frame-shared. + _BODY_TO_WIND_PREFIX = {"cN": "cL", "cY": "cQ", "cA": "cD"} + + def _force_frames_present(self, coefficients): + """Detect the force frame from the derivative-name prefixes: a wind key + looks like ``cL_alpha``/``cD_0``/``cQ_beta`` and a body key like + ``cN_alpha``/``cA_0``/``cY_beta``. The moment derivatives (``cm_*``, + ``cn_*``, ``cl_*``) are frame-shared and ignored here. + """ + prefixes = {key.split("_", 1)[0] for key in coefficients} + has_wind = bool(prefixes & set(self._WIND_FORCE_NAMES)) + has_body = bool(prefixes & set(self._BODY_FORCE_NAMES)) + return has_wind, has_body + + def _wind_default_coefficient_names(self): + """The valid wind-frame input names: the body defaults with the force + prefixes swapped to wind (``cN_* -> cL_*``, ``cY_* -> cQ_*``, + ``cA_* -> cD_*``); the moment names are unchanged. + """ + names = set() + for key in self._get_default_coefficients(): + prefix, sep, suffix = key.partition("_") + wind_prefix = self._BODY_TO_WIND_PREFIX.get(prefix, prefix) + names.add(f"{wind_prefix}{sep}{suffix}") + return names + + def _wind_input_to_body(self, coefficients): + """Convert wind-frame coefficient derivatives (``cL_*``/``cD_*``/``cQ_*``) + into the canonical body-frame derivatives (``cN_*``/``cY_*``/``cA_*``). + + The full body-frame force coefficients are the wind ones rotated by the + angle of attack and sideslip; linearizing that rotation about + ``alpha = beta = 0`` gives, to first order, a coefficient-derivative map + with four cross-frame terms:: + + cN_alpha = cL_alpha + cD_0 cA_alpha = cD_alpha - cL_0 + cY_beta = cQ_beta - cD_0 cA_beta = cD_beta + cQ_0 + + Every other derivative is a straight rename (``cN_0 = cL_0``, + ``cN_beta = cL_beta``, the rate derivatives ``cN_p = cL_p`` ..., and the + wind side/axial analogues). At zero angle this reduces to ``cN = cL``, + ``cY = cQ``, ``cA = cD``, matching the generic surface. The moment + derivatives (``cm_*``/``cn_*``/``cl_*``) are frame-shared and pass + through unchanged. + """ + invalid = set(coefficients) - self._wind_default_coefficient_names() + if invalid: + raise ValueError( + f"Invalid coefficient name(s) used in key(s): {', '.join(invalid)}. " + "Check the documentation for valid names." + ) + + def wind(name): + return coefficients.get(name, 0) + + body = { + "cN_0": wind("cL_0"), + "cN_alpha": self._combine(wind("cL_alpha"), wind("cD_0"), 1.0, "cN_alpha"), + "cN_beta": wind("cL_beta"), + "cN_p": wind("cL_p"), + "cN_q": wind("cL_q"), + "cN_r": wind("cL_r"), + "cY_0": wind("cQ_0"), + "cY_alpha": wind("cQ_alpha"), + "cY_beta": self._combine(wind("cQ_beta"), wind("cD_0"), -1.0, "cY_beta"), + "cY_p": wind("cQ_p"), + "cY_q": wind("cQ_q"), + "cY_r": wind("cQ_r"), + "cA_0": wind("cD_0"), + "cA_alpha": self._combine(wind("cD_alpha"), wind("cL_0"), -1.0, "cA_alpha"), + "cA_beta": self._combine(wind("cD_beta"), wind("cQ_0"), 1.0, "cA_beta"), + "cA_p": wind("cD_p"), + "cA_q": wind("cD_q"), + "cA_r": wind("cD_r"), + } + # Moment derivatives are the same in both frames; pass them through. + for name, value in coefficients.items(): + if name.split("_", 1)[0] not in self._WIND_FORCE_NAMES: + body[name] = value + return body + + def _as_coefficient(self, source, name): + """Wrap a raw coefficient input as an :class:`AeroCoefficient` over this + surface's variables (used when recombining wind-frame derivatives). + """ + return AeroCoefficient( + source, + unsteady_aero=self._unsteady_aero, + control_variables=self.control_variables, + name=name, + ) + + def _combine(self, first, second, sign, name): + """Return a coefficient equal to ``first + sign * second``. + + When one term is identically zero the other is returned directly (as a + renamed coefficient), so a derivative that is really just a rename keeps + its original, low-dimensional form. Otherwise the two are summed by a + small wrapper evaluated over the full variable tuple. + """ + coeff_first = self._as_coefficient(first, name) + coeff_second = self._as_coefficient(second, name) + if coeff_second.is_zero: + return coeff_first + if coeff_first.is_zero: + return coeff_second if sign > 0 else coeff_second * -1.0 + first_opt = coeff_first.get_value_opt + second_opt = coeff_second.get_value_opt + + def combined(*args): + return first_opt(*args) + sign * second_opt(*args) + + combined.__signature__ = inspect.Signature( + inspect.Parameter(var, inspect.Parameter.POSITIONAL_OR_KEYWORD) + for var in self.independent_vars + ) + return self._as_coefficient(combined, name) + _COEFFICIENT_INPUTS = [ "alpha", "beta", diff --git a/tests/unit/rocket/aero_surface/test_linear_generic_surfaces.py b/tests/unit/rocket/aero_surface/test_linear_generic_surfaces.py index 8f3095fd9..7bb884220 100644 --- a/tests/unit/rocket/aero_surface/test_linear_generic_surfaces.py +++ b/tests/unit/rocket/aero_surface/test_linear_generic_surfaces.py @@ -1,11 +1,14 @@ import pytest -from rocketpy import Function, LinearGenericSurface +from rocketpy import Function, GenericSurface, LinearGenericSurface from rocketpy.mathutils import Vector REFERENCE_AREA = 1 REFERENCE_LENGTH = 1 +# (alpha, beta, mach, reynolds, pitch_rate, yaw_rate, roll_rate) +_ARGS = (0.0, 0.0, 0.5, 1e6, 0.0, 0.0, 0.0) + @pytest.mark.parametrize( "coefficients", @@ -121,3 +124,108 @@ def test_roll_damping_uses_reduced_rate(): # Old (raw-rate) formulation -- identical value: old_damping_scaling = 0.5 * rho * speed * ref_area * ref_length**2 / 2 assert roll_moment == pytest.approx(old_damping_scaling * cl_p * raw_roll) + + +def test_force_convention_inference(): + """The input frame is inferred from the derivative names when + ``force_convention`` is not given, defaulting to body when there are no + force derivatives to infer from.""" + assert LinearGenericSurface(1, 1, {"cN_alpha": 2.0}).force_convention == "body" + assert LinearGenericSurface(1, 1, {"cL_alpha": 2.0}).force_convention == "wind" + # Moment-only / empty input carries no force frame -> body (canonical). + assert LinearGenericSurface(1, 1, {"cm_alpha": 1.0}).force_convention == "body" + assert LinearGenericSurface(1, 1, {}).force_convention == "body" + + +def test_mixed_frame_input_raises(): + """Supplying both wind (cL_*) and body (cN_*) force derivatives without + declaring the frame is rejected.""" + with pytest.raises(ValueError, match="[Mm]ixed"): + LinearGenericSurface(1, 1, {"cL_alpha": 1.0, "cN_0": 1.0}) + + +def test_invalid_wind_coefficient_name_raises(): + """A wind-frame input with an unknown derivative name is rejected.""" + with pytest.raises(ValueError, match="Invalid coefficient name"): + LinearGenericSurface(1, 1, {"cL_gamma": 1.0}, force_convention="wind") + + +def test_wind_derivatives_convert_to_body_first_order(): + """Wind-frame derivatives are converted to the body-frame set by linearizing + the wind/body rotation about zero: the four cross terms plus straight + renames. A nonzero base drag ``cD_0`` couples into the normal- and + axial-force slopes.""" + surface = LinearGenericSurface( + reference_area=0.01, + reference_length=0.1, + coefficients={ + "cL_0": 0.1, + "cL_alpha": 5.0, + "cD_0": 0.3, + "cD_alpha": 0.2, + "cQ_beta": -4.0, + "cQ_0": 0.05, + "cm_alpha": -2.0, # frame-shared moment, must pass through unchanged + }, + force_convention="wind", + ) + assert surface.force_convention == "wind" + assert surface.cN_0.get_value_opt(*_ARGS) == pytest.approx(0.1) # cL_0 + assert surface.cN_alpha.get_value_opt(*_ARGS) == pytest.approx(5.3) # cL_alpha+cD_0 + assert surface.cY_beta.get_value_opt(*_ARGS) == pytest.approx(-4.3) # cQ_beta-cD_0 + assert surface.cA_0.get_value_opt(*_ARGS) == pytest.approx(0.3) # cD_0 + assert surface.cA_alpha.get_value_opt(*_ARGS) == pytest.approx(0.1) # cD_alpha-cL_0 + assert surface.cA_beta.get_value_opt(*_ARGS) == pytest.approx(0.05) # cD_beta+cQ_0 + assert surface.cm_alpha.get_value_opt(*_ARGS) == pytest.approx(-2.0) # passthrough + + +def test_wind_input_matches_body_input(): + """A wind-frame surface equals the body-frame surface built from the + hand-converted derivatives, at an arbitrary angle.""" + wind = LinearGenericSurface( + 1, + 1, + coefficients={"cL_0": 0.1, "cL_alpha": 5.0, "cD_0": 0.3, "cQ_beta": -4.0}, + force_convention="wind", + ) + body = LinearGenericSurface( + 1, + 1, + coefficients={ + "cN_0": 0.1, + "cN_alpha": 5.3, + "cA_0": 0.3, + "cA_alpha": -0.1, # cD_alpha - cL_0 + "cY_beta": -4.3, + }, + ) + args = (0.02, 0.01, 0.5, 1e6, 0.0, 0.0, 0.0) + for coeff in ("cN", "cY", "cA"): + assert getattr(wind, coeff).get_value_opt(*args) == pytest.approx( + getattr(body, coeff).get_value_opt(*args) + ) + + +def test_wind_linear_matches_generic_surface_to_first_order(): + """The body-frame forces of a wind-input linear surface agree with the + exact rotation used by GenericSurface to first order in the flow angles.""" + coeffs = {"cL_0": 0.1, "cL_alpha": 5.0, "cD_0": 0.3, "cQ_beta": -4.0} + linear = LinearGenericSurface(0.01, 0.1, coeffs, force_convention="wind") + generic = GenericSurface( + 0.01, + 0.1, + coefficients={ + "cL": lambda a, b, m, re, p, q, r: 0.1 + 5.0 * a, + "cD": lambda a, b, m, re, p, q, r: 0.3, + "cQ": lambda a, b, m, re, p, q, r: -4.0 * b, + }, + force_convention="wind", + ) + eps = 1e-3 + for alpha, beta in [(eps, 0.0), (0.0, eps), (eps, eps)]: + args = (alpha, beta, 0.5, 1e6, 0.0, 0.0, 0.0) + for coeff in ("cN", "cY", "cA"): + # Difference is second order in the angle (~1e-6 at eps=1e-3). + assert getattr(linear, coeff).get_value_opt(*args) == pytest.approx( + getattr(generic, coeff).get_value_opt(*args), abs=1e-5 + ) From 8fd3b16f872cc68c4ad31e528c2d17ef31757440 Mon Sep 17 00:00:00 2001 From: MateusStano Date: Thu, 23 Jul 2026 13:05:42 -0300 Subject: [PATCH 09/22] ENH: apply review changes --- rocketpy/plots/flight_plots.py | 216 +++-- rocketpy/plots/rocket_plots.py | 186 ++--- rocketpy/prints/flight_prints.py | 85 +- rocketpy/prints/rocket_prints.py | 24 +- .../rocket/aero_surface/_barrowman_surface.py | 13 +- .../rocket/aero_surface/aero_coefficient.py | 165 ++-- .../controllable_generic_surface.py | 44 +- .../rocket/aero_surface/fins/_base_fin.py | 7 + rocketpy/rocket/aero_surface/fins/fin.py | 4 +- .../rocket/aero_surface/generic_surface.py | 485 ++++++----- .../aero_surface/linear_generic_surface.py | 29 +- rocketpy/rocket/helpers.py | 280 +++++++ rocketpy/rocket/rocket.py | 768 ++++++++++++------ rocketpy/simulation/flight.py | 149 ++-- .../simulation/helpers/flight_derivatives.py | 6 + tests/fixtures/rockets/rocket_fixtures.py | 357 +++++++- .../aero_surface/test_aero_coefficient.py | 17 +- .../test_controllable_generic_surface.py | 39 + .../aero_surface/test_generic_surfaces.py | 209 ++++- .../test_surface_coefficient_completeness.py | 2 +- .../test_unsteady_generic_surface.py | 71 -- .../test_generic_calisto_equivalence.py | 69 ++ tests/unit/rocket/test_stability_rework.py | 334 ++++++-- tests/unit/simulation/test_flight.py | 59 ++ 24 files changed, 2649 insertions(+), 969 deletions(-) create mode 100644 rocketpy/rocket/helpers.py delete mode 100644 tests/unit/rocket/aero_surface/test_unsteady_generic_surface.py create mode 100644 tests/unit/rocket/test_generic_calisto_equivalence.py diff --git a/rocketpy/plots/flight_plots.py b/rocketpy/plots/flight_plots.py index 99a528fb7..99e4d2fa8 100644 --- a/rocketpy/plots/flight_plots.py +++ b/rocketpy/plots/flight_plots.py @@ -59,8 +59,9 @@ def first_parachute_event_time_index(self): # Consistent red used for the rocket trajectory line across all plots. _TRAJECTORY_COLOR = "#e63946" - # Burnout vertical/drop-line color -- kept separate from the orange dot marker so - # the dashed line stays readable against typical orange and blue plot lines. + # Dark drop-line/vertical-line color shared by the Burnout and Out Of Rail + # events -- kept separate from their (orange / dark-red) dot markers so the + # dashed line stays readable against typical orange and blue plot lines. _BURNOUT_LINE_COLOR = "#4a4a4a" _EVENT_LINE_WIDTH = 1.2 @@ -69,7 +70,7 @@ def first_parachute_event_time_index(self): "Impact": "#ff1f1f", "Apogee": "#46daff", "Burnout": "#ff8121", - "Out Of Rail": "#8b0000", + "Out Of Rail": "#e6c000", } _COLOR_CYCLE = [ "#7de07a", @@ -110,8 +111,9 @@ def _collect_events(self): (t_ev, "Apogee", "o", self._RESERVED_COLORS["Apogee"], 40) ) elif name == "Out Of Rail": + # Same dot shape and size as Burnout; distinguished by color. events.append( - (t_ev, "Out Of Rail", "^", self._RESERVED_COLORS["Out Of Rail"], 30) + (t_ev, "Out Of Rail", "o", self._RESERVED_COLORS["Out Of Rail"], 40) ) elif "Parachute" in name: if name not in parachute_color_map: @@ -186,18 +188,21 @@ def _add_event_markers(self, ax, legend=True): def _add_event_markers_dropline(self, ax, legend=True, y_bottom=None, labels=None): """Event markers on the plotted curve with drop-lines from the y-axis bottom. - For each trigger-once event (excluding Out Of Rail and Landing), draws an - unlabelled dashed vertical line from the axis bottom to the curve value at - that time, and a labelled scatter marker on the curve itself. Apogee is - drawn last so it renders on top of coincident markers. + For each trigger-once event, draws an unlabelled dashed vertical line from + the axis bottom to the curve value at that time, and a labelled scatter + marker on the curve itself. Apogee is drawn last so it renders on top of + coincident markers. By default Out Of Rail and Landing are omitted, but a + caller can draw them by naming them explicitly in ``labels``. Parameters ---------- y_bottom : float or None - Y coordinate for the bottom of drop-lines. When None (default) the + Y coordinate for the bottom of drop-lines. When None (default) the bottom is derived from the minimum of the visible plotted data. labels : set or None - If given, only events whose label is in this set are drawn. + If given, only events whose label is in this set are drawn; an + explicit set also overrides the default omission of Out Of Rail and + Landing (so e.g. ``labels={"Out Of Rail"}`` draws that marker). """ lines = [ln for ln in ax.lines if len(ln.get_xdata()) > 1] if not lines: @@ -217,14 +222,22 @@ def _add_event_markers_dropline(self, ax, legend=True, y_bottom=None, labels=Non deferred_apogee = None for t_ev, label, marker, color, size in self._collect_events(): - if label in ("Out Of Rail", "Landing"): - continue - if labels is not None and label not in labels: + if labels is not None: + # An explicit label set is an opt-in: draw exactly those events, + # including Out Of Rail / Landing when named. + if label not in labels: + continue + elif label in ("Out Of Rail", "Landing"): + # Omitted from the default (unfiltered) set of drop-line markers. continue if not xlim[0] <= t_ev <= xlim[1]: continue y_ev = float(np.interp(t_ev, xdata, ydata)) - line_color = self._BURNOUT_LINE_COLOR if label == "Burnout" else color + line_color = ( + self._BURNOUT_LINE_COLOR + if label in ("Burnout", "Out Of Rail") + else color + ) lw = ( self._EVENT_LINE_WIDTH if label == "Burnout" else self._EVENT_LINE_WIDTH ) @@ -1245,9 +1258,16 @@ def fluid_mechanics_data(self, *, filename=None): # pylint: disable=too-many-st plt.subplots_adjust(hspace=0.5) show_or_save_plot(filename) - def stability_and_control_data(self, *, filename=None): # pylint: disable=too-many-statements - """Prints out Rocket Stability and Control parameters graphs available - about the Flight + def stability_margin_data(self, *, filename=None): + """Plots the rocket's stability margin over the flight, in calibers. + + The stability margin is one of the most important results of a + simulation: it is the distance from the center of mass to the center of + pressure that must stay positive (center of pressure behind the center + of mass) for the rocket to correct disturbances. A secondary axis reads + the same margin as a percentage of the rocket's overall length, the + convention often used in hobby rocketry. For a non-axisymmetric rocket + the pitch and yaw margins are drawn separately. Parameters ---------- @@ -1261,21 +1281,20 @@ def stability_and_control_data(self, *, filename=None): # pylint: disable=too-m ------- None """ - - plt.figure(figsize=(9, 6)) - asymmetric = not self.flight.rocket.is_axisymmetric - ax1 = plt.subplot(211) + + plt.figure(figsize=(9, 4.5)) + ax1 = plt.subplot(111) ax1.plot( self.flight.stability_margin[:, 0], self.flight.stability_margin[:, 1], - label="Linear pitch" if asymmetric else "Linear (aerodynamic center)", + label="Pitch" if asymmetric else "Stability margin", ) if asymmetric: ax1.plot( self.flight.stability_margin_yaw[:, 0], self.flight.stability_margin_yaw[:, 1], - label="Linear yaw", + label="Yaw", ) ax1.set_title("Stability Margin") ax1.set_xlabel("Time (s)") @@ -1283,59 +1302,56 @@ def stability_and_control_data(self, *, filename=None): # pylint: disable=too-m ax1.set_xlim(0, self.first_parachute_event_time) ax1.legend() ax1.grid() - self._add_event_markers_dropline(ax1, labels={"Burnout"}) - - ax2 = plt.subplot(212) - x_axis = np.arange(0, 5, 0.01) - max_attitude = self.flight.attitude_frequency_response.max - max_attitude = max_attitude if max_attitude != 0 else 1 - ax2.plot( - x_axis, - self.flight.attitude_frequency_response(x_axis) / max_attitude, - label="Attitude Angle", - ) - max_omega1 = self.flight.omega1_frequency_response.max - max_omega1 = max_omega1 if max_omega1 != 0 else 1 - ax2.plot( - x_axis, - self.flight.omega1_frequency_response(x_axis) / max_omega1, - label=r"$\omega_1$", - ) - max_omega2 = self.flight.omega2_frequency_response.max - max_omega2 = max_omega2 if max_omega2 != 0 else 1 - ax2.plot( - x_axis, - self.flight.omega2_frequency_response(x_axis) / max_omega2, - label=r"$\omega_2$", - ) - max_omega3 = self.flight.omega3_frequency_response.max - max_omega3 = max_omega3 if max_omega3 != 0 else 1 - ax2.plot( - x_axis, - self.flight.omega3_frequency_response(x_axis) / max_omega3, - label=r"$\omega_3$", - ) - ax2.set_title("Frequency Response") - ax2.set_xlabel("Frequency (Hz)") - ax2.set_ylabel("Amplitude Magnitude Normalized") - ax2.set_xlim(0, 5) - ax2.legend() - ax2.grid() + # Secondary y-axis reading the same margin as a percentage of the + # rocket's overall length (see Rocket.length), the convention often used + # in hobby rocketry. A margin in calibers and the same margin as a + # fraction of body length differ only by the constant factor below, so + # the second scale is a plain rescaling of the caliber axis. + # A rocket with no aerodynamic surfaces has no defined length, so the + # percentage-of-length scale can't be drawn; skip it in that case. + rocket = self.flight.rocket + rocket_length = rocket.length if rocket.aerodynamic_surfaces else 0 + if rocket_length > 0: + factor = 2 * rocket.radius / rocket_length * 100 + secondary_axis = ax1.secondary_yaxis( + "right", + functions=(lambda c: c * factor, lambda p: p / factor), + ) + secondary_axis.set_ylabel("Stability Margin (% of length)") + self._add_event_markers_dropline(ax1, labels={"Out Of Rail", "Burnout"}) - plt.subplots_adjust(hspace=0.5) show_or_save_plot(filename) - def dynamic_stability_data(self, *, filename=None): + def stability_and_control_data(self, *, filename=None): + """Deprecated. Stability and the frequency response are now separate + plots. + + Use :meth:`stability_margin_data` for the stability margin, and + :meth:`dynamic_stability_data` for the natural frequency, damping ratio + and attitude frequency response. + + Parameters + ---------- + filename : str | None, optional + Passed through to both replacement plots. + """ + warnings.warn( + "stability_and_control_data() is deprecated and will be removed in " + "v1.13. Stability is now its own plot: use stability_margin_data() " + "for the stability margin, and dynamic_stability_data() for the " + "natural frequency, damping ratio and attitude frequency response.", + DeprecationWarning, + stacklevel=2, + ) + self.stability_margin_data(filename=filename) + self.dynamic_stability_data(filename=filename) + + def dynamic_stability_data(self, *, filename=None): # pylint: disable=too-many-statements """Plots the rocket's dynamic-stability quantities over the flight: the pitch (and, for non-axisymmetric rockets, yaw) natural frequency and - damping ratio of the linearized attitude oscillation. - - The roll rate is overlaid on the natural-frequency plot (as a frequency). - Roll is neutrally stable -- it has no restoring moment and therefore no - natural frequency of its own -- but **roll resonance** ("roll lock-in") - occurs where the roll rate crosses the pitch/yaw natural frequency, the - roll-pitch/yaw coupling driving the attitude oscillation. Those crossings - are the points to watch. + damping ratio of the linearized attitude oscillation, together with the + attitude frequency response (the FFT of the simulated oscillation), which + independently verifies the predicted natural frequency. Parameters ---------- @@ -1350,11 +1366,18 @@ def dynamic_stability_data(self, *, filename=None): None """ asymmetric = not self.flight.rocket.is_axisymmetric - upper = self.first_parachute_event_time + # Cap the time axis at apogee: the attitude oscillation is only + # meaningful during ascent. Fall back to the first parachute event (or + # flight end) when there is no apogee, e.g. a flight cut short before it. + upper = ( + self.flight.apogee_time + if self.flight.apogee_time != 0 + else self.first_parachute_event_time + ) - plt.figure(figsize=(9, 6)) + plt.figure(figsize=(9, 9)) - ax1 = plt.subplot(211) + ax1 = plt.subplot(311) freq = self.flight.pitch_natural_frequency ax1.plot(freq[:, 0], freq[:, 1] / (2 * np.pi), label="Pitch natural freq.") if asymmetric: @@ -1365,15 +1388,13 @@ def dynamic_stability_data(self, *, filename=None): "--", label="Yaw natural freq.", ) - # Roll rate as a frequency: where it crosses the natural frequency the - # rocket is in roll resonance (roll-pitch/yaw coupling). roll_rate = self.flight.w3 ax1.plot( roll_rate[:, 0], np.abs(roll_rate[:, 1]) / (2 * np.pi), ":", color="tab:red", - label="Roll rate (resonance if crossing)", + label="Roll rate", ) ax1.set_title("Natural Frequency & Roll Rate") ax1.set_xlabel("Time (s)") @@ -1381,15 +1402,14 @@ def dynamic_stability_data(self, *, filename=None): ax1.set_xlim(0, upper) ax1.legend() ax1.grid() - self._add_event_markers_dropline(ax1, labels={"Burnout"}) + self._add_event_markers_dropline(ax1, labels={"Out Of Rail", "Burnout"}) - ax2 = plt.subplot(212) + ax2 = plt.subplot(312) ratio = self.flight.pitch_damping_ratio ax2.plot(ratio[:, 0], ratio[:, 1], label="Pitch") if asymmetric: yaw_ratio = self.flight.yaw_damping_ratio ax2.plot(yaw_ratio[:, 0], yaw_ratio[:, 1], "--", label="Yaw") - ax2.axhline(1.0, color="gray", linestyle=":", label="Critical (ζ=1)") ax2.set_title("Damping Ratio") ax2.set_xlabel("Time (s)") ax2.set_ylabel("Damping Ratio (ζ)") @@ -1397,6 +1417,26 @@ def dynamic_stability_data(self, *, filename=None): ax2.legend() ax2.grid() + # Frequency response: the FFT spectrum of the simulated attitude and body + # rates. Its peak should fall at the natural frequency plotted above, + # giving an independent check of the linearized prediction. + ax3 = plt.subplot(313) + x_axis = np.arange(0, 5, 0.01) + for response, label in ( + (self.flight.attitude_frequency_response, "Attitude Angle"), + (self.flight.omega1_frequency_response, r"$\omega_1$"), + (self.flight.omega2_frequency_response, r"$\omega_2$"), + (self.flight.omega3_frequency_response, r"$\omega_3$"), + ): + peak = response.max if response.max != 0 else 1 + ax3.plot(x_axis, response(x_axis) / peak, label=label) + ax3.set_title("Attitude Frequency Response") + ax3.set_xlabel("Frequency (Hz)") + ax3.set_ylabel("Amplitude Magnitude Normalized") + ax3.set_xlim(0, 5) + ax3.legend() + ax3.grid() + plt.subplots_adjust(hspace=0.5) show_or_save_plot(filename) @@ -1515,7 +1555,11 @@ def altitude_data(self, *, filename=None): if not xlim[0] <= t_ev <= xlim[1]: continue alt_ev = float(np.interp(t_ev, z_times, z_agl)) - line_color = self._BURNOUT_LINE_COLOR if label == "Burnout" else color + line_color = ( + self._BURNOUT_LINE_COLOR + if label in ("Burnout", "Out Of Rail") + else color + ) lw = ( self._EVENT_LINE_WIDTH if label == "Burnout" else self._EVENT_LINE_WIDTH ) @@ -1827,6 +1871,12 @@ def all(self): # pylint: disable=too-many-statements print("\n\nTrajectory Angular Velocity and Acceleration Plots\n") self.angular_kinematics_data() + print("\n\nStability Margin Plot\n") + self.stability_margin_data() + + print("\n\nDynamic Stability Plots\n") + self.dynamic_stability_data() + print("\n\nAngle of Attack Plots\n") self.angle_of_attack_data() @@ -1851,10 +1901,6 @@ def all(self): # pylint: disable=too-many-statements print("\n\nTrajectory Fluid Mechanics Plots\n") self.fluid_mechanics_data() - print("\n\nTrajectory Stability and Control Plots\n") - self.stability_and_control_data() - self.dynamic_stability_data() - if self.flight.sensors: print("\n\nSensor Data Plots\n") self.sensor_data() diff --git a/rocketpy/plots/rocket_plots.py b/rocketpy/plots/rocket_plots.py index 4cfc0934b..ed7c6d3bb 100644 --- a/rocketpy/plots/rocket_plots.py +++ b/rocketpy/plots/rocket_plots.py @@ -3,12 +3,13 @@ import matplotlib.pyplot as plt import numpy as np +from rocketpy.mathutils.function import Function from rocketpy.mathutils.vector_matrix import Vector from rocketpy.motors import EmptyMotor, HybridMotor, LiquidMotor, SolidMotor from rocketpy.rocket.aero_surface import Fin, Fins, NoseCone, Tail from rocketpy.rocket.aero_surface.generic_surface import GenericSurface -from .plot_helpers import show_or_save_plot +from .plot_helpers import show_or_save_fig, show_or_save_plot class _RocketPlots: @@ -55,9 +56,28 @@ def reduced_mass(self): self.rocket.reduced_mass() + def _caliber_to_length_percent(self): + """Return the (forward, inverse) pair converting a margin in calibers to + a percentage of the rocket's overall aerodynamic length. + + A margin in calibers is ``distance / (2 * radius)``; the same distance as + a fraction of the body length is ``distance / length``. The two therefore + differ only by the constant factor ``2 * radius / length`` (times 100 for + a percentage), so the length-percentage scale is a plain rescaling of the + caliber scale and can be drawn as a secondary axis. See + :attr:`rocketpy.Rocket.length`. + """ + factor = 2 * self.rocket.radius / self.rocket.length * 100 + return (lambda calibers: calibers * factor, lambda percent: percent / factor) + def static_margin(self, *, filename=None): """Plots static margin of the rocket as a function of time. + A secondary y-axis on the right expresses the same margin as a + percentage of the rocket's overall length (see + :attr:`rocketpy.Rocket.length`), the convention often used in hobby + rocketry, alongside the primary caliber (diameter) axis. + Parameters ---------- filename : str | None, optional @@ -70,23 +90,49 @@ def static_margin(self, *, filename=None): ------- None """ + self._plot_static_margin(self.rocket.static_margin, "Static Margin", filename) + + def _plot_static_margin(self, margin, title, filename): + """Draw a static-margin-vs-time line plot with a caliber primary y-axis + and a length-percentage secondary y-axis.""" + time = np.linspace(0, self.rocket.motor.burn_out_time, 200) + values = margin.get_value(time) + + fig, ax = plt.subplots() + ax.plot(time, values) + ax.set_xlabel("Time (s)") + ax.set_ylabel("Static Margin (calibers)") + ax.set_title(title) + ax.grid(True) - self.rocket.static_margin(filename=filename) + secondary_axis = ax.secondary_yaxis( + "right", functions=self._caliber_to_length_percent() + ) + secondary_axis.set_ylabel("Static Margin (% of length)") - def stability_margin(self): - """Plots static margin of the rocket as a function of time. + show_or_save_fig(fig, filename) + + def stability_margin(self, *, filename=None): + """Plots the stability margin of the rocket as a function of Mach number + and time, at zero angle of attack (the design surface). + + Parameters + ---------- + filename : str | None, optional + The path the plot should be saved to. By default None, in which case + the plot will be shown instead of saved. Returns ------- None """ - - self.rocket.stability_margin.plot_2d( + self._design_stability_margin(self.rocket.stability_margin).plot_2d( lower=0, upper=[2, self.rocket.motor.burn_out_time], # Mach 2 and burnout samples=[20, 20], disp_type="surface", alpha=1, + filename=filename, ) def static_margin_yaw(self, *, filename=None): @@ -94,6 +140,9 @@ def static_margin_yaw(self, *, filename=None): time. Only meaningful for non-axisymmetric rockets; for an axisymmetric rocket it is identical to :meth:`static_margin`. + A secondary y-axis expresses the margin as a percentage of the rocket's + overall length (see :attr:`rocketpy.Rocket.length`). + Parameters ---------- filename : str | None, optional @@ -106,23 +155,42 @@ def static_margin_yaw(self, *, filename=None): ------- None """ - self.rocket.static_margin_yaw(filename=filename) + self._plot_static_margin( + self.rocket.static_margin_yaw, "Static Margin (Yaw Plane)", filename + ) - def stability_margin_yaw(self): + def stability_margin_yaw(self, *, filename=None): """Plots the yaw-plane stability margin of the rocket as a function of Mach number and time. Only meaningful for non-axisymmetric rockets; for an axisymmetric rocket it is identical to :meth:`stability_margin`. + Parameters + ---------- + filename : str | None, optional + The path the plot should be saved to. By default None, in which case + the plot will be shown instead of saved. + Returns ------- None """ - self.rocket.stability_margin_yaw.plot_2d( + self._design_stability_margin(self.rocket.stability_margin_yaw).plot_2d( lower=0, upper=[2, self.rocket.motor.burn_out_time], # Mach 2 and burnout samples=[20, 20], disp_type="surface", alpha=1, + filename=filename, + ) + + @staticmethod + def _design_stability_margin(margin): + """The zero-incidence (Mach, time) slice of an angle-of-attack-aware + stability margin, as a 2-D Function for surface plotting.""" + return Function( + lambda mach, time: margin.get_value_opt(0.0, mach, time), + inputs=["Mach", "Time (s)"], + outputs=margin.__outputs__[0], ) # pylint: disable=too-many-statements @@ -179,51 +247,6 @@ def drag_curves(self, *, filename=None): plt.grid(True) show_or_save_plot(filename) - def aerodynamic_coefficients(self, *, filename=None): - """Plots the rocket's total aerodynamic coefficients -- normal force - ``C_N`` and pitch moment ``C_m`` (about the center of dry mass) -- versus - angle of attack, for a range of Mach numbers. The drag coefficient versus - Mach is shown by :meth:`drag_curves`. - - Parameters - ---------- - filename : str | None, optional - The path the plot should be saved to. By default None, in which case - the plot will be shown instead of saved. Supported file endings are: - eps, jpg, jpeg, pdf, pgf, png, ps, raw, rgba, svg, svgz, tif, tiff - and webp (these are the formats supported by matplotlib). - - Returns - ------- - None - """ - alphas_deg = np.linspace(0, 15, 40) - alphas_rad = np.radians(alphas_deg) - - _, (ax1, ax2) = plt.subplots(1, 2, figsize=(11, 4.5)) - for mach in (0.1, 0.5, 0.8, 1.2, 2.0): - coeffs = [ - self.rocket.aerodynamic_coefficients(a, 0.0, mach) for a in alphas_rad - ] - ax1.plot( - alphas_deg, [c["normal_force"] for c in coeffs], label=f"Mach {mach}" - ) - ax2.plot( - alphas_deg, [c["pitch_moment"] for c in coeffs], label=f"Mach {mach}" - ) - - ax1.set_title("Normal Force Coefficient") - ax1.set_xlabel("Angle of Attack (deg)") - ax1.set_ylabel(r"$C_N$") - ax1.legend(loc="best", shadow=True) - ax1.grid(True) - ax2.set_title("Pitch Moment Coefficient (about CDM)") - ax2.set_xlabel("Angle of Attack (deg)") - ax2.set_ylabel(r"$C_m$") - ax2.grid(True) - plt.tight_layout() - show_or_save_plot(filename) - def thrust_to_weight(self): """ Plots the motor thrust force divided by rocket weight as a function of time. @@ -746,67 +769,15 @@ def _draw_center_of_mass_and_pressure(self, ax, plane="xz"): """Draws the center of mass and center of pressure of the rocket. The red dot is the (linear) aerodynamic center, conventionally labeled - the center of pressure. A translucent red band through it shows the - range over which the *nonlinear* center of pressure travels as the - incidence angle grows (angle of attack in the xz plane, sideslip in the - yz plane). + the center of pressure. """ # Draw center of mass and center of pressure cm = self.rocket.center_of_mass(0) ax.scatter(cm, 0, color="#1565c0", label="Center of Mass", s=10) cp = self.rocket.aerodynamic_center(0) - - # Center of pressure travel band: sweep the nonlinear center of - # pressure over the relevant incidence angle and shade its min-max span. - cp_min, cp_max = self._center_of_pressure_range(plane) - if cp_max > cp_min: - ax.plot( - [cp_min, cp_max], - [0, 0], - color="red", - alpha=0.3, - linewidth=4, - solid_capstyle="butt", - zorder=9, - label="Center of Pressure Range", - ) - ax.scatter(cp, 0, label="Center of Pressure", color="red", s=10, zorder=10) - def _center_of_pressure_range(self, plane, max_angle=np.deg2rad(15), samples=31): - """Min and max nonlinear center-of-pressure position over an incidence - sweep. - - Sweeps the angle of attack (xz plane) or sideslip (yz plane) from 0 to - ``max_angle`` and reconstructs the nonlinear center of pressure -- - ``x_cdm + csys * d * Cm / CN`` -- from the rocket aerodynamic - coefficients (:meth:`Rocket.aerodynamic_coefficients_full`), returning - the extent of its travel. The center of pressure is singular at zero - incidence (``CN -> 0``); those samples are skipped. - """ - rocket = self.rocket - csys = rocket._csys - diameter = 2 * rocket.radius - cdm = rocket.center_of_dry_mass_position - angles = np.linspace(0, max_angle, samples) - positions = [] - for angle in angles: - if plane == "yz": - coeffs = rocket.aerodynamic_coefficients_full(0.0, angle, 0.0) - force, moment = coeffs["cY"], coeffs["cn"] - else: - coeffs = rocket.aerodynamic_coefficients_full(angle, 0.0, 0.0) - force, moment = coeffs["cN"], coeffs["cm"] - if force == 0: - continue - position = cdm + csys * diameter * moment / force - if np.isfinite(position): - positions.append(position) - if len(positions) == 0: - return (0.0, 0.0) - return (float(min(positions)), float(max(positions))) - def _draw_sensors(self, ax, sensors, plane): """Draw the sensor as a small thick line at the position of the sensor, with a vector pointing in the direction normal of the sensor. Get the @@ -888,7 +859,6 @@ def all(self): print("Drag Plots") print("-" * 20) # Separator for Drag Plots self.drag_curves() - self.aerodynamic_coefficients() # Stability Plots print("\nStability Plots") diff --git a/rocketpy/prints/flight_prints.py b/rocketpy/prints/flight_prints.py index 5ead46adc..34e63a22b 100644 --- a/rocketpy/prints/flight_prints.py +++ b/rocketpy/prints/flight_prints.py @@ -612,22 +612,36 @@ def stability_margin(self): The stability margin is typically measured in calibers (c), where 1 caliber is the diameter of the rocket. """ + # The stability margin is reported in calibers and, when the rocket has + # a defined overall length, also as a percentage of that length (the + # convention often used in hobby rocketry). See Rocket.length. + rocket = self.flight.rocket + length = rocket.length if rocket.aerodynamic_surfaces else 0 + to_percent = 2 * rocket.radius / length * 100 if length > 0 else None + + def _margin(value): + """Format a margin in calibers, appending the length percentage when + the rocket length is known.""" + if to_percent is None: + return f"{value:.3f} c" + return f"{value:.3f} c ({value * to_percent:.2f}% of length)" + print("\nStability Margin\n") print( - f"Initial Stability Margin: {self.flight.initial_stability_margin:.3f} c " + f"Initial Stability Margin: {_margin(self.flight.initial_stability_margin)} " f"at {self.flight.time[0]:.2f} s" ) print( "Out of Rail Stability Margin: " - f"{self.flight.out_of_rail_stability_margin:.3f} c " + f"{_margin(self.flight.out_of_rail_stability_margin)} " f"at {self.flight.out_of_rail_time:.2f} s" ) print( - f"Maximum Stability Margin: {self.flight.max_stability_margin:.3f} c " + f"Maximum Stability Margin: {_margin(self.flight.max_stability_margin)} " f"at {self.flight.max_stability_margin_time:.2f} s" ) print( - f"Minimum Stability Margin: {self.flight.min_stability_margin:.3f} c " + f"Minimum Stability Margin: {_margin(self.flight.min_stability_margin)} " f"at {self.flight.min_stability_margin_time:.2f} s" ) @@ -637,20 +651,58 @@ def stability_margin(self): if not self.flight.rocket.is_axisymmetric: print( "Out of Rail Stability Margin - yaw: " - f"{self.flight.stability_margin_yaw.get_value_opt(out_of_rail_time):.3f} c" + f"{_margin(self.flight.stability_margin_yaw.get_value_opt(out_of_rail_time))}" ) - # Dynamic stability at rail departure (representative powered condition). + def dynamic_stability(self): + """Prints the rocket's dynamic-stability quantities at the key instants + of the ascent. + + For rail departure and motor burnout it reports the attitude + oscillation's natural frequency and damping ratio (pitch, and yaw as + well for a non-axisymmetric rocket), and it reports the roll rate at + burnout. Companion summary to ``Flight.plots.dynamic_stability_data``. + + Notes + ----- + The natural frequency sets how fast the rocket oscillates after a + disturbance; the damping ratio sets how quickly that oscillation decays + (below 1 is underdamped, the usual case). Roll resonance is a concern + where the roll rate crosses the natural frequency: the + ``dynamic_stability_data`` plot overlays the two so those crossings can + be read off directly. Frequencies are given in Hz. + """ two_pi = 6.283185307179586 - natural_frequency = self.flight.pitch_natural_frequency.get_value_opt( - out_of_rail_time - ) - damping_ratio = self.flight.pitch_damping_ratio.get_value_opt(out_of_rail_time) - print( - f"Pitch Natural Frequency (out of rail): " - f"{natural_frequency / two_pi:.2f} Hz" - ) - print(f"Pitch Damping Ratio (out of rail): {damping_ratio:.3f}") + flight = self.flight + asymmetric = not flight.rocket.is_axisymmetric + + def report(label, time): + natural_frequency = ( + flight.pitch_natural_frequency.get_value_opt(time) / two_pi + ) + damping_ratio = flight.pitch_damping_ratio.get_value_opt(time) + plane = "Pitch " if asymmetric else "" + print( + f"{label} (t = {time:.2f} s): {plane}natural frequency = " + f"{natural_frequency:.2f} Hz, damping ratio = {damping_ratio:.3f}" + ) + if asymmetric: + yaw_frequency = ( + flight.yaw_natural_frequency.get_value_opt(time) / two_pi + ) + yaw_damping = flight.yaw_damping_ratio.get_value_opt(time) + print( + f" Yaw natural frequency = {yaw_frequency:.2f} Hz, " + f"damping ratio = {yaw_damping:.3f}" + ) + + burn_out_time = flight.rocket.motor.burn_out_time + + print("\nDynamic Stability\n") + report("Out of Rail", flight.out_of_rail_time) + report("Burnout", burn_out_time) + roll_rate = abs(flight.w3.get_value_opt(burn_out_time)) / two_pi + print(f"Roll Rate at Burnout: {roll_rate:.2f} Hz") def all(self): """Prints out all data available about the Flight. This method invokes @@ -689,6 +741,9 @@ def all(self): self.stability_margin() print() + self.dynamic_stability() + print() + self.maximum_values() print() diff --git a/rocketpy/prints/rocket_prints.py b/rocketpy/prints/rocket_prints.py index 72a2daf8e..d9021b6e6 100644 --- a/rocketpy/prints/rocket_prints.py +++ b/rocketpy/prints/rocket_prints.py @@ -129,13 +129,29 @@ def rocket_aerodynamics_quantities(self): f"Aerodynamic Center position (Mach=0): " f"{self.rocket.aerodynamic_center(0):.3f} m" ) + # The static margin is reported in calibers and, when the rocket has a + # defined overall length, also as a percentage of that length (the + # convention often used in hobby rocketry). See Rocket.length. + burn_out_time = self.rocket.motor.burn_out_time + length = self.rocket.length if self.rocket.aerodynamic_surfaces else 0 + to_percent = 2 * self.rocket.radius / length * 100 if length > 0 else None + + def _margin(value): + """Format a margin in calibers, appending the length percentage when + the rocket length is known.""" + if to_percent is None: + return f"{value:.3f} c" + return f"{value:.3f} c ({value * to_percent:.2f}% of length)" + + if length > 0: + print(f"Rocket Length: {length:.3f} m") print( f"Initial Static Margin (mach=0, time=0): " - f"{self.rocket.static_margin(0):.3f} c" + f"{_margin(self.rocket.static_margin(0))}" ) print( f"Final Static Margin (mach=0, time=burn_out): " - f"{self.rocket.static_margin(self.rocket.motor.burn_out_time):.3f} c" + f"{_margin(self.rocket.static_margin(burn_out_time))}" ) print( f"Rocket Center of Mass (time=0) - Aerodynamic Center (Mach=0): " @@ -153,11 +169,11 @@ def rocket_aerodynamics_quantities(self): ) print( f"Initial Static Margin - yaw (mach=0, time=0): " - f"{self.rocket.static_margin_yaw(0):.3f} c" + f"{_margin(self.rocket.static_margin_yaw(0))}" ) print( f"Final Static Margin - yaw (mach=0, time=burn_out): " - f"{self.rocket.static_margin_yaw(self.rocket.motor.burn_out_time):.3f} c\n" + f"{_margin(self.rocket.static_margin_yaw(burn_out_time))}\n" ) def parachute_data(self): diff --git a/rocketpy/rocket/aero_surface/_barrowman_surface.py b/rocketpy/rocket/aero_surface/_barrowman_surface.py index addc41364..dc161b9e6 100644 --- a/rocketpy/rocket/aero_surface/_barrowman_surface.py +++ b/rocketpy/rocket/aero_surface/_barrowman_surface.py @@ -96,9 +96,7 @@ def evaluate_coefficients(self): # Axisymmetric Barrowman normal force: equal-magnitude slopes in the # pitch and yaw planes. The yaw-plane (side-force) slope is opposite in # sign due to the body-frame axis convention. - self.cN_alpha = self._mach_coefficient( - lambda mach: clalpha.get_value_opt(mach), "cN_alpha" - ) + self.cN_alpha = self._mach_coefficient(clalpha.get_value_opt, "cN_alpha") self.cY_beta = self._mach_coefficient( lambda mach: -clalpha.get_value_opt(mach), "cY_beta" ) @@ -116,9 +114,7 @@ def evaluate_coefficients(self): self.cl_0 = self._mach_coefficient( lambda mach: clf_delta.get_value_opt(mach) * cant_angle_rad, "cl_0" ) - self.cl_p = self._mach_coefficient( - lambda mach: cld_omega.get_value_opt(mach), "cl_p" - ) + self.cl_p = self._mach_coefficient(cld_omega.get_value_opt, "cl_p") def compute_forces_and_moments( self, @@ -160,8 +156,8 @@ def compute_forces_and_moments( component (``omega[2]``) is used, by fin sets. *args Extra positional arguments accepted for signature compatibility with - the generic surface (``density``, ``dynamic_viscosity``, ``z``, - ``alpha_dot``, ``beta_dot``); unused by the Barrowman model. + the generic surface (``density``, ``dynamic_viscosity``, ``z``); + unused by the Barrowman model. Returns ------- @@ -225,7 +221,6 @@ def _mach_coefficient(self, func_of_mach, name="coefficient"): return AeroCoefficient( func_of_mach, depends_on=("mach",), - unsteady_aero=self._unsteady_aero, control_variables=self.control_variables, name=name, ) diff --git a/rocketpy/rocket/aero_surface/aero_coefficient.py b/rocketpy/rocket/aero_surface/aero_coefficient.py index 264b49a84..09ebe5445 100644 --- a/rocketpy/rocket/aero_surface/aero_coefficient.py +++ b/rocketpy/rocket/aero_surface/aero_coefficient.py @@ -16,20 +16,15 @@ ] -def build_independent_vars(unsteady_aero=False, control_variables=()): +def build_independent_vars(control_variables=()): """Build the ordered independent-variable list of a coefficient/surface. - The seven base axes (``BASE_INDEPENDENT_VARS``), plus ``alpha_dot`` and - ``beta_dot`` when ``unsteady_aero`` is enabled (axes the flight integrator - supplies automatically), plus any ``control_variables`` (axes supplied - externally, e.g. by a controller). Shared by :class:`AeroCoefficient` and - :class:`GenericSurface` so the ordering is defined in exactly one place. + The seven base axes (``BASE_INDEPENDENT_VARS``), plus any + ``control_variables`` (axes supplied externally, e.g. by a controller). + Shared by :class:`AeroCoefficient` and :class:`GenericSurface` so the + ordering is defined in exactly one place. """ - names = list(BASE_INDEPENDENT_VARS) - if unsteady_aero: - names += ["alpha_dot", "beta_dot"] - names += list(control_variables) - return names + return list(BASE_INDEPENDENT_VARS) + list(control_variables) class AeroCoefficient: @@ -40,7 +35,6 @@ def __init__( self, source, depends_on=None, - unsteady_aero=False, control_variables=(), name="coefficient", extrapolation=None, @@ -90,26 +84,17 @@ def __init__( The variables this coefficient actually uses, chosen from the surface's variables: the seven base ones ``"alpha"``, ``"beta"``, ``"mach"``, ``"reynolds"``, ``"pitch_rate"``, ``"yaw_rate"``, - ``"roll_rate"``, plus ``"alpha_dot"`` and ``"beta_dot"`` when - ``unsteady_aero`` is ``True``, plus any names in + ``"roll_rate"``, plus any names in ``control_variables``. List them in the same order as the source's own inputs (a function's arguments, a CSV's columns). For example, ``()`` for a constant, ``("mach",)`` for a Mach-only curve, or the whole list for something that uses every variable. A name that is not one of the surface's variables raises a ``ValueError``. Leave it as ``None`` (the default) to have it worked out from ``source``. - unsteady_aero : bool, optional - Whether the coefficient can also depend on how fast the flow angles - are changing. When ``True``, two more variables, ``alpha_dot`` and - ``beta_dot`` (the rates of change of the angle of attack and - sideslip), are added after the seven base variables. The simulation - fills these in, using ``0`` when it does not compute them, so - ordinary coefficients keep working. This must match the surface the - coefficient belongs to. Default ``False``. control_variables : sequence of str, optional Names of extra variables, such as control-surface deflections set by - a controller. They are added after the base (and unsteady) variables, - in the order given. Empty for ordinary surfaces. Default ``()``. + a controller. They are added after the seven base variables, in the + order given. Empty for ordinary surfaces. Default ``()``. name : str, optional A readable name for the coefficient (e.g. ``"cL_alpha"`` or ``"Drag Coefficient with Power Off"``). It appears in error messages, @@ -142,18 +127,13 @@ def __init__( self.name = name self.extrapolation = extrapolation self.interpolation = interpolation - self.unsteady_aero = unsteady_aero self.control_variables = tuple(control_variables) - # ``unsteady_aero`` and ``control_variables`` define the full ordered - # variable list: every coefficient's argument order and each variable's - # position. This is a surface-wide property, distinct from ``depends_on`` - # (the subset a single coefficient reads), and it is passed in rather - # than derived from ``depends_on``: inferring ``depends_on`` already - # needs this list, and the unsteady axes shift the position of the - # control variables even for coefficients that never use the rates. - self.independent_vars = tuple( - build_independent_vars(unsteady_aero, control_variables) - ) + # ``control_variables`` completes the full ordered variable list: every + # coefficient's argument order and each variable's position. This is a + # surface-wide property, distinct from ``depends_on`` (the subset a + # single coefficient reads), and it is passed in rather than derived + # from ``depends_on``: inferring ``depends_on`` already needs this list. + self.independent_vars = tuple(build_independent_vars(control_variables)) # Infer the stored source and its dependencies from the raw input when # ``depends_on`` is not given. if depends_on is None: @@ -429,8 +409,8 @@ def _infer_single_var(function, independent_vars): return independent_vars[0] label_lower = str(label).lower() # Exact match first; then substring, longest variable name first, so a - # label like "alpha_dot" binds to "alpha_dot" rather than the shorter - # substring "alpha". + # label containing a longer variable name binds to it rather than to a + # shorter variable that happens to be a substring of it. for var in independent_vars: if var == label_lower: return var @@ -517,7 +497,6 @@ def __mul__(self, other): return AeroCoefficient( source * other, self.depends_on, - self.unsteady_aero, self.control_variables, self.name, extrapolation=self.extrapolation, @@ -526,31 +505,6 @@ def __mul__(self, other): __rmul__ = __mul__ - def to_dict(self, **kwargs): # pylint: disable=unused-argument - """Serialize the coefficient for :class:`rocketpy._encoders.RocketPyEncoder`.""" - return { - "source": self._constant if self._constant is not None else self.function, - "depends_on": list(self.depends_on), - "unsteady_aero": self.unsteady_aero, - "control_variables": list(self.control_variables), - "name": self.name, - "extrapolation": self.extrapolation, - "interpolation": self.interpolation, - } - - @classmethod - def from_dict(cls, data): - """Rebuild an :class:`AeroCoefficient` from its :meth:`to_dict` form.""" - return cls( - data["source"], - data["depends_on"], - data.get("unsteady_aero", False), - data.get("control_variables", ()), - data["name"], - extrapolation=data.get("extrapolation"), - interpolation=data.get("interpolation"), - ) - def __repr__(self): """Return a concise representation showing the constant or dependencies.""" if self._constant is not None: @@ -614,3 +568,88 @@ def sliced(*values): for var in free_variables ) return Function(sliced, list(free_variables), [self.name]) + + def slope(self, variable, *free_variables, at=None, dx=1e-6): + """Return the derivative of this coefficient with respect to one variable + as a :class:`Function` of the chosen free variables. + + This is the aerodynamic slope, such as a lift-curve slope. For example, + ``cL.slope("alpha", "mach")`` is the lift-curve slope ``d(cL)/d(alpha)`` + as a function of Mach, taken at ``alpha = 0`` with sideslip, Reynolds + number and the rotation rates held at zero. + + Parameters + ---------- + variable : str + Name of the variable to differentiate with respect to (for example + ``"alpha"`` or ``"beta"``). Must be one of this coefficient's + independent variables, and must not also appear in + ``free_variables``. + *free_variables : str + Names of the variables to keep as inputs of the resulting slope, in + the order you want them (for example ``"mach"``). Each must be one of + this coefficient's independent variables. Leave empty to get the + slope at a single point. + at : dict, optional + Values to hold the remaining variables at, keyed by variable name. + The value for ``variable`` is the point the derivative is taken at + (default 0, the linearization point). Any variable not listed is held + at 0. + dx : float, optional + Step size used for the numerical differentiation. Default 1e-6. + + Returns + ------- + Function + A Function of ``free_variables`` giving ``d(self)/d(variable)`` with + the remaining variables held fixed. + """ + fixed = dict(at or {}) + overlap = [var for var in free_variables if var == variable] + if overlap: + raise ValueError( + f"{variable!r} cannot be both differentiated and kept free." + ) + # Point to differentiate at, defaulting to the linearization point (0). + diff_point = fixed.pop(variable, 0.0) + name = f"d({self.name})/d({variable})" + + def evaluate_slope(*free_values): + # Hold the fixed variables and the current free-variable values, + # keep only ``variable`` free, and differentiate along it. + slice_at = dict(fixed) + for var, value in zip(free_variables, free_values): + slice_at[var] = value + return self.slice(variable, at=slice_at).differentiate(diff_point, dx=dx) + + if not free_variables: + return Function(evaluate_slope(), name) + + evaluate_slope.__signature__ = inspect.Signature( + inspect.Parameter(var, inspect.Parameter.POSITIONAL_OR_KEYWORD) + for var in free_variables + ) + return Function(evaluate_slope, list(free_variables), [name]) + + def to_dict(self, **kwargs): # pylint: disable=unused-argument + """Serialize the coefficient for :class:`rocketpy._encoders.RocketPyEncoder`.""" + return { + "source": self._constant if self._constant is not None else self.function, + "depends_on": list(self.depends_on), + "control_variables": list(self.control_variables), + "name": self.name, + "extrapolation": self.extrapolation, + "interpolation": self.interpolation, + } + + @classmethod + def from_dict(cls, data): + """Rebuild an :class:`AeroCoefficient` from its :meth:`to_dict` form.""" + return cls( + data["source"], + data["depends_on"], + data.get("control_variables", ()), + data["name"], + extrapolation=data.get("extrapolation"), + interpolation=data.get("interpolation"), + ) diff --git a/rocketpy/rocket/aero_surface/controllable_generic_surface.py b/rocketpy/rocket/aero_surface/controllable_generic_surface.py index 52ad1ae1f..f312d95c6 100644 --- a/rocketpy/rocket/aero_surface/controllable_generic_surface.py +++ b/rocketpy/rocket/aero_surface/controllable_generic_surface.py @@ -1,4 +1,5 @@ from rocketpy.rocket.aero_surface.generic_surface import GenericSurface +from rocketpy.tools import from_hex_decode, to_hex_encode class ControllableGenericSurface(GenericSurface): @@ -58,8 +59,10 @@ def __init__( center_of_pressure=(0, 0, 0), name="Controllable Generic Surface", controls=("deflection",), + reynolds_length=None, extrapolation=None, interpolation=None, + active_during="always", ): """Create a controllable generic aerodynamic surface. @@ -84,6 +87,10 @@ def __init__( Names of the controls, such as a canard deflection angle. Default ``("deflection",)``. Each name becomes an extra input to every coefficient and a key in :attr:`control_state`. + reynolds_length : int, float, optional + Length scale, in meters, of the Reynolds number passed to the + coefficients. See :class:`GenericSurface`. ``None`` (the default) + uses ``reference_length`` (the diameter). extrapolation : str or dict, optional What tabulated coefficients do outside their data range: ``"constant"`` holds the nearest edge value, ``"natural"`` keeps @@ -98,6 +105,11 @@ def __init__( Give one string for all coefficients or a dict keyed by coefficient name. ``None`` (the default) uses ``"linear"`` for tables built here and leaves a pre-built :class:`Function` unchanged. + active_during : str or callable, optional + When this surface produces force during a simulation: ``"always"`` + (default), ``"power_on"`` (only while the motor burns, e.g. jet + vanes), ``"power_off"`` (only after burnout), or a function + ``active_during(t, flight)``. See :class:`GenericSurface` for details. """ # These must be set before ``super().__init__`` so coefficient # processing (arity, CSV validation) and the derived-cp accessors see @@ -112,8 +124,10 @@ def __init__( coefficients=coefficients, center_of_pressure=center_of_pressure, name=name, + reynolds_length=reynolds_length, extrapolation=extrapolation, interpolation=interpolation, + active_during=active_during, ) # ``self.prints``/``self.plots`` are the generic ones wired by the base. @@ -126,12 +140,9 @@ def _coefficient_arguments( pitch_rate, yaw_rate, roll_rate, - alpha_dot=0.0, - beta_dot=0.0, ): """Append the current control-variable values (in - ``self.control_variables`` order) to the standard inputs (which may - already include the unsteady ``alpha_dot``/``beta_dot`` axes).""" + ``self.control_variables`` order) to the standard inputs.""" base = super()._coefficient_arguments( alpha, beta, @@ -140,8 +151,6 @@ def _coefficient_arguments( pitch_rate, yaw_rate, roll_rate, - alpha_dot, - beta_dot, ) controls = tuple(self.control_state[name] for name in self.control_variables) return base + controls @@ -176,6 +185,16 @@ def get_control(self, name): def to_dict( # pylint: disable=unused-argument self, include_outputs=False, **kwargs ): + # A preset ``active_during`` is stored as is; a custom (t, flight) -> bool + # function is pickled to text when allowed, otherwise dropped to "always" + # (a function cannot be restored without pickling). + active_during = self.active_during + if callable(active_during): + active_during = ( + to_hex_encode(active_during) + if kwargs.get("allow_pickle", True) + else "always" + ) return { "reference_area": self.reference_area, "reference_length": self.reference_length, @@ -190,10 +209,21 @@ def to_dict( # pylint: disable=unused-argument "center_of_pressure": self.center_of_pressure, "name": self.name, "controls": self.control_variables, + "reynolds_length": self.reynolds_length, + "active_during": active_during, } @classmethod def from_dict(cls, data): + # A preset ``active_during`` is used as is; anything else is unpickled + # back into the original function (falling back to "always" if it cannot + # be restored). + active_during = data.get("active_during", "always") + if active_during not in ("always", "power_on", "power_off"): + try: + active_during = from_hex_decode(active_during) + except (TypeError, ValueError): + active_during = "always" return cls( reference_area=data["reference_area"], reference_length=data["reference_length"], @@ -201,4 +231,6 @@ def from_dict(cls, data): center_of_pressure=data.get("center_of_pressure", (0, 0, 0)), name=data.get("name", "Controllable Generic Surface"), controls=data.get("controls", ("deflection",)), + reynolds_length=data.get("reynolds_length"), + active_during=active_during, ) diff --git a/rocketpy/rocket/aero_surface/fins/_base_fin.py b/rocketpy/rocket/aero_surface/fins/_base_fin.py index 9b2b7a536..0f2fa7864 100644 --- a/rocketpy/rocket/aero_surface/fins/_base_fin.py +++ b/rocketpy/rocket/aero_surface/fins/_base_fin.py @@ -9,6 +9,13 @@ from ..linear_generic_surface import LinearGenericSurface +# TODO: review note: airfoil handling can now be fully implemented. That is +# instead of getting just the clalpha from the airfoil, we can get and use the +# full lift curve. We need to check if simulation does not break with this +# change. If we use a full curve for airfoils, then we will have fin stall +# (abrupt cN drop), but the other surfaces do not have this drop, they simply +# will have their generated normal forces grow linearly with AoA, this can lead +# to wrong behaviour at high AoA. class _BaseFin(_BarrowmanSurface): """ Base class for fins, shared by both Fin and Fins classes. diff --git a/rocketpy/rocket/aero_surface/fins/fin.py b/rocketpy/rocket/aero_surface/fins/fin.py index c9ca3e490..f5d71e932 100644 --- a/rocketpy/rocket/aero_surface/fins/fin.py +++ b/rocketpy/rocket/aero_surface/fins/fin.py @@ -380,8 +380,8 @@ def compute_forces_and_moments( Tuple containing angular velocities around the x, y, z axes. *args Extra positional arguments accepted for signature compatibility with - the generic surface (e.g. ``density``, ``dynamic_viscosity``, ``z``, - ``alpha_dot``, ``beta_dot``). Unused by the fin's Barrowman model. + the generic surface (e.g. ``density``, ``dynamic_viscosity``, + ``z``). Unused by the fin's Barrowman model. Returns ------- diff --git a/rocketpy/rocket/aero_surface/generic_surface.py b/rocketpy/rocket/aero_surface/generic_surface.py index decb832b6..174a95836 100644 --- a/rocketpy/rocket/aero_surface/generic_surface.py +++ b/rocketpy/rocket/aero_surface/generic_surface.py @@ -1,4 +1,3 @@ -import copy import inspect import math @@ -12,6 +11,7 @@ AeroCoefficient, build_independent_vars, ) +from rocketpy.tools import from_hex_decode, to_hex_encode def _as_function(func, independent_vars, name): @@ -108,10 +108,11 @@ def __init__( coefficients, center_of_pressure=(0, 0, 0), name="Generic Surface", - unsteady_aero=False, + reynolds_length=None, interpolation=None, extrapolation=None, force_convention=None, + active_during="always", ): """Create a generic aerodynamic surface, defined by its aerodynamic coefficients. This surface is used to model any aerodynamic surface @@ -126,18 +127,19 @@ def __init__( "reynolds", "pitch_rate", "yaw_rate" and "roll_rate". The independent variable columns can be provided in any order. - When ``unsteady_aero`` is True, the coefficients may additionally be - functions of the flow-angle rates "alpha_dot" and "beta_dot", which are - appended (in that order) after "roll_rate": callables must accept the - two extra trailing arguments and CSV files may include "alpha_dot" and - "beta_dot" columns. + The Reynolds number ("reynolds") is by default built on the reference + length (the rocket diameter). Published rocket data and tools often base + Reynolds on the **body length** instead, which for a slender rocket is + much larger (Re scales with the chosen length). If your coefficient + table uses a different length than the reference length, pass that + length as ``reynolds_length`` so the Reynolds number the simulation + feeds your table matches the one it was built against. The angular-rate inputs ("pitch_rate", "yaw_rate", "roll_rate") are the - conventional **non-dimensional reduced rates**, ``q* = q * L_ref / (2 * V)`` - (and likewise for ``r``/``p``), matching how published and tool-generated - aerotables (Missile DATCOM, OpenVSP, CFD/wind-tunnel data) tabulate rate - derivatives. Provide coefficient tables against the reduced rates, not the - raw body rates in rad/s. + conventional **non-dimensional reduced rates**, + ``q* = q * L_ref / (2 * V)`` (and likewise for ``r``/``p``). + Provide coefficient tables against the reduced rates, not the raw body + rates in rad/s. See Also -------- @@ -149,8 +151,10 @@ def __init__( Reference area of the aerodynamic surface. Has the unit of meters squared. Commonly defined as the rocket's cross-sectional area. reference_length : int, float - Reference length of the aerodynamic surface. Has the unit of meters. - Commonly defined as the rocket's diameter. + Reference length of the aerodynamic surface, in meters. Commonly the + rocket's diameter. Used to non-dimensionalize the moment coefficients + and the reduced rotation rates, and (unless ``reynolds_length`` is + given) as the length scale of the Reynolds number. coefficients: dict The six force and moment coefficients, by name. Any you leave out are set to 0. Each one can be a constant number, a function of the flow @@ -176,12 +180,13 @@ def __init__( aerodynamic surface. The default value is (0, 0, 0). name : str, optional Name of the aerodynamic surface. Default is 'Generic Surface'. - unsteady_aero : bool, optional - If True, the coefficients additionally depend on the time - derivatives of the flow angles, and ``alpha_dot`` and ``beta_dot`` - are appended (in that order) to the independent variables. CSV files - may then include "alpha_dot"/"beta_dot" columns, and callables must - accept the two extra trailing arguments. Default is False. + reynolds_length : int, float, optional + Length scale, in meters, of the Reynolds number passed to the + coefficients. Set it to the length your Reynolds-dependent + coefficient data was tabulated against (for example the rocket's + body length, if your table uses a length-based Reynolds number). + ``None`` (the default) uses ``reference_length`` (the diameter). Has + no effect unless a coefficient actually depends on "reynolds". interpolation : str or dict, optional How tabulated coefficients interpolate between points. The accepted methods depend on the coefficient's dimensionality: a 1-D table @@ -191,77 +196,72 @@ def __init__( multi-dimensional table on a regular Cartesian grid accepts ``"linear"``, ``"nearest"``, ``"slinear"``, ``"cubic"``, ``"quintic"`` and ``"pchip"`` (with ``"spline"`` mapped to - ``"cubic"`` and ``"akima"`` to ``"pchip"``). Accepts either a simple - string or a dict keyed by coefficient name (names left out fall back - to the default). ``None`` (the default) uses ``"linear"`` for tables - built here and keeps a pre-built ``Function``'s own setting. + ``"cubic"`` and ``"akima"`` to ``"pchip"``). Pass a single string to + use that method for every coefficient, or a dict keyed by coefficient + name to set them individually (coefficients left out of the dict fall + back to the default). ``None`` (the default) uses ``"linear"`` for + tables built here and keeps a pre-built ``Function``'s own setting. extrapolation : str or dict, optional How tabulated coefficients behave outside their data range: ``"constant"`` holds the value at the nearest data edge, ``"natural"`` keeps following the curve, and ``"zero"`` returns 0. - Accepts either a simple string or a dict keyed by coefficient name - (names left out fall back to the default). ``None`` (the default) - uses ``"constant"`` for tables built here and keeps whatever a - pre-built ``Function`` already carries. Only affects tabulated + Pass a single string to use that method for every coefficient, or a + dict keyed by coefficient name to set them individually (coefficients + left out of the dict fall back to the default). ``None`` (the + default) uses ``"constant"`` for tables built here and keeps whatever + a pre-built ``Function`` already carries. Only affects tabulated sources (constants and callables are evaluated directly). force_convention : str, optional The frame your force coefficients are given in. ``"wind"`` for the - aerodynamic-frame coefficients ``cL`` (lift), ``cQ`` (side) and + wind-frame coefficients ``cL`` (lift), ``cQ`` (side) and ``cD`` (drag); ``"body"`` for the body-frame coefficients ``cN`` (normal), ``cY`` (side) and ``cA`` (axial), the convention used by - Missile DATCOM, wind tunnels and Barrowman. The moment coefficients + DATCOM, wind tunnels and Barrowman. The moment coefficients (``cm``, ``cn``, ``cl``) are the same in both. ``None`` (the default) infers the frame from the coefficient names you pass. Whichever frame you use, all nine coefficients are available as attributes afterwards (the other frame is computed on demand). + active_during : str or callable, optional + When this surface produces aerodynamic force during a simulation. + Use it to model a surface that is only present in part of the flight, + such as jet vanes that only work while the motor burns, or a base + drag that only appears after burnout. Accepts: + + - ``"always"`` (default): the surface always contributes force. + - ``"power_on"``: only while the motor is burning (up to the motor's + burn-out time). + - ``"power_off"``: only after the motor has burned out. + - a function ``active_during(t, flight)`` returning ``True`` when the + surface is active at time ``t`` (in seconds) of the given + :class:`Flight`. Use this for any custom window. """ - self._unsteady_aero = unsteady_aero # Externally-supplied axes (e.g. control deflections). Subclasses set # this before ``super().__init__``. Defaults to none for plain surfaces. self.control_variables = getattr(self, "control_variables", ()) # Ordered independent variables accepted by every coefficient: the seven - # base axes, plus ``alpha_dot``/``beta_dot`` when ``unsteady_aero`` is - # enabled, plus any ``control_variables`` - self.independent_vars = build_independent_vars( - self._unsteady_aero, self.control_variables - ) + # base axes, plus any ``control_variables`` + self.independent_vars = build_independent_vars(self.control_variables) self.reference_area = reference_area self.reference_length = reference_length + self.reynolds_length = ( + reference_length if reynolds_length is None else reynolds_length + ) self.center_of_pressure = center_of_pressure self.cp = center_of_pressure self.cpx = center_of_pressure[0] self.cpy = center_of_pressure[1] self.cpz = center_of_pressure[2] self.name = name + self.active_during = self._validate_active_during(active_during) + self.is_active = self._build_activation_check(self.active_during) self._rotation_surface_to_body = self._default_surface_rotation() - default_coefficients = self._get_default_coefficients() - self.force_convention = self._resolve_force_convention( - coefficients, force_convention + self._build_coefficients( + coefficients, interpolation, extrapolation, force_convention ) - # Wind-frame force input (cL/cQ/cD) is converted once to the canonical - # body-frame coefficients before validation. Each surface supplies the - # conversion appropriate to its coefficients: the generic surface rotates - # the full force coefficients, while the linear model recombines the - # coefficient derivatives (see LinearGenericSurface._wind_input_to_body). - # A non-dict input falls through to _check_coefficients, which rejects it. - if self.force_convention == "wind" and isinstance(coefficients, dict): - coefficients = self._wind_input_to_body(coefficients) - self._check_coefficients(coefficients, default_coefficients) - coefficients = self._complete_coefficients(coefficients, default_coefficients) - for coeff, coeff_value in coefficients.items(): - value = AeroCoefficient( - coeff_value, - unsteady_aero=self._unsteady_aero, - control_variables=self.control_variables, - name=coeff, - extrapolation=self._coefficient_option(extrapolation, coeff), - interpolation=self._coefficient_option(interpolation, coeff), - ) - setattr(self, coeff, value) self.evaluate_coefficients() self._evaluate_stability_derivatives() @@ -280,6 +280,47 @@ def _default_surface_rotation(self): """ return Matrix([[1, 0, 0], [0, 1, 0], [0, 0, 1]]) + @staticmethod + def _validate_active_during(active_during): + """Check the ``active_during`` policy and return it unchanged. + + Accepts one of the preset strings ``"always"``, ``"power_on"``, + ``"power_off"`` or a callable ``(t, flight) -> bool``; anything else + raises a ``ValueError`` so a typo is caught at construction rather than + silently keeping the surface active. + """ + if callable(active_during) or active_during in ( + "always", + "power_on", + "power_off", + ): + return active_during + raise ValueError( + "`active_during` must be one of 'always', 'power_on', 'power_off' " + "or a callable(t, flight) -> bool; " + f"got {active_during!r}." + ) + + @staticmethod + def _build_activation_check(active_during): + """Resolve an ``active_during`` policy into the ``is_active(t, flight)`` + function the flight integrator calls for every surface each step to skip + the ones that are not currently active. + + Resolving it once here keeps that per-step check free of policy + branching. A custom callable is used unchanged; each preset becomes a + small function of the simulation time ``t`` (in seconds) and the + ``flight`` being run, and ``"always"`` becomes a function that simply + returns ``True``. + """ + if callable(active_during): + return active_during + if active_during == "power_on": + return lambda t, flight: t < flight.rocket.motor.burn_out_time + if active_during == "power_off": + return lambda t, flight: t >= flight.rocket.motor.burn_out_time + return lambda t, flight: True # "always" + @property def force_application_point(self): """Local point (surface frame) at which the resultant force is applied @@ -290,7 +331,7 @@ def force_application_point(self): return Vector([self.cpx, self.cpy, self.cpz]) @property - def cL(self): # pylint: disable=invalid-name + def cL(self): """Wind-frame lift coefficient, as a :class:`Function` of the surface's independent variables. Derived from the canonical body-frame ``cN``, ``cY`` and ``cA`` by the angle-of-attack/sideslip rotation.""" @@ -299,40 +340,19 @@ def cL(self): # pylint: disable=invalid-name )[0] @property - def cD(self): # pylint: disable=invalid-name + def cD(self): """Wind-frame drag coefficient (derived from ``cN``/``cY``/``cA``).""" return body_to_wind_coefficients( self.cN, self.cY, self.cA, self.independent_vars )[1] @property - def cQ(self): # pylint: disable=invalid-name + def cQ(self): """Wind-frame side-force coefficient (derived from ``cN``/``cY``/``cA``).""" return body_to_wind_coefficients( self.cN, self.cY, self.cA, self.independent_vars )[2] - def info(self): - """Prints a summary of the surface's geometry and aerodynamic - coefficients. Subclasses override this with surface-specific summaries. - - Returns - ------- - None - """ - self.prints.geometry() - self.prints.coefficients() - - def all_info(self): - """Prints and plots all available information of the surface. - - Returns - ------- - None - """ - self.prints.all() - self.plots.all() - def evaluate_coefficients(self): """Hook for subclasses to (re)populate the aerodynamic coefficient ``Function``s from their geometry. The base class builds coefficients @@ -362,41 +382,31 @@ def _evaluate_stability_derivatives(self): ------- None """ - self.cN_alpha = self._derivative_coefficient(self.cN, "alpha", "cN_alpha") - self.cm_alpha = self._derivative_coefficient(self.cm, "alpha", "cm_alpha") - self.cY_beta = self._derivative_coefficient(self.cY, "beta", "cY_beta") - self.cn_beta = self._derivative_coefficient(self.cn, "beta", "cn_beta") - self._set_stability_accessors() - - def _derivative_coefficient(self, coefficient, axis, name): - """Numerically differentiate ``coefficient`` along ``axis`` at the - linearization point and wrap the Mach-only result as an - :class:`AeroCoefficient`, so every surface exposes ``cN_alpha`` and its - siblings in the same form (a coefficient callable over the full - argument tuple that depends only on Mach). - - Parameters - ---------- - coefficient : AeroCoefficient - The force or moment coefficient to differentiate. - axis : str - Either ``"alpha"`` or ``"beta"``. - name : str - Name of the resulting derivative coefficient. - - Returns - ------- - AeroCoefficient - The Mach-only derivative ``d(coefficient)/d(axis)``. - """ - slope = self._partial_slope(coefficient, axis=axis) - return AeroCoefficient( - slope, + self.cN_alpha = AeroCoefficient( + self.cN.slope("alpha", "mach"), + depends_on=("mach",), + control_variables=self.control_variables, + name="cN_alpha", + ) + self.cm_alpha = AeroCoefficient( + self.cm.slope("alpha", "mach"), depends_on=("mach",), - unsteady_aero=self._unsteady_aero, control_variables=self.control_variables, - name=name, + name="cm_alpha", ) + self.cY_beta = AeroCoefficient( + self.cY.slope("beta", "mach"), + depends_on=("mach",), + control_variables=self.control_variables, + name="cY_beta", + ) + self.cn_beta = AeroCoefficient( + self.cn.slope("beta", "mach"), + depends_on=("mach",), + control_variables=self.control_variables, + name="cn_beta", + ) + self._set_stability_accessors() def _set_stability_accessors(self): """Build the pitch- and yaw-plane center-of-pressure accessors from the @@ -432,49 +442,6 @@ def cp_z(mach): self.center_of_pressure_z = _cp_z(self.cN_alpha, self.cm_alpha) self.center_of_pressure_z_yaw = _cp_z(self.cY_beta, self.cn_beta) - def _partial_slope(self, coefficient, axis): - """Partial derivative ``d(coefficient)/d(axis)`` at ``alpha = beta = 0`` - and zero rates, returned as a mach-only ``Function``. - - Reuses :meth:`Function.differentiate` on a single-variable slice of the - coefficient taken along ``axis`` (``"alpha"`` or ``"beta"``) with all - other base inputs frozen at zero. Extra axes (control deflections) are - frozen at their current value via :meth:`_coefficient_arguments`. - - Parameters - ---------- - coefficient : Function - A coefficient ``Function`` over ``self.independent_vars``. - axis : str - Either ``"alpha"`` or ``"beta"``. - - Returns - ------- - Function - ``d(coefficient)/d(axis)`` evaluated at the zero point, vs. mach. - """ - - def slope(mach): - if axis == "alpha": - sliced = Function( - lambda alpha: coefficient( - *self._coefficient_arguments( - alpha, 0.0, mach, 0.0, 0.0, 0.0, 0.0 - ) - ) - ) - else: - sliced = Function( - lambda beta: coefficient( - *self._coefficient_arguments( - 0.0, beta, mach, 0.0, 0.0, 0.0, 0.0 - ) - ) - ) - return sliced.differentiate(0) - - return Function(slope, "Mach", "Coefficient derivative") - @staticmethod def _coefficient_option(option, coeff_name): """Resolve a per-coefficient interpolation/extrapolation setting. @@ -553,7 +520,6 @@ def _wind_input_to_body(self, coefficients): def as_coefficient(source, name): return AeroCoefficient( source, - unsteady_aero=self._unsteady_aero, control_variables=self.control_variables, name=name, ) @@ -566,6 +532,60 @@ def as_coefficient(source, name): ) return {"cN": c_normal, "cY": c_yaw, "cA": c_axial, **passthrough} + def _build_coefficients( + self, coefficients, interpolation, extrapolation, force_convention + ): + """Resolve the force-coefficient frame and store the surface's + aerodynamic coefficients as :class:`AeroCoefficient` attributes. + + Runs the full coefficient setup from the user input: picks the force + frame, converts a wind-frame input to the canonical body frame, fills in + any coefficient the user left out with its default (0), and stores each + one as an attribute (``self.cN``, ``self.cm``, ...). + + Parameters + ---------- + coefficients : dict + The user-provided coefficients (see :meth:`__init__`). + interpolation, extrapolation : str, dict, or None + The interpolation/extrapolation settings (see :meth:`__init__`). + force_convention : str or None + The frame the input force coefficients are given in, or ``None`` to + infer it from the coefficient names. + """ + default_coefficients = self._get_default_coefficients() + self.force_convention = self._resolve_force_convention( + coefficients, force_convention + ) + # Wind-frame force input (cL/cQ/cD) is converted once to the canonical + # body-frame coefficients before validation. Each surface supplies the + # conversion appropriate to its coefficients: the generic surface rotates + # the full force coefficients, while the linear model recombines the + # coefficient derivatives (see LinearGenericSurface._wind_input_to_body). + # A non-dict input falls through to _check_coefficients, which rejects it. + if self.force_convention == "wind" and isinstance(coefficients, dict): + coefficients = self._wind_input_to_body(coefficients) + self._check_coefficients(coefficients, default_coefficients) + coefficients = self._complete_coefficients(coefficients, default_coefficients) + + # ``_needs_reynolds`` lets the flight loop skip the per-step atmosphere + # lookups when no coefficient uses the Reynolds number. Only these + # primary coefficients are checked: they are what the surface evaluates, + # and the linear model's combined coefficients are linear combinations of + # them, so a Reynolds dependence always shows up here. + self._needs_reynolds = False + for coeff, coeff_value in coefficients.items(): + value = AeroCoefficient( + coeff_value, + control_variables=self.control_variables, + name=coeff, + extrapolation=self._coefficient_option(extrapolation, coeff), + interpolation=self._coefficient_option(interpolation, coeff), + ) + setattr(self, coeff, value) + if "reynolds" in value.depends_on: + self._needs_reynolds = True + def _get_default_coefficients(self): """Returns default coefficients @@ -603,9 +623,13 @@ def _complete_coefficients(self, input_coefficients, default_coefficients): coefficients : dict Coefficients dictionary used to setup coefficient attributes """ - coefficients = copy.deepcopy(input_coefficients) + # Shallow copy: only missing keys are added, so the user's dict is left + # intact. The values are not mutated here (each is wrapped in an + # AeroCoefficient, which copies it when it needs its own settings), so + # there is no need to deep-copy potentially large tabulated coefficients. + coefficients = dict(input_coefficients) for coeff, value in default_coefficients.items(): - if coeff not in coefficients.keys(): + if coeff not in coefficients: coefficients[coeff] = value return coefficients @@ -645,8 +669,6 @@ def _compute_from_coefficients( pitch_rate, yaw_rate, roll_rate, - alpha_dot=0.0, - beta_dot=0.0, ): """Compute the aerodynamic forces and moments from the aerodynamic coefficients. @@ -671,12 +693,6 @@ def _compute_from_coefficients( Non-dimensional (reduced) yaw rate, ``r * L_ref / (2 * V)``. roll_rate : float Non-dimensional (reduced) roll rate, ``p * L_ref / (2 * V)``. - alpha_dot : float, optional - Non-dimensional angle-of-attack rate, used by unsteady surfaces. - Defaults to 0. - beta_dot : float, optional - Non-dimensional sideslip-angle rate, used by unsteady surfaces. - Defaults to 0. Returns ------- @@ -689,8 +705,7 @@ def _compute_from_coefficients( dyn_pressure_area_length = dyn_pressure_area * self.reference_length # Coefficient arguments (base 7 vars, plus any extra axes appended by - # subclasses such as control deflections or the unsteady alpha_dot/ - # beta_dot terms). + # subclasses such as control deflections). args = self._coefficient_arguments( alpha, beta, @@ -699,8 +714,6 @@ def _compute_from_coefficients( pitch_rate, yaw_rate, roll_rate, - alpha_dot, - beta_dot, ) # Body-frame force components straight from the body-frame coefficients @@ -708,16 +721,16 @@ def _compute_from_coefficients( normal = dyn_pressure_area * self.cN(*args) yaw_side = dyn_pressure_area * self.cY(*args) axial = dyn_pressure_area * self.cA(*args) - r1 = yaw_side - r2 = -normal - r3 = -axial + R1 = yaw_side + R2 = -normal + R3 = -axial # Compute aerodynamic moments pitch = dyn_pressure_area_length * self.cm(*args) yaw = dyn_pressure_area_length * self.cn(*args) roll = dyn_pressure_area_length * self.cl(*args) - return r1, r2, r3, pitch, yaw, roll + return R1, R2, R3, pitch, yaw, roll def _coefficient_arguments( self, @@ -728,19 +741,13 @@ def _coefficient_arguments( pitch_rate, yaw_rate, roll_rate, - alpha_dot=0.0, - beta_dot=0.0, ): """Returns the argument tuple passed to every coefficient ``Function``, in ``self.independent_vars`` order. The base class provides the seven - standard inputs, plus ``alpha_dot``/``beta_dot`` when ``unsteady_aero`` - is enabled. Subclasses (e.g. :class:`ControllableGenericSurface`) + standard inputs. Subclasses (e.g. :class:`ControllableGenericSurface`) override this to append further axes such as control deflections. """ - base = (alpha, beta, mach, reynolds, pitch_rate, yaw_rate, roll_rate) - if self._unsteady_aero: - return base + (alpha_dot, beta_dot) - return base + return (alpha, beta, mach, reynolds, pitch_rate, yaw_rate, roll_rate) def compute_forces_and_moments( self, @@ -753,8 +760,6 @@ def compute_forces_and_moments( density, dynamic_viscosity, z, - alpha_dot=0.0, - beta_dot=0.0, ): """Computes the forces and moments acting on the aerodynamic surface. Used in each time step of the simulation. This method is valid for @@ -783,12 +788,6 @@ def compute_forces_and_moments( z : float Altitude of the surface, used to evaluate ``density`` and ``dynamic_viscosity``. - alpha_dot : float, optional - Non-dimensional angle-of-attack rate, used by unsteady surfaces. - Defaults to 0. - beta_dot : float, optional - Non-dimensional sideslip-angle rate, used by unsteady surfaces. - Defaults to 0. Returns ------- @@ -797,16 +796,24 @@ def compute_forces_and_moments( (pitch, yaw, roll) in the body frame. """ # Reynolds number at the surface altitude. Computed here (rather than in - # the flight loop) since it is only needed by generic surfaces. - comp_density = density.get_value_opt(z) - comp_dynamic_viscosity = dynamic_viscosity.get_value_opt(z) - reynolds = ( - comp_density * stream_speed * self.reference_length / comp_dynamic_viscosity - if comp_dynamic_viscosity > 0 - else 0 - ) + # the flight loop) since it is only needed by generic surfaces, and only + # when a coefficient actually depends on it -- otherwise the two + # atmosphere lookups are skipped for every surface, every step. + if self._needs_reynolds: + comp_density = density.get_value_opt(z) + comp_dynamic_viscosity = dynamic_viscosity.get_value_opt(z) + reynolds = ( + comp_density + * stream_speed + * self.reynolds_length + / comp_dynamic_viscosity + if comp_dynamic_viscosity > 0 + else 0 + ) + else: + reynolds = 0.0 - # Stream velocity in standard aerodynamic frame + # Stream velocity in standard wind frame stream_velocity = -stream_velocity # Angles of attack and sideslip @@ -833,11 +840,81 @@ def compute_forces_and_moments( omega[0] * reduced_rate_factor, # q* reduced pitch rate omega[1] * reduced_rate_factor, # r* reduced yaw rate omega[2] * reduced_rate_factor, # p* reduced roll rate - alpha_dot, - beta_dot, ) # Dislocation of the aerodynamic application point to CDM M1, M2, M3 = Vector([pitch, yaw, roll]) + (cp ^ Vector([R1, R2, R3])) return R1, R2, R3, M1, M2, M3 + + def to_dict(self, include_outputs=False, **kwargs): # pylint: disable=unused-argument + # The stored coefficients are always the canonical body-frame set (the + # names from ``_get_default_coefficients``: cN/cY/cA/... for a generic + # surface, the derivative set for the linear model), so they are saved + # with ``force_convention="body"`` and rebuilt directly on load. + coefficients = { + name: getattr(self, name) for name in self._get_default_coefficients() + } + # A preset ``active_during`` is stored as is; a custom (t, flight) -> bool + # function is pickled to text when allowed, otherwise dropped to "always" + # (a function cannot be restored without pickling). + active_during = self.active_during + if callable(active_during): + active_during = ( + to_hex_encode(active_during) + if kwargs.get("allow_pickle", True) + else "always" + ) + return { + "reference_area": self.reference_area, + "reference_length": self.reference_length, + "reynolds_length": self.reynolds_length, + "coefficients": coefficients, + "center_of_pressure": self.center_of_pressure, + "name": self.name, + "force_convention": "body", + "active_during": active_during, + } + + @classmethod + def from_dict(cls, data): + # A preset ``active_during`` is used as is; anything else is unpickled + # back into the original function (falling back to "always" if it cannot + # be restored). + active_during = data.get("active_during", "always") + if active_during not in ("always", "power_on", "power_off"): + try: + active_during = from_hex_decode(active_during) + except (TypeError, ValueError): + active_during = "always" + return cls( + reference_area=data["reference_area"], + reference_length=data["reference_length"], + coefficients=data["coefficients"], + center_of_pressure=data.get("center_of_pressure", (0, 0, 0)), + name=data.get("name", "Generic Surface"), + reynolds_length=data.get("reynolds_length"), + force_convention=data.get("force_convention", "body"), + active_during=active_during, + ) + + def info(self): + """Prints a summary of the surface's geometry and aerodynamic + coefficients. Subclasses override this with surface-specific summaries. + + Returns + ------- + None + """ + self.prints.geometry() + self.prints.coefficients() + + def all_info(self): + """Prints and plots all available information of the surface. + + Returns + ------- + None + """ + self.prints.all() + self.plots.all() diff --git a/rocketpy/rocket/aero_surface/linear_generic_surface.py b/rocketpy/rocket/aero_surface/linear_generic_surface.py index 69aa5442e..3b313bfef 100644 --- a/rocketpy/rocket/aero_surface/linear_generic_surface.py +++ b/rocketpy/rocket/aero_surface/linear_generic_surface.py @@ -23,9 +23,11 @@ def __init__( coefficients, center_of_pressure=(0, 0, 0), name="Generic Linear Surface", + reynolds_length=None, interpolation=None, extrapolation=None, force_convention=None, + active_during="always", ): """Create a generic linear aerodynamic surface, defined by its aerodynamic coefficients derivatives. This surface is used to model any @@ -185,6 +187,10 @@ def __init__( name : str, optional Name of the aerodynamic surface. Default is 'Generic Linear Surface'. + reynolds_length : int, float, optional + Length scale, in meters, of the Reynolds number passed to the + coefficient derivatives. See :class:`GenericSurface`. ``None`` (the + default) uses ``reference_length`` (the diameter). interpolation : str or dict, optional How tabulated coefficient derivatives interpolate between points. The accepted methods depend on the coefficient's dimensionality: a @@ -210,7 +216,7 @@ def __init__( force_convention : str, optional The frame your force-coefficient derivatives are given in. ``"body"`` for the body-frame derivatives ``cN_*`` (normal), ``cY_*`` (side) and - ``cA_*`` (axial); ``"wind"`` for the aerodynamic-frame derivatives + ``cA_*`` (axial); ``"wind"`` for the wind-frame derivatives ``cL_*`` (lift), ``cQ_*`` (side) and ``cD_*`` (drag). The moment derivatives (``cm_*``, ``cn_*``, ``cl_*``) are the same in both. ``None`` (the default) infers the frame from the coefficient names you @@ -222,6 +228,12 @@ def __init__( ``cY_beta = cQ_beta - cD_0``, ``cA_alpha = cD_alpha - cL_0`` and ``cA_beta = cD_beta + cQ_0``. At zero angle this reduces to ``cN = cL``, ``cY = cQ``, ``cA = cD``. + active_during : str or callable, optional + When this surface produces aerodynamic force during a simulation: + ``"always"`` (default), ``"power_on"`` (only while the motor burns), + ``"power_off"`` (only after burnout), or a function + ``active_during(t, flight)`` returning ``True`` when the surface is + active at time ``t``. See :class:`GenericSurface` for details. """ super().__init__( @@ -230,9 +242,11 @@ def __init__( coefficients=coefficients, center_of_pressure=center_of_pressure, name=name, + reynolds_length=reynolds_length, extrapolation=extrapolation, interpolation=interpolation, force_convention=force_convention, + active_during=active_during, ) self.compute_all_coefficients() @@ -390,7 +404,6 @@ def _as_coefficient(self, source, name): """ return AeroCoefficient( source, - unsteady_aero=self._unsteady_aero, control_variables=self.control_variables, name=name, ) @@ -567,7 +580,6 @@ def total_coefficient( def compute_all_coefficients(self): """Compute all the aerodynamic coefficients from the derivatives.""" - # pylint: disable=invalid-name self.cNf = self.compute_forcing_coefficient( self.cN_0, self.cN_alpha, self.cN_beta ) @@ -616,15 +628,10 @@ def _compute_from_coefficients( pitch_rate, yaw_rate, roll_rate, - alpha_dot=0.0, # pylint: disable=unused-argument - beta_dot=0.0, # pylint: disable=unused-argument ): """Compute the aerodynamic forces and moments from the aerodynamic coefficients. - The linear (Barrowman) model does not use the unsteady ``alpha_dot`` / - ``beta_dot`` terms; they are accepted for signature compatibility. - Parameters ---------- rho : float @@ -645,12 +652,6 @@ def _compute_from_coefficients( Non-dimensional (reduced) yaw rate, ``r * L_ref / (2 * V)``. roll_rate : float Non-dimensional (reduced) roll rate, ``p * L_ref / (2 * V)``. - alpha_dot : float, optional - Non-dimensional angle-of-attack rate. Ignored by the linear model; - accepted for signature compatibility. Defaults to 0. - beta_dot : float, optional - Non-dimensional sideslip-angle rate. Ignored by the linear model; - accepted for signature compatibility. Defaults to 0. Returns ------- diff --git a/rocketpy/rocket/helpers.py b/rocketpy/rocket/helpers.py new file mode 100644 index 000000000..26593c832 --- /dev/null +++ b/rocketpy/rocket/helpers.py @@ -0,0 +1,280 @@ +"""Helper functions backing some :class:`rocketpy.Rocket` methods. + +These carry the heavier computations behind the rocket's full-body aerodynamic +reduction (``to_coefficients`` / ``to_surface``), kept out of ``rocket.py`` so +that module stays focused on the rocket's public interface. Each function takes +the rocket it operates on as its first argument. +""" + +import math + +import numpy as np + +from rocketpy.mathutils.function import Function +from rocketpy.mathutils.vector_matrix import Vector +from rocketpy.rocket.aero_surface.aero_coefficient import AeroCoefficient + + +def zero_drag(rocket, which): + """Set one of the rocket's built-in drag curves to zero. ``which`` is + ``"power_on"`` or ``"power_off"``. Rebuilds the ``*_drag_7d``, + ``*_drag_by_mach`` and the public ``*_drag`` alias to match, mirroring how + they are built in ``Rocket.__init__``. + """ + label = "Power On" if which == "power_on" else "Power Off" + setattr( + rocket, + f"{which}_drag_7d", + AeroCoefficient( + 0, + name=f"Drag Coefficient with {label}", + extrapolation="constant", + single_var="mach", + ), + ) + by_mach = Function( + lambda mach: 0.0, + inputs="Mach Number", + outputs=f"Drag Coefficient with {label}", + interpolation="linear", + extrapolation="constant", + ) + setattr(rocket, f"{which}_drag_by_mach", by_mach) + setattr(rocket, f"{which}_drag", by_mach) + setattr(rocket, f"_{which}_drag_input", 0) + + +def summed_force_and_moment(rocket, alpha, beta, mach, omega, speed=1.0): + """Total body-frame force ``(R1, R2, R3)`` and moment ``(M1, M2, M3)`` about + the center of dry mass, summed over every aerodynamic surface of ``rocket`` + at a flow state and set of body rates. + + Mirrors the per-surface computation the flight integrator performs: each + surface is fed its own local stream velocity, which includes the + ``omega x cp`` lever-arm term, so the sum captures the pitch and yaw damping + the distributed surfaces produce through their fore-and-aft positions. + Evaluated at unit air density; the result scales out of any dimensionless + coefficient, and the chosen ``speed`` cancels from every coefficient built + from it. ``omega`` is the body angular rate in rad/s. + """ + stream_direction = Vector([-math.tan(beta), -math.tan(alpha), -1.0]) + stream_at_cdm = stream_direction / abs(stream_direction) * speed + body_rates = Vector(list(omega)) + density = Function(1.0) + dynamic_viscosity = Function(1e30) # vanishing-Reynolds limit + speed_of_sound = speed / mach if mach > 0 else 1e30 + totals = np.zeros(6) + for surface, _ in rocket.aerodynamic_surfaces: + cp = rocket.surfaces_cp_to_cdm[surface] + comp_stream = stream_at_cdm - (body_rates ^ cp) + comp_speed = abs(comp_stream) + forces = surface.compute_forces_and_moments( + comp_stream, + comp_speed, + comp_speed / speed_of_sound, + 1.0, + cp, + body_rates, + density, + dynamic_viscosity, + 0.0, + ) + totals += np.array(forces) + return totals + + +def neutral_point_and_slope(rocket, alpha, beta, mach, plane="pitch", step=1e-4): + """Local (tangent) neutral point and force-curve slope at a finite incidence. + + Generalizes the aerodynamic center to a non-zero angle of attack. The neutral + point is the point about which the aerodynamic moment does not change for a + *small* perturbation of the incidence angle around the given ``(alpha, beta)`` + state, i.e. the tangent of the moment-versus-force curve at that state. It is + obtained by central-differencing the rocket's summed body-frame force and + moment (about the center of dry mass) with respect to the plane's incidence + angle, then forming ``x_cdm + csys * L_ref * (dCm/da) / (dCN/da)``. + + For a rocket whose surfaces are all linear in incidence (the built-in + Barrowman surfaces) the result is independent of ``alpha``/``beta`` and equals + :attr:`rocketpy.Rocket.aerodynamic_center`. It moves with incidence only when + a surface's normal-force coefficient is nonlinear in the incidence angle (for + example a Galejs ``sin**2(alpha)`` body-lift term added as a + :class:`rocketpy.GenericSurface`). + + Parameters + ---------- + rocket : rocketpy.Rocket + The rocket to evaluate. + alpha, beta : float + Angle of attack and sideslip angle, in radians, defining the state the + neutral point is taken about. + mach : float + Free-stream Mach number. + plane : str, optional + ``"pitch"`` (perturb ``alpha``, use the normal force and pitch moment) or + ``"yaw"`` (perturb ``beta``, use the side force and yaw moment). Default + ``"pitch"``. + step : float, optional + Half-step, in radians, of the central difference. Default ``1e-4``. + + Returns + ------- + tuple of float + ``(neutral_point, slope)``: the neutral-point axial position in the + user-defined rocket frame, and the force-curve slope ``dCN/da`` (pitch) + or ``dCY/db`` (yaw) at the state. When the slope vanishes (no lift at all) + the neutral point falls back to the zero-incidence aerodynamic center. + """ + rocket.evaluate_surfaces_cp_to_cdm() + reference_length = 2 * rocket.radius + dynamic_pressure_area = 0.5 * rocket.area # unit speed, unit density + dynamic_pressure_area_length = dynamic_pressure_area * reference_length + + def coefficients(a, b): + r1, r2, _, m1, m2, _ = summed_force_and_moment( + rocket, a, b, mach, (0.0, 0.0, 0.0) + ) + if plane == "yaw": + return r1 / dynamic_pressure_area, m2 / dynamic_pressure_area_length + return -r2 / dynamic_pressure_area, m1 / dynamic_pressure_area_length + + if plane == "yaw": + force_high, moment_high = coefficients(alpha, beta + step) + force_low, moment_low = coefficients(alpha, beta - step) + else: + force_high, moment_high = coefficients(alpha + step, beta) + force_low, moment_low = coefficients(alpha - step, beta) + + force_slope = (force_high - force_low) / (2 * step) + moment_slope = (moment_high - moment_low) / (2 * step) + if force_slope == 0: + center = ( + rocket.aerodynamic_center_yaw + if plane == "yaw" + else rocket.aerodynamic_center + ) + return center.get_value_opt(mach), 0.0 + neutral_point = rocket.center_of_dry_mass_position + ( + rocket._csys * reference_length * moment_slope / force_slope + ) + return neutral_point, force_slope + + +def full_body_coefficients(rocket, machs=None, force_convention="body"): + """Compute the rocket's lumped stability-derivative set, split by motor + phase. Backs :meth:`rocketpy.Rocket.to_coefficients`; see that method for the + full description of the returned coefficient sets and their limitations. + """ + if force_convention not in ("body", "wind"): + raise ValueError( + f"force_convention must be 'body' or 'wind', got {force_convention!r}." + ) + if machs is None: + machs = np.arange(0.0, 3.01, 0.02) + machs = np.asarray(machs, dtype=float) + # Make sure each surface's center-of-pressure offset is current. + rocket.evaluate_surfaces_cp_to_cdm() + + reference_length = 2 * rocket.radius + dynamic_pressure_area = 0.5 * rocket.area # unit speed, unit density + dynamic_pressure_area_length = dynamic_pressure_area * reference_length + + def coefficients_at(alpha, beta, red_pitch, red_yaw, red_roll, mach): + # reduced rate -> body rate at unit speed: omega = rate * 2 V / L_ref + rate_factor = 2.0 / reference_length + omega = ( + red_pitch * rate_factor, + red_yaw * rate_factor, + red_roll * rate_factor, + ) + r1, r2, _, m1, m2, m3 = summed_force_and_moment( + rocket, alpha, beta, mach, omega + ) + return { + "cN": -r2 / dynamic_pressure_area, + "cY": r1 / dynamic_pressure_area, + "cm": m1 / dynamic_pressure_area_length, + "cn": m2 / dynamic_pressure_area_length, + "cl": m3 / dynamic_pressure_area_length, + } + + step = 1e-5 + + def slope(field, coeff): + values = [] + for mach in machs: + state = { + "alpha": 0.0, + "beta": 0.0, + "red_pitch": 0.0, + "red_yaw": 0.0, + "red_roll": 0.0, + } + high = coefficients_at(mach=mach, **{**state, field: step}) + low = coefficients_at(mach=mach, **{**state, field: -step}) + values.append((high[coeff] - low[coeff]) / (2 * step)) + return np.array(values) + + # Motor-independent derivative values on the Mach grid, in the body frame. + # The rocket's shape does not change with the motor, so these are shared by + # both phases; only the drag below differs. + derivatives = { + "cN_alpha": slope("alpha", "cN"), + "cm_alpha": slope("alpha", "cm"), + "cN_q": slope("red_pitch", "cN"), + "cm_q": slope("red_pitch", "cm"), + "cY_beta": slope("beta", "cY"), + "cn_beta": slope("beta", "cn"), + "cY_r": slope("red_yaw", "cY"), + "cn_r": slope("red_yaw", "cn"), + "cl_p": slope("red_roll", "cl"), + } + # The drag is a Mach curve at zero incidence (the axial coefficient's + # constant term), and it is the one term that differs by motor phase. + drag_by_phase = { + "power_off": rocket.power_off_drag_by_mach, + "power_on": rocket.power_on_drag_by_mach, + } + + result = {} + for phase, drag_curve in drag_by_phase.items(): + values = { + **derivatives, + "cA_0": np.array([drag_curve.get_value_opt(m) for m in machs]), + } + if force_convention == "wind": + values = body_derivatives_to_wind(values) + result[phase] = { + coeff_name: Function( + np.column_stack([machs, curve]), + "Mach", + coeff_name, + interpolation="akima", + extrapolation="constant", + ) + for coeff_name, curve in values.items() + } + return result + + +def body_derivatives_to_wind(body): + """Express a body-frame derivative set (``cN_*``/``cY_*``/``cA_*``) in the + wind-frame names (``cL_*``/``cQ_*``/``cD_*``); the moment derivatives are + frame-shared. Two cross terms fold the axial force in at incidence, + ``cL_alpha = cN_alpha - cA_0`` and ``cQ_beta = cY_beta + cA_0`` -- the linear + inverse of the wind-to-body rotation :class:`LinearGenericSurface` applies to + a wind-frame input, so feeding the result back with + ``force_convention="wind"`` recovers the same body-frame surface. Operates on + the tabulated derivative values (arrays over the Mach grid). + """ + drag = body.get("cA_0", 0.0) + rename = {"cN": "cL", "cY": "cQ", "cA": "cD"} + wind = {} + for key, value in body.items(): + prefix, sep, suffix = key.partition("_") + wind[f"{rename.get(prefix, prefix)}{sep}{suffix}"] = value + if "cN_alpha" in body: + wind["cL_alpha"] = body["cN_alpha"] - drag + if "cY_beta" in body: + wind["cQ_beta"] = body["cY_beta"] + drag + return wind diff --git a/rocketpy/rocket/rocket.py b/rocketpy/rocket/rocket.py index 821eb884d..32f32a231 100644 --- a/rocketpy/rocket/rocket.py +++ b/rocketpy/rocket/rocket.py @@ -14,6 +14,7 @@ from rocketpy.rocket.aero_surface import ( AirBrakes, EllipticalFins, + Fin, Fins, NoseCone, RailButtons, @@ -26,7 +27,13 @@ from rocketpy.rocket.aero_surface.fins.free_form_fins import FreeFormFins from rocketpy.rocket.aero_surface.fins.trapezoidal_fin import TrapezoidalFin from rocketpy.rocket.aero_surface.generic_surface import GenericSurface +from rocketpy.rocket.aero_surface.linear_generic_surface import LinearGenericSurface from rocketpy.rocket.components import Components +from rocketpy.rocket.helpers import ( + full_body_coefficients, + neutral_point_and_slope, + zero_drag, +) from rocketpy.rocket.parachute import Parachute from rocketpy.tools import ( deprecated, @@ -35,22 +42,6 @@ ) -def _stability_slope(derivative_coefficient, sign=1.0): - """Turn a surface's coefficient derivative (``cN_alpha``, ``cY_beta``, ...) - into the force-curve slope as a Function of Mach, evaluated at zero - alpha/beta and zero rates. ``sign`` flips it when needed (the yaw plane uses - ``-cY_beta``). - """ - return Function( - lambda mach: ( - sign - * derivative_coefficient.get_value_opt(0.0, 0.0, mach, 0.0, 0.0, 0.0, 0.0) - ), - "Mach", - "Force coefficient slope", - ) - - # pylint: disable=too-many-instance-attributes, too-many-public-methods, too-many-instance-attributes class Rocket: """Keeps rocket information. @@ -156,10 +147,12 @@ class Rocket: alias for this attribute. See :doc:`Positions and Coordinate Systems ` for more information. Rocket.stability_margin : Function - Stability margin of the rocket, in calibers, as a function of mach - number and time. Stability margin is defined as the distance between - the center of pressure and the center of mass, divided by the - rocket's diameter. + Stability margin of the rocket, in calibers, as a function of angle of + attack (radians), mach number and time. Stability margin is defined as + the distance between the center of pressure and the center of mass, + divided by the rocket's diameter. The angle-of-attack argument matters + only when a surface is nonlinear in incidence (see + ``Rocket.is_incidence_linear``); otherwise it has no effect. Rocket.static_margin : Function Static margin of the rocket, in calibers, as a function of time. Static margin is defined as the distance between the center of pressure and the @@ -362,6 +355,9 @@ def __init__( # pylint: disable=too-many-statements self.sensors_by_name = {} self.aerodynamic_surfaces = Components() self.surfaces_cp_to_cdm = {} + # Set once a full-body model replaces the modeled aerodynamics + # (add_full_body_aerodynamics(overwrite=True)); warns on later surface adds. + self._aerodynamics_overwritten = False self.rail_buttons = Components() self._aerodynamic_center = Function( @@ -378,8 +374,8 @@ def __init__( # pylint: disable=too-many-statements lambda time: 0, inputs="Time (s)", outputs="Static Margin (c)" ) self._stability_margin = Function( - lambda mach, time: 0, - inputs=["Mach", "Time (s)"], + lambda alpha, mach, time: 0, + inputs=["Angle of Attack (rad)", "Mach", "Time (s)"], outputs="Stability Margin (c)", ) # Yaw-plane counterparts. The pitch-plane attributes above remain the @@ -399,8 +395,8 @@ def __init__( # pylint: disable=too-many-statements lambda time: 0, inputs="Time (s)", outputs="Static Margin - Yaw (c)" ) self._stability_margin_yaw = Function( - lambda mach, time: 0, - inputs=["Mach", "Time (s)"], + lambda beta, mach, time: 0, + inputs=["Sideslip Angle (rad)", "Mach", "Time (s)"], outputs="Stability Margin - Yaw (c)", ) @@ -460,6 +456,9 @@ def __init__( # pylint: disable=too-many-statements # The aerodynamic center and the margins are evaluated lazily self._cp_outdated = True self._margin_outdated = True + # Whether the neutral point moves with angle of attack; set when the + # margins are evaluated (see evaluate_stability_margin). + self._is_incidence_linear = True # Flag for rocket non-axisymmetric warning. Used to show warning once. self._axisymmetry_warned = False @@ -683,6 +682,61 @@ def aerodynamic_center_yaw(self): self._ensure_aerodynamic_center() return self._aerodynamic_center_yaw + def neutral_point(self, alpha, mach): + """Pitch-plane neutral point at a finite angle of attack, in meters. + + The neutral point is the point about which the aerodynamic pitching + moment does not change for a small change in angle of attack. It is the + angle-of-attack-aware generalization of the + :attr:`aerodynamic_center`: evaluated at ``alpha = 0`` the two are equal, + and for a rocket built only from the linear Barrowman surfaces the + neutral point does not move with angle of attack at all. + + It moves with angle of attack only when a surface's normal force is + nonlinear in the angle of attack, for example a Galejs body-lift term + (growing like ``sin**2(alpha)``) added as a + :class:`rocketpy.GenericSurface`. In that case the neutral point migrates + as the angle of attack changes, exactly the behavior OpenRocket models, + and the flight stability margin follows it. + + Parameters + ---------- + alpha : float + Angle of attack, in radians, to evaluate the neutral point at. + mach : float + Free-stream Mach number. + + Returns + ------- + float + Axial position of the pitch-plane neutral point in the user-defined + rocket coordinate system, in meters. + """ + return neutral_point_and_slope(self, alpha, 0.0, mach, "pitch")[0] + + def neutral_point_yaw(self, beta, mach): + """Yaw-plane neutral point at a finite sideslip angle, in meters. + + Yaw-plane counterpart of :meth:`neutral_point`: the point about which the + yaw moment does not change for a small change in sideslip angle, + evaluated at the given sideslip angle. Equal to + :attr:`aerodynamic_center_yaw` at ``beta = 0``. + + Parameters + ---------- + beta : float + Sideslip angle, in radians, to evaluate the neutral point at. + mach : float + Free-stream Mach number. + + Returns + ------- + float + Axial position of the yaw-plane neutral point in the user-defined + rocket coordinate system, in meters. + """ + return neutral_point_and_slope(self, 0.0, beta, mach, "yaw")[0] + @property def total_lift_coeff_der(self): """Total normal-force-coefficient derivative vs Mach (lazily evaluated).""" @@ -709,16 +763,79 @@ def static_margin_yaw(self): @property def stability_margin(self): - """Pitch-plane stability margin (calibers) vs Mach and time (lazy).""" + """Pitch-plane stability margin (calibers) as a function of angle of + attack (radians), Mach and time (lazily evaluated). The angle-of-attack + argument matters only for a rocket that is nonlinear in incidence (see + :attr:`is_incidence_linear`); otherwise it has no effect and the margin + reduces to the Mach-and-time value.""" self._ensure_margins() return self._stability_margin @property def stability_margin_yaw(self): - """Yaw-plane stability margin (calibers) vs Mach and time (lazy).""" + """Yaw-plane stability margin (calibers) as a function of sideslip angle + (radians), Mach and time (lazily evaluated). Equal to + :attr:`stability_margin` for an axisymmetric rocket.""" self._ensure_margins() return self._stability_margin_yaw + @property + def length(self): + """Overall aerodynamic length of the rocket, in meters. + + This is the axial distance from the fore-most point of the rocket (the + nose cone tip) to the aft-most point of the rocket. It is measured along + the rocket axis and does not depend on the chosen coordinate-system + orientation. + + The aft-most point is usually the trailing edge of the last fin set or + the base of the aft tail, but if the motor nozzle extends past the last + aerodynamic surface, the nozzle sets the aft end instead. The rocket + must have at least one aerodynamic surface with a defined axial extent + (a nose cone, tail or fin set); otherwise a ``ValueError`` is raised. + + This length is what the hobby-rocketry convention of expressing the + static/stability margin as a *percentage of body length* is measured + against, as opposed to the caliber (diameter) convention used by + ``static_margin`` and ``stability_margin``. + + Returns + ------- + float + Overall aerodynamic length of the rocket, in meters. + """ + fore_points = [] + aft_points = [] + for surface, position in self.aerodynamic_surfaces: + if isinstance(surface, (NoseCone, Tail)): + axial_extent = surface.length + elif isinstance(surface, (Fins, Fin)): + axial_extent = surface.root_chord + else: + # Generic/controllable surfaces have no defined axial extent; + # they contribute a single point at their reference position. + axial_extent = 0.0 + # The reference point and the point one axial extent toward the tail + # (the tail direction is -_csys along the z axis). Taking the global + # extremes makes the result independent of which end is the reference. + fore_points.append(position.z) + aft_points.append(position.z - self._csys * axial_extent) + + if not fore_points: + raise ValueError( + "The rocket must have at least one aerodynamic surface to have a " + "defined length." + ) + + all_points = fore_points + aft_points + # Include the nozzle if a real motor extends past the aerodynamic + # surfaces. nozzle_position is already in the rocket reference frame. + if getattr(self, "motor", None) is not None and not isinstance( + self.motor, EmptyMotor + ): + all_points.append(self.nozzle_position) + return max(all_points) - min(all_points) + def evaluate_center_of_pressure(self): """Evaluates the rocket's aerodynamic center (and cp_position) as a function of Mach number, relative to the user-defined rocket reference @@ -760,12 +877,12 @@ def evaluate_center_of_pressure(self): if len(self.aerodynamic_surfaces) > 0: for aero_surface, position in self.aerodynamic_surfaces: # Force-curve slopes as Functions of Mach, from the surface's - # coefficient derivatives evaluated at zero alpha/beta and zero + # coefficient derivatives sliced at zero alpha/beta and zero # rates. The yaw slope is the sign-flipped ``cY_beta`` so an # axisymmetric surface gives the same signed weight as the pitch # plane (their margins then coincide when symmetric). - lift_coeff_der = _stability_slope(aero_surface.cN_alpha) - side_coeff_der = _stability_slope(aero_surface.cY_beta, sign=-1.0) + lift_coeff_der = aero_surface.cN_alpha.slice("mach") + side_coeff_der = -1.0 * aero_surface.cY_beta.slice("mach") cp_z = aero_surface.center_of_pressure_z cp_z_yaw = aero_surface.center_of_pressure_z_yaw # ref_factor corrects force for different reference areas @@ -838,158 +955,24 @@ def is_axisymmetric(self): return self._cp_plane_max_difference() <= 1e-6 * (2 * self.radius) @property - def cp_position(self): - """Alias for :attr:`aerodynamic_center`. + def is_incidence_linear(self): + """``True`` when the rocket's aerodynamics are linear in the angle of + attack, so the neutral point (and therefore the stability margin) does + not move as the angle of attack changes. This holds for a rocket built + only from the linear Barrowman surfaces. It is ``False`` when a surface's + normal force is nonlinear in incidence, such as a Galejs body-lift term + added as a :class:`rocketpy.GenericSurface`, in which case + :attr:`stability_margin` varies with its angle-of-attack argument.""" + self._ensure_margins() + return self._is_incidence_linear - Historically named "center of pressure", this is the linearized, - Mach-dependent aerodynamic center -- the slope-weighted (Barrowman) - quantity the rocketry community conventionally calls the CP, and the - well-conditioned reference used by the static and stability margins. - """ - # TODO: review note: I guess having the full, real nonlinear cp would be cool and good for completeness, althouhg not that useful ... should try it + @property + def cp_position(self): + """Alias for :attr:`aerodynamic_center`. Traditional center of pressure + position, defined as the linearized (small-incidence) center of pressure, + is the same as the aerodynamic center.""" return self.aerodynamic_center - # TODO: review note: why are the bellow functions related to aerodynamic forces - # not using rates? What I want from this is to define a complete set of the - # entire vehicle aerodynamic coefficients. And make it as complete as possible - # than make some helper analysis functions to get something like the version - # without rates, etc. Could that be done? - def _aerodynamic_forces_and_moments(self, alpha, beta, mach, reynolds=0.0): - """Total body-frame aerodynamic force ``(R1, R2, R3)`` and moment - ``(M1, M2, M3)`` about the center of dry mass, summed over every - aerodynamic surface at a static state (zero rates), plus the - ``stream_speed`` used. - - Computed at unit air density, so the forces equal the dimensionless - coefficients times ``0.5 * stream_speed**2 * reference_area``; dynamic - pressure therefore cancels from any coefficient or center-of-pressure - ratio. The viscosity is chosen so each surface's Reynolds number (built - on its own reference length) is consistent with the requested - rocket-level Reynolds number (built on the diameter): - ``Re_surface = reynolds * reference_length / (2 * radius)``; a - non-positive Reynolds collapses to the vanishing-Reynolds limit. - """ - # Body-frame stream velocity reproducing (alpha, beta). - # ``compute_forces_and_moments`` negates it internally and recovers - # ``alpha = atan2(sv_y, sv_z)`` and ``beta = atan2(sv_x, sv_z)``. - stream_velocity = Vector([-math.tan(beta), -math.tan(alpha), -1.0]) - stream_speed = abs(stream_velocity) - omega = Vector([0, 0, 0]) - density = Function(1.0) - if reynolds > 0: - dynamic_viscosity = Function(stream_speed * 2 * self.radius / reynolds) - else: - dynamic_viscosity = Function(1e30) - - totals = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0] - for surface, _ in self.aerodynamic_surfaces: - cp = self.surfaces_cp_to_cdm[surface] - forces = surface.compute_forces_and_moments( - stream_velocity, - stream_speed, - mach, - 1.0, - cp, - omega, - density, - dynamic_viscosity, - 0.0, - ) - totals = [acc + value for acc, value in zip(totals, forces)] - return (*totals, stream_speed) - - def aerodynamic_coefficients(self, alpha, beta, mach, reynolds=0.0): - """Total rocket aerodynamic coefficients at a given state, referenced to - the rocket cross-section area and diameter and taken about the center of - dry mass. - - Parameters - ---------- - alpha, beta : float - Angle of attack and sideslip, in radians. - mach : float - Free-stream Mach number. - reynolds : float, optional - Rocket-level Reynolds number. Default 0. - - Returns - ------- - dict - ``{"normal_force": C_N, "pitch_moment": C_m}`` -- the total - normal-force and pitch-moment (about the center of dry mass) - coefficient magnitudes. - - Notes - ----- - The rocket's axial (drag) coefficient is **not** included: the geometric - (Barrowman) surfaces carry no drag coefficient, the rocket drag being - supplied separately by ``power_off_drag``/``power_on_drag``. See - :meth:`Rocket.plots.drag_curves`. - """ - r1, r2, _, m1, m2, _, stream_speed = self._aerodynamic_forces_and_moments( - alpha, beta, mach, reynolds - ) - dynamic_pressure_area = 0.5 * stream_speed**2 * self.area - if dynamic_pressure_area == 0: - return {"normal_force": 0.0, "pitch_moment": 0.0} - reference_length = 2 * self.radius - return { - "normal_force": (r1**2 + r2**2) ** 0.5 / dynamic_pressure_area, - "pitch_moment": (m1**2 + m2**2) ** 0.5 - / (dynamic_pressure_area * reference_length), - } - - def aerodynamic_coefficients_full(self, alpha, beta, mach, reynolds=0.0): - """All six signed rocket-level aerodynamic coefficients at a state. - - Aggregates every aerodynamic surface into the vehicle's force and moment - coefficients, referenced to the rocket cross-section area and diameter - and taken about the center of dry mass, in the body aerodynamic frame: - - - ``cN`` (normal force), ``cY`` (side force), ``cA`` (axial force); - - ``cm`` (pitch), ``cn`` (yaw), ``cl`` (roll). - - These are body-frame, signed and complete, unlike - :meth:`aerodynamic_coefficients` (which returns unsigned normal-force and - pitch-moment magnitudes). The axial coefficient ``cA`` is taken from the - vehicle drag curve (``power_off_drag``), since the geometric surfaces - carry no axial coefficient; this unifies the per-surface normal-force / - moment model with the separately supplied drag curve into a single - coefficient set. - - Parameters - ---------- - alpha, beta : float - Angle of attack and sideslip, in radians. - mach : float - Free-stream Mach number. - reynolds : float, optional - Rocket-level Reynolds number. Default 0. - - Returns - ------- - dict - ``{"cN", "cY", "cA", "cm", "cn", "cl"}``. - """ - r1, r2, r3, m1, m2, m3, stream_speed = self._aerodynamic_forces_and_moments( - alpha, beta, mach, reynolds - ) - dynamic_pressure_area = 0.5 * stream_speed**2 * self.area - if dynamic_pressure_area == 0: - return {c: 0.0 for c in ("cN", "cY", "cA", "cm", "cn", "cl")} - reference_length = 2 * self.radius - dynamic_pressure_area_length = dynamic_pressure_area * reference_length - # Body-frame force components: R1 = cY, R2 = -cN, R3 = -cA (see - # GenericSurface.compute_forces_and_moments). - return { - "cN": -r2 / dynamic_pressure_area, - "cY": r1 / dynamic_pressure_area, - "cA": self.power_off_drag_by_mach.get_value_opt(mach), - "cm": m1 / dynamic_pressure_area_length, - "cn": m2 / dynamic_pressure_area_length, - "cl": m3 / dynamic_pressure_area_length, - } - def evaluate_surfaces_cp_to_cdm(self): """Calculates the relative position of each aerodynamic surface center of pressure to the rocket's center of dry mass in Body Axes Coordinate @@ -1020,8 +1003,7 @@ def __evaluate_single_surface_cp_to_cdm(self, surface, position): # position of the force application point in body frame. Every surface # applies its resultant force at its center of pressure and transports # the moment geometrically; the surface-local application point is mapped - # into the body frame by ``_rotation_surface_to_body`` (identity for a - # generic surface, a 180-degree flip for Barrowman surfaces). + # into the body frame by ``_rotation_surface_to_body`` application_point = getattr( surface, "force_application_point", @@ -1032,42 +1014,122 @@ def __evaluate_single_surface_cp_to_cdm(self, surface, position): ) # TODO: this should be recomputed whenever cant angle changes for fin self.surfaces_cp_to_cdm[surface] = pos + def _evaluate_is_incidence_linear(self): + """Detect whether the rocket's aerodynamics are linear in the angle of + attack, i.e. whether the neutral point moves as the angle of attack + changes. Returns ``True`` for a rocket built only from the linear + Barrowman surfaces and ``False`` when a surface's normal force is + nonlinear in incidence (for example a Galejs body-lift term added as a + :class:`rocketpy.GenericSurface`). + + The check central-differences the neutral point at zero and at five + degrees of incidence, in both the pitch and yaw planes; if either plane + moves, the rocket is treated as incidence-nonlinear. + """ + probe_mach = 0.3 + probe_alpha = math.radians(5.0) + for plane in ("pitch", "yaw"): + if plane == "yaw": + point_zero = neutral_point_and_slope(self, 0.0, 0.0, probe_mach, "yaw") + point_five = neutral_point_and_slope( + self, 0.0, probe_alpha, probe_mach, "yaw" + ) + else: + point_zero = neutral_point_and_slope(self, 0.0, 0.0, probe_mach, "pitch") + point_five = neutral_point_and_slope( + self, probe_alpha, 0.0, probe_mach, "pitch" + ) + if abs(point_five[0] - point_zero[0]) > 1e-6: + return False + return True + + def _neutral_point_margin_slope(self, incidence, mach, time, plane="pitch"): + """Stability margin (in calibers) and local normal-force-curve slope at a + single flow state, the shared computation behind ``stability_margin`` and + the flight dynamic-stability oscillator. + + For a rocket that is linear in incidence the neutral point is the + zero-incidence :attr:`aerodynamic_center` and ``incidence`` is ignored, + keeping the fast analytic path (and byte-for-byte the previous margin + values). Otherwise the neutral point and slope are found at ``incidence`` + by :func:`rocketpy.rocket.helpers.neutral_point_and_slope`, so a surface + that is nonlinear in the angle of attack (e.g. a Galejs body-lift + :class:`rocketpy.GenericSurface`) makes the margin move with incidence. + + Parameters + ---------- + incidence : float + Angle of attack (pitch) or sideslip angle (yaw), in radians. + mach : float + Free-stream Mach number. + time : float + Flight time, in seconds, at which the center of mass is taken. + plane : str, optional + ``"pitch"`` or ``"yaw"``. Default ``"pitch"``. + + Returns + ------- + tuple of float + ``(margin, slope)``: the stability margin in calibers and the local + force-curve slope (``dCN/dalpha`` for pitch, ``dCY/dbeta`` for yaw). + """ + self._ensure_margins() + if plane == "yaw": + center = self.aerodynamic_center_yaw + slope_curve = self.total_side_coeff_der + else: + center = self.aerodynamic_center + slope_curve = self.total_lift_coeff_der + + if self._is_incidence_linear: + neutral_point = center.get_value_opt(mach) + slope = slope_curve.get_value_opt(mach) + elif plane == "yaw": + neutral_point, slope = neutral_point_and_slope( + self, 0.0, incidence, mach, "yaw" + ) + else: + neutral_point, slope = neutral_point_and_slope( + self, incidence, 0.0, mach, "pitch" + ) + + margin = ( + self._csys + * (self.center_of_mass.get_value_opt(time) - neutral_point) + / (2 * self.radius) + ) + return margin, slope + def evaluate_stability_margin(self): - """Calculates the stability margin of the rocket as a function of mach - number and time. + """Calculates the stability margin of the rocket as a function of angle + of attack, Mach number and time. Returns ------- stability_margin : Function - Stability margin of the rocket, in calibers, as a function of mach - number and time. Stability margin is defined as the distance between - the center of pressure and the center of mass, divided by the - rocket's diameter. + Stability margin of the rocket, in calibers, as a function of angle + of attack (radians), Mach number and time. The stability margin is + the distance between the center of pressure and the center of mass, + divided by the rocket's diameter. It depends on the angle of attack + only when a surface is nonlinear in incidence; for a rocket built + from the linear Barrowman surfaces the angle-of-attack argument has + no effect. """ + self._is_incidence_linear = self._evaluate_is_incidence_linear() self._stability_margin.set_source( - lambda mach, time: ( - ( - ( - self.center_of_mass.get_value_opt(time) - - self.aerodynamic_center.get_value_opt(mach) - ) - / (2 * self.radius) - ) - * self._csys - ) + lambda alpha, mach, time: self._neutral_point_margin_slope( + alpha, mach, time, "pitch" + )[0] ) + self._stability_margin.set_inputs(["Angle of Attack (rad)", "Mach", "Time (s)"]) # Yaw-plane stability margin (equal to the pitch plane when axisymmetric) self._stability_margin_yaw.set_source( - lambda mach, time: ( - ( - ( - self.center_of_mass.get_value_opt(time) - - self.aerodynamic_center_yaw.get_value_opt(mach) - ) - / (2 * self.radius) - ) - * self._csys - ) + lambda beta, mach, time: self._neutral_point_margin_slope( + beta, mach, time, "yaw" + )[0] + ) + self._stability_margin_yaw.set_inputs( + ["Sideslip Angle (rad)", "Mach", "Time (s)"] ) return self._stability_margin @@ -1514,6 +1576,14 @@ def add_surfaces(self, surfaces, positions): ------- None """ + if self._aerodynamics_overwritten: + warnings.warn( + "This rocket's aerodynamics were overwritten by a full-body " + "model (add_full_body_aerodynamics(overwrite=True)); the surface(s) " + "you are adding now will be summed on top of that model.", + UserWarning, + stacklevel=2, + ) if isinstance(surfaces, Iterable): if isinstance(positions, Iterable): if len(surfaces) != len(positions): @@ -1536,68 +1606,242 @@ def add_surfaces(self, surfaces, positions): # legitimately warn again about the new configuration. self._axisymmetry_warned = False - # TODO: review note: several issues with this. First, the name is bad - # second, there should be an overwrite option, so it overwrites any existing - # surfaces on the rocket (even if a surface is added AFTER this method is called). - # TODO: review note: how can power on/power off be considered here? - def add_vehicle_aerodynamic_surface( - self, coefficients, reference_position=None, name="Vehicle Aerodynamics" - ): - """Define the whole vehicle from a supplied set of aerodynamic - coefficients (a "rocket-as-:class:`GenericSurface`" model). + def add_full_body_aerodynamics(self, surfaces, position=None, overwrite=False): + """Add a prebuilt full-body aerodynamic surface: the whole rocket + modeled as a single surface. Instead of (or in addition to) modeling + each component, this lets you provide a set of coefficients for the + whole rocket, which is often easier. + + Parameters + ---------- + surfaces : GenericSurface or list of GenericSurface + The prebuilt full-body surface, or a list of them (for example a + power-on/power-off pair, each carrying its own ``active_during``). + Any of: + + - a :class:`GenericSurface`; + - a :class:`LinearGenericSurface`; + - a :class:`ControllableGenericSurface` for coefficients that + also depend on control-deflection axes. + + Reference the surface's coefficients to the rocket cross-section + area and diameter (build it with ``reference_area=rocket.area`` and + ``reference_length=2 * rocket.radius``) so it sums consistently with + the rest of the rocket. Because it is just another aerodynamic + surface, a full-body model can be **mixed** with modeled add-on + surfaces (e.g. use ``add_full_body_aerodynamics`` together with + ``add_tail``): they simply add. + + A rocket's aerodynamics usually differ between powered and coasting + flight. To capture this, build two surfaces, set each one's + ``active_during`` to ``"power_on"`` and ``"power_off"``, and pass + them together as a list; each then produces force only during its + phase. + position : int, float, optional + Position along the rocket's center axis (in the user coordinate + system) where the surface's resultant force is applied and about + which its moment coefficients are taken. Defaults to the center of + dry mass position. + overwrite : bool, optional + If ``True``, make this the rocket's only aerodynamics: every + aerodynamic surface already on the rocket is removed first, and both + built-in drag curves (``power_on_drag`` and ``power_off_drag``) are + cleared. Default ``False`` (the model is added on top of the + existing aerodynamics). + + Returns + ------- + GenericSurface or list of GenericSurface + The surface(s) added. + """ + if position is None: + position = self.center_of_dry_mass_position + + if overwrite: + self._clear_aerodynamic_surfaces() + + surface_list = ( + list(surfaces) if isinstance(surfaces, (list, tuple)) else [surfaces] + ) + for surface in surface_list: + self.add_surfaces(surface, position) + + if overwrite: + # Re-arm the "added after" guard now that the full-body model is set. + self._aerodynamics_overwritten = True + + return surfaces + + def _clear_aerodynamic_surfaces(self): + """Wipe the rocket's aerodynamics so a full-body model can fully replace + them: remove every aerodynamic surface, clear both built-in drag curves, + and reset the derived stability caches. Used by + :meth:`add_full_body_aerodynamics` with ``overwrite=True``. + """ + self.aerodynamic_surfaces.clear() + self.surfaces_cp_to_cdm.clear() + # Clear both built-in drag curves; the supplied surface(s) now provide + # the complete aerodynamics, including any drag they carry. + zero_drag(self, "power_on") + zero_drag(self, "power_off") + warnings.warn( + "add_full_body_aerodynamics(overwrite=True): the rocket's existing " + "aerodynamic surfaces and both built-in drag curves (power_on_drag, " + "power_off_drag) were cleared; the supplied surface(s) now provide " + "the complete aerodynamics, including any drag they carry.", + UserWarning, + stacklevel=3, + ) + self._cp_outdated = True + self._margin_outdated = True + self._axisymmetry_warned = False + # New adds are welcome again; the guard is re-armed once the full-body + # surfaces are in place (see add_full_body_aerodynamics). + self._aerodynamics_overwritten = False + + def to_coefficients(self, machs=None, force_convention="body"): + """Return the whole rocket's aerodynamic coefficients, split by motor + phase. + + Sweeps the rocket's aerodynamic surfaces and lumps them into the + rocket's complete stability-derivative set about the dry center of mass: + the normal-force and pitch-moment slopes ``cN_alpha``/``cm_alpha`` + (pitch), the side-force and yaw-moment slopes ``cY_beta``/``cn_beta`` + (yaw), the pitch and yaw rate damping ``cN_q``/``cm_q`` and + ``cY_r``/``cn_r``, the fin roll damping ``cl_p`` and the drag ``cA_0``. + + The result is returned as two coefficient sets, ``"power_off"`` + (coasting) and ``"power_on"`` (motor burning). + + Important + --------- + The resulting coefficients are a **linear summary tabulated only against + Mach**: the derivatives are taken at zero angle of attack, zero sideslip + and zero rates, so only their Mach dependence is kept. This leaves out: + + - **Incidence and rate nonlinearity.** Only the slope at zero is + retained, so any curvature in angle of attack, sideslip or the body + rates is not represented. + - **Reynolds dependence.** The derivatives are measured in the + vanishing-Reynolds limit, so a coefficient that varies with Reynolds + number is frozen at that value rather than following the flight + Reynolds number. + - **Control-surface dependence.** Deflection axes of a + :class:`ControllableGenericSurface` are not carried into the summary. + - **Induced drag.** The axial coefficient is constant in incidence, so + drag does not increase with angle of attack. + + These are exactly the assumptions of RocketPy's built-in Barrowman + surfaces (:class:`NoseCone`, :class:`Tail` and the fin sets), which are + already linear, Mach-tabulated and Reynolds-independent. A rocket built + only from them is therefore reproduced exactly, with nothing lost. The + limitations matter only when you have added a :class:`GenericSurface` or + :class:`ControllableGenericSurface` (or a Reynolds-dependent + :class:`LinearGenericSurface`) whose coefficients truly vary with + incidence beyond a straight line, with Reynolds number, or with a + control deflection. - Instead of (or in addition to) modeling each surface, this lets a user - fly the 6-DOF directly from a full-vehicle coefficient set, e.g. exported - from CFD, a wind tunnel, or OpenRocket. The coefficients are wrapped in a - single :class:`GenericSurface` referenced to the rocket cross-section - area and diameter and added through the standard aerodynamic-surface - path, so the equations of motion sum it like any other surface. + Parameters + ---------- + machs : sequence of float, optional + Mach numbers at which the derivatives are sampled and tabulated. + Defaults to ``0`` to ``3`` in steps of ``0.02``. + force_convention : str, optional + The frame the force coefficients are named in. ``"body"`` (default) + gives the body-frame set : normal ``cN_*``, side ``cY_*`` and axial + ``cA_0`` (drag). ``"wind"`` gives the wind-frame set: lift + ``cL_*``, side ``cQ_*`` and drag ``cD_0``. The moment derivatives + (``cm_*``, ``cn_*``, ``cl_p``) are the same in both. - Because it is just another aerodynamic surface, a vehicle coefficient set - can be **mixed** with modeled add-on surfaces (e.g. a measured body plus - modeled canards): they simply add. + Returns + ------- + dict + A dict with keys ``"power_off"`` and ``"power_on"``. Each value is + itself a dict mapping a coefficient name to its curve over Mach (a + :class:`rocketpy.Function`). The body-frame set is ``cN_alpha``, + ``cm_alpha``, ``cN_q``, ``cm_q``, ``cY_beta``, ``cn_beta``, + ``cY_r``, ``cn_r``, ``cl_p`` and ``cA_0``. + """ + return full_body_coefficients(self, machs, force_convention) + + def to_surface( + self, + machs=None, + force_convention="body", + name="Full Body Aerodynamics", + ): + """Collapse the whole assembled rocket aerodynamics into two + :class:`rocketpy.LinearGenericSurface` objects, one for coasting and one + for powered flight. It reproduces the source rocket's aerodynamics, so a + bare rocket carrying the same body and motor plus this pair flies the + same as the fully modeled rocket. + + Important + --------- + The resulting coefficients are a **linear summary tabulated only against + Mach**: the derivatives are taken at zero angle of attack, zero sideslip + and zero rates, so only their Mach dependence is kept. This leaves out: + + - **Incidence and rate nonlinearity.** Only the slope at zero is + retained, so any curvature in angle of attack, sideslip or the body + rates is not represented. + - **Reynolds dependence.** The derivatives are measured in the + vanishing-Reynolds limit, so a coefficient that varies with Reynolds + number is frozen at that value rather than following the flight + Reynolds number. + - **Control-surface dependence.** Deflection axes of a + :class:`ControllableGenericSurface` are not carried into the summary. + - **Induced drag.** The axial coefficient is constant in incidence, so + drag does not increase with angle of attack. + + These are exactly the assumptions of RocketPy's built-in Barrowman + surfaces (:class:`NoseCone`, :class:`Tail` and the fin sets), which are + already linear, Mach-tabulated and Reynolds-independent. A rocket built + only from them is therefore reproduced exactly, with nothing lost. The + limitations matter only when you have added a :class:`GenericSurface` or + :class:`ControllableGenericSurface` (or a Reynolds-dependent + :class:`LinearGenericSurface`) whose coefficients truly vary with + incidence beyond a straight line, with Reynolds number, or with a + control deflection. Parameters ---------- - coefficients : dict - Aerodynamic coefficients ``cL, cQ, cD, cm, cn, cl`` (omitted ones - default to 0), each a number, callable, :class:`Function` or CSV - path of ``(alpha, beta, mach, reynolds, pitch_rate, yaw_rate, - roll_rate)`` -- the same input forms accepted by - :class:`GenericSurface`. - reference_position : int, float, optional - Axial station (in the user coordinate system) about which the - supplied moment coefficients are defined and where the resultant - force is applied. Defaults to the center of dry mass position. The - supplied moment coefficients are taken about this fixed station. + machs : sequence of float, optional + Mach numbers at which the derivatives are sampled and tabulated. + Defaults to ``0`` to ``3`` in steps of ``0.02``. + force_convention : str, optional + The frame the force coefficients are expressed in. ``"body"`` + (default) gives the body-frame set: normal ``cN``, side ``cY`` and + axial ``cA`` (drag). ``"wind"`` gives the wind-frame set: lift + ``cL``, side ``cQ`` and drag ``cD``. The moment coefficients are the + same in both. name : str, optional - Name of the surface. Default ``"Vehicle Aerodynamics"``. + Base name of the returned surfaces. + Default ``"Full Body Aerodynamics"``. Returns ------- - GenericSurface - The created vehicle aerodynamic surface (also added to the rocket). - - Notes - ----- - A single vehicle coefficient set necessarily drops per-surface locals - (the ``omega x r`` velocity at each surface, per-surface Reynolds, rail - buttons and individual-fin roll). For controllable vehicle coefficients - (deflection axes), build a - :class:`ControllableGenericSurface` and add it with - :meth:`add_surfaces` / ``add_controllable_surface`` instead. + list of rocketpy.LinearGenericSurface + Two surfaces, ``[power_off, power_on]``, each carrying the whole + rocket's coefficient derivatives referenced to the rocket + cross-section area and diameter and taken about the center of dry + mass, and gated to its motor phase. """ - if reference_position is None: - reference_position = self.center_of_dry_mass_position - - surface = GenericSurface( - reference_area=self.area, - reference_length=2 * self.radius, - coefficients=coefficients, - name=name, + coefficients = self.to_coefficients( + machs=machs, + force_convention=force_convention, ) - self.add_surfaces(surface, reference_position) - return surface + return [ + LinearGenericSurface( + reference_area=self.area, + reference_length=2 * self.radius, + coefficients=coefficients[phase], + force_convention=force_convention, + name=f"{name} ({phase.replace('_', ' ')})", + active_during=phase, + ) + for phase in ("power_off", "power_on") + ] def _add_controllers(self, controllers): """Adds a controller to the rocket. @@ -1726,7 +1970,7 @@ def add_nose( @deprecated( reason="This method is set to be deprecated in version 1.0.0 and fully " - "removed by version 2.0.0", + "removed by version 1.14.0", alternative="Rocket.add_trapezoidal_fins", ) def add_fins(self, *args, **kwargs): # pragma: no cover @@ -2678,7 +2922,13 @@ def to_dict(self, **kwargs): if kwargs.get("include_outputs", False): thrust_to_weight = self.thrust_to_weight aerodynamic_center = self.aerodynamic_center - stability_margin = self.stability_margin + # The zero-incidence design surface (Mach, time), a 2-D slice of the + # angle-of-attack-aware stability_margin, for output inspection. + stability_margin = Function( + lambda mach, time: self.stability_margin.get_value_opt(0.0, mach, time), + inputs=["Mach", "Time (s)"], + outputs="Stability Margin (c)", + ) center_of_mass = self.center_of_mass motor_center_of_mass_position = self.motor_center_of_mass_position reduced_mass = self.reduced_mass diff --git a/rocketpy/simulation/flight.py b/rocketpy/simulation/flight.py index b5b8e7723..2ae53bc41 100644 --- a/rocketpy/simulation/flight.py +++ b/rocketpy/simulation/flight.py @@ -2304,48 +2304,77 @@ def attitude_frequency_response(self): @cached_property def static_margin(self): - """Static margin of the rocket.""" + """Static margin of the rocket (linear, zero-airspeed reference).""" return self.rocket.static_margin + def _incidence(self, time): + """Total angle of attack, in radians (unsigned), used as the incidence + for the stability quantities. This is exact for a crosswind in the pitch + plane and a close approximation for combined pitch-and-yaw incidence.""" + return abs(np.radians(self.angle_of_attack.get_value_opt(time))) + @funcify_method("Time (s)", "Stability Margin (c)", "linear", "zero") def stability_margin(self): - """Linear stability margin along the flight, in calibers. + """Pitch-plane stability margin along the flight, in calibers. + + This samples the rocket's own :attr:`Rocket.stability_margin` along the + flight's realized angle of attack, Mach number and time. It therefore + accounts for the center-of-mass shift as propellant burns, the variation + of the aerodynamic center with Mach number, and, when a surface is + nonlinear in incidence (e.g. a Galejs body-lift + :class:`rocketpy.GenericSurface`), the migration of the neutral point + with the flight angle of attack. In that last case the margin oscillates + as the angle of attack oscillates; for a rocket built only from the + linear Barrowman surfaces the angle of attack has no effect and this + reduces to the Mach-and-time margin. - This is the classical margin: it evaluates the rocket's linearized - stability margin (:meth:`Rocket.stability_margin`) at the realized - flight Mach and time at each instant, capturing the Mach variation of - the aerodynamic center together with the center-of-mass shift as - propellant burns. + For non-axisymmetric rockets, this represents the pitch plane. Returns ------- stability : rocketpy.Function Stability margin in calibers as a function of time. """ - return [(t, self.rocket.stability_margin(m, t)) for t, m in self.mach_number] + rocket_margin = self.rocket.stability_margin + margin = np.array( + [ + rocket_margin.get_value_opt( + self._incidence(t), self.mach_number.get_value_opt(t), t + ) + for t in self.time + ] + ) + return np.column_stack((self.time, margin)) @funcify_method("Time (s)", "Stability Margin - Yaw (c)", "linear", "zero") def stability_margin_yaw(self): - """Linear yaw-plane stability margin along the flight, in calibers. + """Yaw-plane stability margin along the flight, in calibers. - Yaw-plane counterpart of :meth:`stability_margin`, using the rocket's - yaw-plane aerodynamic center (:meth:`Rocket.stability_margin_yaw`). - Equals :meth:`stability_margin` for an axisymmetric rocket; for a - non-axisymmetric rocket (e.g. single-plane canards) it differs, since - the pitch and yaw aerodynamic centers no longer coincide. + Yaw-plane counterpart of :meth:`stability_margin`, sampling the rocket's + :attr:`Rocket.stability_margin_yaw` along the flight. Equals + :meth:`stability_margin` for an axisymmetric rocket. For a + non-axisymmetric rocket (e.g. single-plane canards) it differs, since the + pitch and yaw aerodynamic centers no longer coincide. Returns ------- stability : rocketpy.Function Yaw-plane stability margin in calibers as a function of time. """ - return [ - (t, self.rocket.stability_margin_yaw(m, t)) for t, m in self.mach_number - ] + rocket_margin = self.rocket.stability_margin_yaw + margin = np.array( + [ + rocket_margin.get_value_opt( + self._incidence(t), self.mach_number.get_value_opt(t), t + ) + for t in self.time + ] + ) + return np.column_stack((self.time, margin)) - # Dynamic stability - # TODO: review note: the two methods below this comment needs to have its - # equations documented in a .rst file + # Dynamic stability. The equations behind the two methods below (the lateral + # inertia and the linearized oscillator) are documented in + # docs/technical/aerodynamics/center_of_pressure_and_stability.rst. def _lateral_inertia(self, dry_lateral_inertia, motor_lateral_inertia): """Lateral moment of inertia about the instantaneous center of mass, as an array over ``self.time``. Uses the reduced-mass formulation of the @@ -2370,23 +2399,27 @@ def _lateral_inertia(self, dry_lateral_inertia, motor_lateral_inertia): ) return inertia - def _dynamic_stability(self, lift_slope, stability_margin, lateral_inertia): + def _dynamic_stability(self, plane, lateral_inertia): """Linearized oscillator coefficients for one plane, as arrays over ``self.time``: corrective moment coefficient ``C1`` (restoring moment per radian), damping moment coefficient ``C2`` (aerodynamic + jet), undamped natural frequency ``omega_n`` and damping ratio ``zeta``. - ``lift_slope`` is the rocket's total normal-force-curve slope for the - plane (``total_lift_coeff_der`` for pitch, ``total_side_coeff_der`` for - yaw); ``stability_margin`` is the matching linear margin - ``Function(mach, time)``; ``lateral_inertia`` is the array from - :meth:`_lateral_inertia`. + ``plane`` is ``"pitch"`` or ``"yaw"``; ``lateral_inertia`` is the array + from :meth:`_lateral_inertia`. The restoring moment ``C1`` is built from + the angle-of-attack-aware margin and force-curve slope taken from the + rocket (:meth:`Rocket._neutral_point_margin_slope`) at the flight angle + of attack, so for a rocket that is nonlinear in incidence the natural + frequency and damping ratio move with the angle of attack. The + aerodynamic damping ``C2`` uses each surface's zero-incidence slope (its + second-order incidence dependence is neglected). """ - area = self.rocket.area - diameter = 2 * self.rocket.radius - csys = self.rocket._csys - nozzle_position = self.rocket.nozzle_position - mass_flow_rate = self.rocket.motor.total_mass_flow_rate + rocket = self.rocket + area = rocket.area + diameter = 2 * rocket.radius + csys = rocket._csys + nozzle_position = rocket.nozzle_position + mass_flow_rate = rocket.motor.total_mass_flow_rate corrective = np.empty(len(self.time)) damping = np.empty(len(self.time)) @@ -2397,16 +2430,15 @@ def _dynamic_stability(self, lift_slope, stability_margin, lateral_inertia): density = self.density.get_value_opt(t) center_of_mass = self.rocket.center_of_mass.get_value_opt(t) - # Corrective moment per radian: q A C_Nalpha (x_cm - x_ac). - margin = stability_margin.get_value_opt(mach, t) # calibers - corrective[i] = ( - dynamic_pressure - * area - * lift_slope.get_value_opt(mach) - * margin - * diameter + # Angle-of-attack-aware margin and local restoring-force slope, from + # the rocket, at this instant's incidence, Mach and time. + margin, force_slope = rocket._neutral_point_margin_slope( + self._incidence(t), mach, t, plane ) + # Corrective moment per radian: q A C_Nalpha (x_cm - x_np). + corrective[i] = dynamic_pressure * area * force_slope * margin * diameter + # Aerodynamic damping: 0.5 rho V A sum_i (A_i/A) C_Nalpha_i arm_i^2. damping_aero = 0.0 for surface, position in self.rocket.aerodynamic_surfaces: @@ -2429,6 +2461,15 @@ def _dynamic_stability(self, lift_slope, stability_margin, lateral_inertia): damping[i] = damping_aero + damping_jet positive_corrective = np.clip(corrective, 0.0, None) + # The damping ratio C2 / (2 sqrt(C1 I)) is ill-conditioned when the + # corrective moment C1 is ~0, i.e. at very low airspeed (on the launch + # rail): the propulsive (jet) part of C2 is already at full strength + # while C1 -> 0, so the ratio blows up even though there is no real + # oscillation yet. Only report it where C1 is a meaningful fraction of + # its flight maximum; elsewhere it is left at 0. The natural frequency + # sqrt(C1 / I) has C1 in the numerator, so it stays well-behaved. + max_corrective = positive_corrective.max(initial=0.0) + meaningful = positive_corrective > 1e-4 * max_corrective with np.errstate(divide="ignore", invalid="ignore"): natural_frequency = np.sqrt(positive_corrective / lateral_inertia) denominator = 2.0 * np.sqrt(positive_corrective * lateral_inertia) @@ -2436,7 +2477,7 @@ def _dynamic_stability(self, lift_slope, stability_margin, lateral_inertia): damping, denominator, out=np.zeros_like(damping), - where=denominator > 0, + where=meaningful, ) return corrective, damping, natural_frequency, damping_ratio @@ -2446,9 +2487,7 @@ def corrective_moment_coefficient(self): function of time -- the aerodynamic restoring moment per radian of angle of attack. Positive for a statically stable rocket.""" inertia = self._lateral_inertia(self.rocket.dry_I_11, self.rocket.motor.I_11) - corrective, _, _, _ = self._dynamic_stability( - self.rocket.total_lift_coeff_der, self.rocket.stability_margin, inertia - ) + corrective, _, _, _ = self._dynamic_stability("pitch", inertia) return np.column_stack((self.time, corrective)) @funcify_method("Time (s)", "Damping Moment Coefficient (N m s/rad)", "linear") @@ -2457,9 +2496,7 @@ def damping_moment_coefficient(self): the moment opposing the pitch rate, summing aerodynamic damping (from every surface) and propulsive (jet) damping.""" inertia = self._lateral_inertia(self.rocket.dry_I_11, self.rocket.motor.I_11) - _, damping, _, _ = self._dynamic_stability( - self.rocket.total_lift_coeff_der, self.rocket.stability_margin, inertia - ) + _, damping, _, _ = self._dynamic_stability("pitch", inertia) return np.column_stack((self.time, damping)) @funcify_method("Time (s)", "Pitch Natural Frequency (rad/s)", "linear") @@ -2467,9 +2504,7 @@ def pitch_natural_frequency(self): """Undamped natural frequency of the pitch oscillation, ``omega_n = sqrt(C1 / I_L)``, as a function of time (rad/s).""" inertia = self._lateral_inertia(self.rocket.dry_I_11, self.rocket.motor.I_11) - _, _, natural_frequency, _ = self._dynamic_stability( - self.rocket.total_lift_coeff_der, self.rocket.stability_margin, inertia - ) + _, _, natural_frequency, _ = self._dynamic_stability("pitch", inertia) return np.column_stack((self.time, natural_frequency)) @funcify_method("Time (s)", "Pitch Damping Ratio", "linear") @@ -2478,9 +2513,7 @@ def pitch_damping_ratio(self): ``zeta = C2 / (2 sqrt(C1 I_L))``, as a function of time. ``zeta < 1`` is underdamped (oscillatory), ``zeta > 1`` overdamped.""" inertia = self._lateral_inertia(self.rocket.dry_I_11, self.rocket.motor.I_11) - _, _, _, damping_ratio = self._dynamic_stability( - self.rocket.total_lift_coeff_der, self.rocket.stability_margin, inertia - ) + _, _, _, damping_ratio = self._dynamic_stability("pitch", inertia) return np.column_stack((self.time, damping_ratio)) @funcify_method("Time (s)", "Yaw Natural Frequency (rad/s)", "linear") @@ -2489,11 +2522,7 @@ def yaw_natural_frequency(self): time (rad/s). Equals :meth:`pitch_natural_frequency` for an axisymmetric rocket.""" inertia = self._lateral_inertia(self.rocket.dry_I_22, self.rocket.motor.I_22) - _, _, natural_frequency, _ = self._dynamic_stability( - self.rocket.total_side_coeff_der, - self.rocket.stability_margin_yaw, - inertia, - ) + _, _, natural_frequency, _ = self._dynamic_stability("yaw", inertia) return np.column_stack((self.time, natural_frequency)) @funcify_method("Time (s)", "Yaw Damping Ratio", "linear") @@ -2501,11 +2530,7 @@ def yaw_damping_ratio(self): """Damping ratio of the yaw oscillation as a function of time. Equals :meth:`pitch_damping_ratio` for an axisymmetric rocket.""" inertia = self._lateral_inertia(self.rocket.dry_I_22, self.rocket.motor.I_22) - _, _, _, damping_ratio = self._dynamic_stability( - self.rocket.total_side_coeff_der, - self.rocket.stability_margin_yaw, - inertia, - ) + _, _, _, damping_ratio = self._dynamic_stability("yaw", inertia) return np.column_stack((self.time, damping_ratio)) # Rail Button Forces diff --git a/rocketpy/simulation/helpers/flight_derivatives.py b/rocketpy/simulation/helpers/flight_derivatives.py index dcd275076..42a4165d4 100644 --- a/rocketpy/simulation/helpers/flight_derivatives.py +++ b/rocketpy/simulation/helpers/flight_derivatives.py @@ -377,6 +377,8 @@ def u_dot(flight, t, u, post_processing=False): velocity_in_body_frame = Vector([vx_b, vy_b, vz_b]) w = Vector([omega1, omega2, omega3]) for aero_surface, _ in flight.rocket.aerodynamic_surfaces: + if not aero_surface.is_active(t, flight): + continue # Component cp relative to CDM in body frame comp_cp = flight.rocket.surfaces_cp_to_cdm[aero_surface] # Component absolute velocity in body frame @@ -614,6 +616,8 @@ def u_dot_generalized_3dof(flight, t, u, post_processing=False): vb_body = Kt @ v for surface, _ in flight.rocket.aerodynamic_surfaces: + if not surface.is_active(t, flight): + continue cp = flight.rocket.surfaces_cp_to_cdm[surface] vb_component = vb_body + (w ^ cp) @@ -858,6 +862,8 @@ def u_dot_generalized(flight, t, u, post_processing=False): velocity_in_body_frame = Kt @ v # Calculate lift and moment for each component of the rocket for aero_surface, _ in flight.rocket.aerodynamic_surfaces: + if not aero_surface.is_active(t, flight): + continue # Component cp relative to CDM in body frame comp_cp = flight.rocket.surfaces_cp_to_cdm[aero_surface] # Component absolute velocity in body frame diff --git a/tests/fixtures/rockets/rocket_fixtures.py b/tests/fixtures/rockets/rocket_fixtures.py index 6ceabf589..ac2e6c418 100644 --- a/tests/fixtures/rockets/rocket_fixtures.py +++ b/tests/fixtures/rockets/rocket_fixtures.py @@ -1,10 +1,14 @@ +import math + import numpy as np import pytest -from rocketpy import LinearGenericSurface, Rocket +from rocketpy import Function, GenericSurface, LinearGenericSurface, Rocket +from rocketpy.mathutils.vector_matrix import Vector # TODO: review note: gotta test execution speed of changes in this branch + def _linear_surface_from_barrowman(surface): """Build a LinearGenericSurface that reproduces a Barrowman surface's aero. @@ -56,6 +60,201 @@ def _linear_surface_from_barrowman(surface): return linear_surface +def _generic_surface_from_barrowman(surface): + """Build a :class:`GenericSurface` that reproduces a Barrowman surface's aero. + + Same idea as :func:`_linear_surface_from_barrowman`, but expressed as a plain + :class:`GenericSurface`: instead of coefficient *slopes*, the total body-frame + coefficients are given directly as the linear expansion of the Barrowman + curves. The normal force is ``cN = clalpha(mach) * alpha``, the side force is + ``cY = -clalpha(mach) * beta`` and (for fins) the roll moment is + ``cl = cl_0(mach) + cl_p(mach) * roll_rate``. Because these are the same + functions the linear surface builds internally, the two surfaces produce + identical forces and moments; this fixture exercises the plain generic-surface + code path. + + The surface applies its force at its own origin (center of pressure + ``(0, 0, 0)``); the source surface's ``cpz`` is exposed as ``barrowman_cpz`` + so the caller can add it at the Barrowman center-of-pressure station, exactly + as for the linear surface. + + Parameters + ---------- + surface : rocketpy.NoseCone, rocketpy.Tail or rocketpy fin set + A standard Barrowman aerodynamic surface to copy the aero curves from. + + Returns + ------- + rocketpy.GenericSurface + A generic surface with the same normal force and (for fins) roll + behaviour as ``surface``, carrying the source surface's ``cpz`` as + ``barrowman_cpz``. + """ + clalpha = surface.clalpha # normal-force-curve slope, a Function of Mach + + # Coefficient callables must accept the full 7-variable argument tuple. The + # slope ``clalpha`` is a Function of Mach only; the fin roll coefficients + # ``cl_0``/``cl_p`` are AeroCoefficients evaluated over the full tuple. + def make_normal(slope): + def cN(alpha, beta, mach, reynolds, pitch_rate, yaw_rate, roll_rate): + return slope.get_value_opt(mach) * alpha + + return cN + + def make_side(slope): + def cY(alpha, beta, mach, reynolds, pitch_rate, yaw_rate, roll_rate): + return -slope.get_value_opt(mach) * beta + + return cY + + coefficients = {"cN": make_normal(clalpha), "cY": make_side(clalpha)} + if getattr(surface, "roll_parameters", None) is not None: + cl_0, cl_p = surface.cl_0, surface.cl_p + + def make_roll(forcing, damping): + def cl(alpha, beta, mach, reynolds, pitch_rate, yaw_rate, roll_rate): + args = (alpha, beta, mach, reynolds, pitch_rate, yaw_rate, roll_rate) + cant = forcing.get_value_opt(*args) + return cant + damping.get_value_opt(*args) * roll_rate + + return cl + + coefficients["cl"] = make_roll(cl_0, cl_p) + + generic_surface = GenericSurface( + reference_area=surface.reference_area, + reference_length=surface.reference_length, + coefficients=coefficients, + center_of_pressure=(0, 0, 0), + name=f"{surface.name}_generic", + ) + generic_surface.barrowman_cpz = surface.cpz + return generic_surface + + +# The three Barrowman surfaces of the Calisto rocket and the axial stations +# (in the tail_to_nose user coordinate system) at which their origins sit. +_CALISTO_SURFACE_STATIONS = ( + ("nose", 1.160), + ("tail", -1.313), + ("fins", -1.168), +) + + +def _full_body_force_and_moment(rocket, alpha, beta, mach, omega, speed=1.0): + """Total body-frame force and moment about the dry center of mass, summed + over every aerodynamic surface at a flow state and set of body rates. + + Reproduces the flight integrator's per-surface computation: each surface is + fed its own local stream velocity, which includes the ``omega ^ cp`` + lever-arm term, so the sum captures the pitch/yaw damping the distributed + surfaces produce. Evaluated at unit air density; the result scales out of any + coefficient ratio. Used to lump the distributed surfaces into a single + full-body coefficient set. + """ + stream_direction = Vector([-math.tan(beta), -math.tan(alpha), -1.0]) + stream_at_cdm = stream_direction / abs(stream_direction) * speed + body_rates = Vector(list(omega)) + density = Function(1.0) + dynamic_viscosity = Function(1e30) # vanishing-Reynolds limit + speed_of_sound = speed / mach if mach > 0 else 1e30 + totals = np.zeros(6) + for surface, _ in rocket.aerodynamic_surfaces: + cp = rocket.surfaces_cp_to_cdm[surface] + comp_stream = stream_at_cdm - (body_rates ^ cp) + comp_speed = abs(comp_stream) + forces = surface.compute_forces_and_moments( + comp_stream, + comp_speed, + comp_speed / speed_of_sound, + 1.0, + cp, + body_rates, + density, + dynamic_viscosity, + 0.0, + ) + totals += np.array(forces) + return totals + + +def _full_body_derivatives(reference_rocket): + """Lump a rocket's distributed aerodynamic surfaces into one full-body + stability-derivative set (about the dry center of mass), as named slopes for + a :class:`LinearGenericSurface`. + + A single surface placed at the center of mass has no lever arm of its own, so + the pitch/yaw damping the distributed fore-and-aft surfaces produce has to be + carried explicitly by rate derivatives. Every derivative is measured by + finite-differencing the distributed aerodynamics + (:func:`_full_body_force_and_moment`) and tabulated against Mach: the static + slopes ``cN_alpha``/``cm_alpha`` (pitch) and ``cY_beta``/``cn_beta`` (yaw), + the rate-damping slopes ``cN_q``/``cm_q`` and ``cY_r``/``cn_r``, and the fin + roll damping ``cl_p``. Axial (drag) is left out so the rocket's own drag curve + still applies. Handed to a linear surface, these reproduce the reference + rocket's aerodynamics as a lumped model -- the way a measured or CFD + stability-derivative set is used. + """ + reference_length = 2 * reference_rocket.radius + area = reference_rocket.area + machs = np.arange(0.0, 3.01, 0.02) + step = 1e-5 + dyn_area = 0.5 * area # unit speed, unit density + dyn_area_length = dyn_area * reference_length + + def coefficients_at(alpha, beta, red_pitch, red_yaw, red_roll, mach): + rate_factor = 2.0 / reference_length # reduced rate -> omega at unit speed + omega = (red_pitch * rate_factor, red_yaw * rate_factor, red_roll * rate_factor) + r1, r2, r3, m1, m2, m3 = _full_body_force_and_moment( + reference_rocket, alpha, beta, mach, omega + ) + return { + "cN": -r2 / dyn_area, + "cY": r1 / dyn_area, + "cm": m1 / dyn_area_length, + "cn": m2 / dyn_area_length, + "cl": m3 / dyn_area_length, + } + + def slope(field, coeff): + values = [] + for mach in machs: + state = { + "alpha": 0.0, + "beta": 0.0, + "red_pitch": 0.0, + "red_yaw": 0.0, + "red_roll": 0.0, + } + high = dict(state, **{field: step}) + low = dict(state, **{field: -step}) + c_high = coefficients_at(mach=mach, **high) + c_low = coefficients_at(mach=mach, **low) + values.append((c_high[coeff] - c_low[coeff]) / (2 * step)) + return Function( + np.column_stack([machs, values]), + "Mach", + f"{coeff}_slope", + interpolation="akima", + extrapolation="constant", + ) + + # Named slopes consumed directly by LinearGenericSurface: the rate names + # ``cN_q``/``cm_q`` multiply the reduced pitch rate, ``cY_r``/``cn_r`` the + # reduced yaw rate and ``cl_p`` the reduced roll rate. + return { + "cN_alpha": slope("alpha", "cN"), + "cm_alpha": slope("alpha", "cm"), + "cN_q": slope("red_pitch", "cN"), + "cm_q": slope("red_pitch", "cm"), + "cY_beta": slope("beta", "cY"), + "cn_beta": slope("beta", "cn"), + "cY_r": slope("red_yaw", "cY"), + "cn_r": slope("red_yaw", "cn"), + "cl_p": slope("red_roll", "cl"), + } + + @pytest.fixture def calisto_motorless(): """Create a simple object of the Rocket class to be used in the tests. This @@ -319,6 +518,162 @@ def calisto_linear_generic( return calisto +def _bare_calisto(motor): + """The Calisto body and motor with no aerodynamic surfaces, parachutes or + rail buttons yet -- the shared starting point for the generic-surface + Calistos below.""" + calisto = Rocket( + radius=0.0635, + mass=14.426, + inertia=(6.321, 6.321, 0.034), + power_off_drag="data/rockets/calisto/powerOffDragCurve.csv", + power_on_drag="data/rockets/calisto/powerOnDragCurve.csv", + center_of_mass_without_motor=0, + coordinate_system_orientation="tail_to_nose", + ) + calisto.add_motor(motor, position=-1.373) + return calisto + + +@pytest.fixture +def calisto_generic( + cesaroni_m1670, + calisto_nose_cone, + calisto_tail, + calisto_trapezoidal_fins, + calisto_main_chute, + calisto_drogue_chute, +): + """Calisto built entirely from :class:`GenericSurface` objects. + + The exact counterpart of ``calisto_linear_generic``, but each surface is a + plain :class:`GenericSurface` carrying the *total* body-frame coefficients + (``cN = clalpha * alpha`` ...) instead of a :class:`LinearGenericSurface` + carrying the coefficient slopes. The two express the same aerodynamics + through different code paths, so this rocket's flight should match both + ``calisto_robust`` and ``calisto_linear_generic``. It is standalone (it does + not reuse the shared ``calisto`` fixture), so a test may build it alongside + the others and compare their flights. + + Parameters + ---------- + cesaroni_m1670 : rocketpy.SolidMotor + The Calisto motor. This is a pytest fixture too. + calisto_nose_cone : rocketpy.NoseCone + The standard nose cone whose aero curves are copied. This is a pytest + fixture too. + calisto_tail : rocketpy.Tail + The standard boat tail whose aero curves are copied. This is a pytest + fixture too. + calisto_trapezoidal_fins : rocketpy.TrapezoidalFins + The standard fin set whose aero curves are copied. This is a pytest + fixture too. + calisto_main_chute : rocketpy.Parachute + The main parachute of the Calisto rocket. This is a pytest fixture too. + calisto_drogue_chute : rocketpy.Parachute + The drogue parachute of the Calisto rocket. This is a pytest fixture too. + + Returns + ------- + rocketpy.Rocket + The Calisto rocket whose nose cone, tail and fins are all + GenericSurfaces. + """ + calisto = _bare_calisto(cesaroni_m1670) + barrowman_surfaces = { + "nose": calisto_nose_cone, + "tail": calisto_tail, + "fins": calisto_trapezoidal_fins, + } + for label, station in _CALISTO_SURFACE_STATIONS: + generic_surface = _generic_surface_from_barrowman(barrowman_surfaces[label]) + calisto.add_surfaces(generic_surface, station - generic_surface.barrowman_cpz) + calisto.set_rail_buttons( + upper_button_position=0.082, + lower_button_position=-0.618, + angular_position=0, + ) + calisto.parachutes.append(calisto_main_chute) + calisto.parachutes.append(calisto_drogue_chute) + return calisto + + +@pytest.fixture +def calisto_full_aerodynamics( + cesaroni_m1670, + calisto_nose_cone, + calisto_tail, + calisto_trapezoidal_fins, + calisto_main_chute, + calisto_drogue_chute, +): + """Calisto flown from a single full-body stability-derivative set. + + Instead of modeling each surface, this rocket carries one + :class:`LinearGenericSurface` added through + :meth:`Rocket.add_full_body_aerodynamics` that lumps the whole rocket into a + set of named coefficient slopes referenced to the dry center of mass. The + slopes are extracted from an equivalent ``calisto_generic`` rocket + (:func:`_full_body_derivatives`): the static normal-force and + pitch/yaw-moment slopes plus the pitch/yaw rate damping the distributed + surfaces produce through their lever arms and the fin roll damping. The + built-in drag curve is kept (no drag coefficient is supplied), exactly as for + the modeled Calisto, so this rocket's flight should match ``calisto_robust``, + ``calisto_linear_generic`` and ``calisto_generic``. + + Parameters + ---------- + cesaroni_m1670 : rocketpy.SolidMotor + The Calisto motor. This is a pytest fixture too. + calisto_nose_cone : rocketpy.NoseCone + The standard nose cone whose aero curves are lumped in. This is a pytest + fixture too. + calisto_tail : rocketpy.Tail + The standard boat tail whose aero curves are lumped in. This is a pytest + fixture too. + calisto_trapezoidal_fins : rocketpy.TrapezoidalFins + The standard fin set whose aero curves are lumped in. This is a pytest + fixture too. + calisto_main_chute : rocketpy.Parachute + The main parachute of the Calisto rocket. This is a pytest fixture too. + calisto_drogue_chute : rocketpy.Parachute + The drogue parachute of the Calisto rocket. This is a pytest fixture too. + + Returns + ------- + rocketpy.Rocket + The Calisto rocket flown from a single full-body derivative set. + """ + # Distributed reference rocket (same motor, so the same dry center of mass) + # whose lumped derivatives feed the single full-body surface. + reference = _bare_calisto(cesaroni_m1670) + barrowman_surfaces = { + "nose": calisto_nose_cone, + "tail": calisto_tail, + "fins": calisto_trapezoidal_fins, + } + for label, station in _CALISTO_SURFACE_STATIONS: + generic_surface = _generic_surface_from_barrowman(barrowman_surfaces[label]) + reference.add_surfaces(generic_surface, station - generic_surface.barrowman_cpz) + + calisto = _bare_calisto(cesaroni_m1670) + full_body_surface = LinearGenericSurface( + reference_area=calisto.area, + reference_length=2 * calisto.radius, + coefficients=_full_body_derivatives(reference), + name="Calisto full body aerodynamics", + ) + calisto.add_full_body_aerodynamics(full_body_surface) + calisto.set_rail_buttons( + upper_button_position=0.082, + lower_button_position=-0.618, + angular_position=0, + ) + calisto.parachutes.append(calisto_main_chute) + calisto.parachutes.append(calisto_drogue_chute) + return calisto + + @pytest.fixture def calisto_nose_to_tail_robust( calisto_nose_to_tail, diff --git a/tests/unit/rocket/aero_surface/test_aero_coefficient.py b/tests/unit/rocket/aero_surface/test_aero_coefficient.py index ed568773a..e795c02ac 100644 --- a/tests/unit/rocket/aero_surface/test_aero_coefficient.py +++ b/tests/unit/rocket/aero_surface/test_aero_coefficient.py @@ -72,25 +72,14 @@ def test_repr_constant_and_function(): assert "depends_on" in function_repr and "mach" in function_repr -# -- Independent-variable axes (unsteady / control) --------------------------- +# -- Independent-variable axes (control) -------------------------------------- -def test_build_independent_vars_base_unsteady_and_controls(): +def test_build_independent_vars_base_and_controls(): assert build_independent_vars() == IV - assert build_independent_vars(unsteady_aero=True) == IV + ["alpha_dot", "beta_dot"] assert build_independent_vars(control_variables=("defl",)) == IV + ["defl"] -def test_unsteady_aero_extends_independent_vars(): - coeff = AeroCoefficient( - lambda alpha_dot: alpha_dot, ("alpha_dot",), unsteady_aero=True, name="cL" - ) - assert coeff.independent_vars == tuple(IV + ["alpha_dot", "beta_dot"]) - assert coeff.__dom_dim__ == 9 - # alpha_dot is the 8th argument (index 7). - assert coeff(0, 0, 0, 0, 0, 0, 0, 1.5, 0) == pytest.approx(1.5) - - def test_control_variable_axis_is_appended(): coeff = AeroCoefficient( lambda deflection: 2 * deflection, @@ -201,12 +190,10 @@ def test_to_dict_from_dict_preserves_axes(): original = AeroCoefficient( lambda deflection: deflection, ("deflection",), - unsteady_aero=True, control_variables=("deflection",), name="cL", ) rebuilt = AeroCoefficient.from_dict(original.to_dict()) - assert rebuilt.unsteady_aero is True assert rebuilt.control_variables == ("deflection",) assert rebuilt.independent_vars == original.independent_vars diff --git a/tests/unit/rocket/aero_surface/test_controllable_generic_surface.py b/tests/unit/rocket/aero_surface/test_controllable_generic_surface.py index 837803648..8e893b14c 100644 --- a/tests/unit/rocket/aero_surface/test_controllable_generic_surface.py +++ b/tests/unit/rocket/aero_surface/test_controllable_generic_surface.py @@ -99,3 +99,42 @@ def test_plain_generic_surface_default_independent_vars_unchanged(): "yaw_rate", "roll_rate", ] + + +def test_active_during_preset_round_trips_through_dict(): + """A preset activation policy survives to_dict/from_dict (jet-vane case).""" + surface = ControllableGenericSurface( + reference_area=1, + reference_length=0.2, + coefficients={}, + active_during="power_on", + ) + restored = ControllableGenericSurface.from_dict(surface.to_dict()) + assert restored.active_during == "power_on" + + +def test_active_during_callable_round_trips_through_dict(): + """A custom activation function is pickled through to_dict/from_dict and + restored to a working callable.""" + surface = ControllableGenericSurface( + reference_area=1, + reference_length=0.2, + coefficients={}, + active_during=lambda t, flight: t < 1.0, + ) + restored = ControllableGenericSurface.from_dict(surface.to_dict()) + assert callable(restored.active_during) + assert restored.active_during(0.5, None) is True + assert restored.active_during(2.0, None) is False + + +def test_active_during_callable_dropped_when_pickling_disabled(): + """With allow_pickle=False a custom function cannot be stored, so it saves + as the 'always' preset rather than a broken reference.""" + surface = ControllableGenericSurface( + reference_area=1, + reference_length=0.2, + coefficients={}, + active_during=lambda t, flight: t < 1.0, + ) + assert surface.to_dict(allow_pickle=False)["active_during"] == "always" diff --git a/tests/unit/rocket/aero_surface/test_generic_surfaces.py b/tests/unit/rocket/aero_surface/test_generic_surfaces.py index 86e2269a6..5bbe81395 100644 --- a/tests/unit/rocket/aero_surface/test_generic_surfaces.py +++ b/tests/unit/rocket/aero_surface/test_generic_surfaces.py @@ -1,8 +1,18 @@ +import json +from types import SimpleNamespace + import pytest -from rocketpy import Function, GenericSurface +from rocketpy import Function, GenericSurface, LinearGenericSurface +from rocketpy._encoders import RocketPyDecoder, RocketPyEncoder from rocketpy.mathutils import Vector + +def _rpy_round_trip(obj): + """Encode ``obj`` and decode it back through the .rpy encoder/decoder.""" + return json.loads(json.dumps(obj, cls=RocketPyEncoder), cls=RocketPyDecoder) + + REFERENCE_AREA = 1 REFERENCE_LENGTH = 1 @@ -265,3 +275,200 @@ def test_angular_rates_are_non_dimensionalized(): assert roll_moment == pytest.approx(dyn_pressure_area_length * reduced_roll) # ... not the raw rad/s rate. assert roll_moment != pytest.approx(dyn_pressure_area_length * raw_roll) + + +class _ExplodingAtmosphere: + """Stand-in for density/viscosity whose lookup raises, so a test can assert + the Reynolds computation (and thus the lookup) is skipped.""" + + def get_value_opt(self, z): + raise AssertionError("atmosphere lookup should have been skipped") + + +def test_reynolds_length_defaults_to_reference_length(): + gs = GenericSurface(REFERENCE_AREA, 0.2, {"cN": 0}) + assert gs.reynolds_length == 0.2 + + +def test_reynolds_length_override(): + gs = GenericSurface(REFERENCE_AREA, 0.2, {"cN": 0}, reynolds_length=4.0) + assert gs.reynolds_length == 4.0 + # The moment/rate reference length is left untouched. + assert gs.reference_length == 0.2 + + +def test_needs_reynolds_reflects_coefficient_dependence(): + without = GenericSurface( + REFERENCE_AREA, REFERENCE_LENGTH, {"cN": lambda mach: mach} + ) + assert without._needs_reynolds is False + + with_re = GenericSurface( + REFERENCE_AREA, REFERENCE_LENGTH, {"cN": lambda reynolds: reynolds} + ) + assert with_re._needs_reynolds is True + + +def test_reynolds_computation_skipped_when_no_coefficient_uses_it(): + """A surface with no Reynolds-dependent coefficient must not perform the + per-step atmosphere lookups (the exploding stand-ins would raise if it did).""" + gs = GenericSurface(REFERENCE_AREA, REFERENCE_LENGTH, {"cN": lambda mach: mach}) + gs.compute_forces_and_moments( + stream_velocity=Vector((0, 0, -100)), + stream_speed=100, + stream_mach=0.3, + rho=1.0, + cp=Vector((0, 0, 0)), + omega=(0, 0, 0), + density=_ExplodingAtmosphere(), + dynamic_viscosity=_ExplodingAtmosphere(), + z=0, + ) + + +def test_reynolds_uses_reynolds_length_not_reference_length(): + """The Reynolds number handed to the coefficients is built on + ``reynolds_length``, not the (diameter) reference length.""" + ref_area, ref_length, re_length = 1.0, 0.2, 4.0 + rho_atm, mu, speed, rho = 1.2, 2.0e-5, 100.0, 1.0 + # cN returns the Reynolds number it is given, so the normal force exposes it. + gs = GenericSurface( + ref_area, + ref_length, + {"cN": lambda reynolds: reynolds}, + reynolds_length=re_length, + ) + + _, r2, *_ = gs.compute_forces_and_moments( + stream_velocity=Vector((0, 0, -speed)), # centerline -> alpha=beta=0 + stream_speed=speed, + stream_mach=0.3, + rho=rho, + cp=Vector((0, 0, 0)), + omega=(0, 0, 0), + density=Function(rho_atm), + dynamic_viscosity=Function(mu), + z=0, + ) + + # R2 = -normal = -(0.5 rho V^2 A_ref) * Re_seen + reynolds_seen = -r2 / (0.5 * rho * speed**2 * ref_area) + assert reynolds_seen == pytest.approx(rho_atm * speed * re_length / mu) + # ... which differs from the diameter-based value. + assert reynolds_seen != pytest.approx(rho_atm * speed * ref_length / mu) + + +def _fake_flight(burn_out_time): + """Minimal stand-in exposing only what ``is_active`` reads + (``flight.rocket.motor.burn_out_time``), so no real Flight is built.""" + return SimpleNamespace( + rocket=SimpleNamespace(motor=SimpleNamespace(burn_out_time=burn_out_time)) + ) + + +def test_active_during_defaults_to_always(): + """By default a surface is active at every time.""" + gs = GenericSurface(REFERENCE_AREA, REFERENCE_LENGTH, {"cN": 1}) + flight = _fake_flight(burn_out_time=3.0) + assert gs.active_during == "always" + assert gs.is_active(0.0, flight) is True + assert gs.is_active(5.0, flight) is True + + +def test_active_during_power_on_gates_at_burnout(): + """A power-on surface is active up to (not including) burnout.""" + gs = GenericSurface( + REFERENCE_AREA, REFERENCE_LENGTH, {"cN": 1}, active_during="power_on" + ) + flight = _fake_flight(burn_out_time=3.0) + assert gs.is_active(2.999, flight) is True + assert gs.is_active(3.0, flight) is False + assert gs.is_active(4.0, flight) is False + + +def test_active_during_power_off_gates_at_burnout(): + """A power-off surface is active only from burnout onward.""" + gs = GenericSurface( + REFERENCE_AREA, REFERENCE_LENGTH, {"cN": 1}, active_during="power_off" + ) + flight = _fake_flight(burn_out_time=3.0) + assert gs.is_active(2.999, flight) is False + assert gs.is_active(3.0, flight) is True + assert gs.is_active(4.0, flight) is True + + +def test_active_during_accepts_callable(): + """A custom predicate receives (t, flight) and drives activation.""" + seen = [] + + def only_after_one_second(t, flight): + seen.append((t, flight)) + return t > 1.0 + + gs = GenericSurface( + REFERENCE_AREA, + REFERENCE_LENGTH, + {"cN": 1}, + active_during=only_after_one_second, + ) + flight = _fake_flight(burn_out_time=3.0) + assert gs.is_active(0.5, flight) is False + assert gs.is_active(2.0, flight) is True + # The predicate was called with the time and the flight object. + assert seen[0] == (0.5, flight) + + +def test_active_during_invalid_value_raises(): + """An unknown activation policy is rejected at construction.""" + with pytest.raises(ValueError, match="active_during"): + GenericSurface( + REFERENCE_AREA, + REFERENCE_LENGTH, + {"cN": 1}, + active_during="sometimes", + ) + + +def test_generic_surface_round_trips_through_encoder(): + """A GenericSurface survives the full .rpy encode/decode: coefficients, + reynolds_length and a custom activation function are all restored.""" + gs = GenericSurface( + reference_area=1.0, + reference_length=0.2, + coefficients={"cN": lambda mach: 2 * mach, "cm": 0.1}, + reynolds_length=4.0, + active_during=lambda t, flight: t < 3.0, + ) + restored = _rpy_round_trip(gs) + + assert isinstance(restored, GenericSurface) + assert restored.reynolds_length == 4.0 + assert restored.cN(0, 0, 0.5, 0, 0, 0, 0) == pytest.approx(1.0) + assert restored.cm(0, 0, 0, 0, 0, 0, 0) == pytest.approx(0.1) + assert restored.active_during(1.0, None) is True + assert restored.active_during(5.0, None) is False + + +def test_linear_generic_surface_round_trips_through_encoder(): + """A LinearGenericSurface restores its derivative coefficients and the + Reynolds length through the .rpy encode/decode.""" + lgs = LinearGenericSurface( + reference_area=1.0, + reference_length=0.2, + coefficients={"cN_alpha": 2.0, "cm_alpha": -0.5}, + reynolds_length=3.0, + ) + restored = _rpy_round_trip(lgs) + + assert isinstance(restored, LinearGenericSurface) + assert restored.reynolds_length == 3.0 + assert restored.cN_alpha(0, 0, 0, 0, 0, 0, 0) == pytest.approx(2.0) + assert restored.cm_alpha(0, 0, 0, 0, 0, 0, 0) == pytest.approx(-0.5) + + +def test_generic_surface_preset_active_during_round_trips(): + """A preset activation policy round-trips as the plain string.""" + gs = GenericSurface( + REFERENCE_AREA, REFERENCE_LENGTH, {"cN": 0}, active_during="power_on" + ) + assert _rpy_round_trip(gs).active_during == "power_on" diff --git a/tests/unit/rocket/aero_surface/test_surface_coefficient_completeness.py b/tests/unit/rocket/aero_surface/test_surface_coefficient_completeness.py index 674e583b7..a0c413080 100644 --- a/tests/unit/rocket/aero_surface/test_surface_coefficient_completeness.py +++ b/tests/unit/rocket/aero_surface/test_surface_coefficient_completeness.py @@ -106,7 +106,7 @@ def _surface_params(): def _coefficient_arguments(surface): """A representative independent-variable tuple for the surface: the seven base variables (alpha, beta, mach, reynolds, and the three rates) plus any - unsteady / control axes the surface adds, filled with zeros.""" + control axes the surface adds, filled with zeros.""" base = [0.05, 0.02, 0.5, 1e6, 0.0, 0.0, 0.0] extra = len(surface.independent_vars) - len(base) return tuple(base + [0.0] * max(0, extra)) diff --git a/tests/unit/rocket/aero_surface/test_unsteady_generic_surface.py b/tests/unit/rocket/aero_surface/test_unsteady_generic_surface.py deleted file mode 100644 index 893f90faf..000000000 --- a/tests/unit/rocket/aero_surface/test_unsteady_generic_surface.py +++ /dev/null @@ -1,71 +0,0 @@ -"""Unit tests for the optional alpha_dot/beta_dot unsteady coefficient axes of -GenericSurface.""" - -import pytest - -from rocketpy import Function, GenericSurface -from rocketpy.mathutils.vector_matrix import Vector - -DENSITY = Function(lambda z: 1.16) -VISCOSITY = Function(lambda z: 1.8e-5) - - -def _pitch_moment(surface, alpha_dot): - return surface.compute_forces_and_moments( - Vector([0, 0, -100]), - 100, - 0.29, - 1.16, - Vector([0, 0, 0]), - Vector([0, 0, 0]), - DENSITY, - VISCOSITY, - 100.0, - alpha_dot=alpha_dot, - beta_dot=0.0, - )[3] - - -def test_unsteady_axes_extend_independent_vars(): - surface = GenericSurface( - reference_area=1, reference_length=0.2, coefficients={}, unsteady_aero=True - ) - assert surface.independent_vars[7:] == ["alpha_dot", "beta_dot"] - - -def test_prescribed_alpha_dot_produces_unsteady_pitch_moment(): - surface = GenericSurface( - reference_area=1, - reference_length=0.2, - coefficients={ - "cm": lambda a, b, m, re, p, q, r, alpha_dot, beta_dot: 0.7 * alpha_dot - }, - unsteady_aero=True, - ) - m0 = _pitch_moment(surface, 0.0) - m1 = _pitch_moment(surface, 0.5) - m2 = _pitch_moment(surface, 1.0) - assert m0 == pytest.approx(0.0) - assert m1 != pytest.approx(0.0) - assert m2 == pytest.approx(2 * m1) - - -def test_default_surface_ignores_alpha_dot_and_stays_seven_var(): - """Existing 7-variable surfaces must be unaffected: independent vars - unchanged and alpha_dot/beta_dot ignored at evaluation.""" - surface = GenericSurface( - reference_area=1, - reference_length=0.2, - coefficients={"cm": lambda a, b, m, re, p, q, r: 0.1}, - ) - assert surface.independent_vars == [ - "alpha", - "beta", - "mach", - "reynolds", - "pitch_rate", - "yaw_rate", - "roll_rate", - ] - # passing nonzero alpha_dot must not change the result - assert _pitch_moment(surface, 0.0) == pytest.approx(_pitch_moment(surface, 99.0)) diff --git a/tests/unit/rocket/test_generic_calisto_equivalence.py b/tests/unit/rocket/test_generic_calisto_equivalence.py new file mode 100644 index 000000000..0882a5111 --- /dev/null +++ b/tests/unit/rocket/test_generic_calisto_equivalence.py @@ -0,0 +1,69 @@ +"""Non-Flight equivalence checks for the generic-surface Calisto rockets. + +``calisto_robust`` (Barrowman surfaces), ``calisto_linear_generic`` (per-surface +LinearGenericSurface), ``calisto_generic`` (per-surface GenericSurface) and +``calisto_full_aerodynamics`` (a single lumped full-body GenericSurface) all +describe the same Calisto aerodynamics. These tests pin that equivalence at the +coefficient/stability level -- without running a Flight -- so a regression in any +of the generic-surface code paths is caught cheaply. The full 6-DOF flight +comparison lives in ``tests/unit/simulation/test_flight.py``. +""" + +import numpy as np +import pytest + +GENERIC_FIXTURES = [ + "calisto_linear_generic", + "calisto_generic", + "calisto_full_aerodynamics", +] + + +@pytest.mark.parametrize("generic_name", GENERIC_FIXTURES) +def test_generic_calisto_matches_static_margin(request, calisto_robust, generic_name): + """Each generic-surface Calisto reproduces the Barrowman rocket's static + margin and Mach-dependent aerodynamic center.""" + generic = request.getfixturevalue(generic_name) + assert generic.static_margin(0) == pytest.approx( + calisto_robust.static_margin(0), rel=1e-3 + ) + for mach in (0.0, 0.3, 0.8, 1.2, 2.0): + assert generic.aerodynamic_center.get_value_opt(mach) == pytest.approx( + calisto_robust.aerodynamic_center.get_value_opt(mach), abs=1e-3 + ) + + +@pytest.mark.parametrize("generic_name", GENERIC_FIXTURES) +def test_generic_calisto_matches_aggregate_slopes( + request, calisto_robust, generic_name +): + """Each generic-surface Calisto reproduces the Barrowman rocket's total + normal-force and side-force curve slopes (the aggregate ``cN_alpha`` and + ``cY_beta`` the whole rocket presents) across a range of Mach numbers.""" + generic = request.getfixturevalue(generic_name) + for mach in (0.2, 0.6, 0.9, 1.5, 2.5): + assert generic.total_lift_coeff_der.get_value_opt(mach) == pytest.approx( + calisto_robust.total_lift_coeff_der.get_value_opt(mach), rel=2e-3 + ) + assert generic.total_side_coeff_der.get_value_opt(mach) == pytest.approx( + calisto_robust.total_side_coeff_der.get_value_opt(mach), rel=2e-3 + ) + + +def test_generic_and_linear_calisto_are_identical( + calisto_generic, calisto_linear_generic +): + """The per-surface GenericSurface and LinearGenericSurface Calistos express + the same coefficients through different code paths, so their aggregate + force-curve slopes and aerodynamic centers must agree to numerical + precision.""" + for mach in (0.2, 0.7, 1.3, 2.5): + for attr in ( + "total_lift_coeff_der", + "total_side_coeff_der", + "aerodynamic_center", + "aerodynamic_center_yaw", + ): + generic = getattr(calisto_generic, attr).get_value_opt(mach) + linear = getattr(calisto_linear_generic, attr).get_value_opt(mach) + assert generic == pytest.approx(linear, rel=1e-9, abs=1e-12) diff --git a/tests/unit/rocket/test_stability_rework.py b/tests/unit/rocket/test_stability_rework.py index b2995a514..8c27b07d1 100644 --- a/tests/unit/rocket/test_stability_rework.py +++ b/tests/unit/rocket/test_stability_rework.py @@ -5,6 +5,20 @@ import numpy as np import pytest +from rocketpy import Function, GenericSurface, LinearGenericSurface, Rocket + + +def _full_body_surface(rocket, coefficients, name="Full Body Aerodynamics", **kwargs): + """Build a full-body GenericSurface referenced to the rocket dimensions, + the way a user would before handing it to ``add_full_body_aerodynamics``.""" + return GenericSurface( + reference_area=rocket.area, + reference_length=2 * rocket.radius, + coefficients=coefficients, + name=name, + **kwargs, + ) + def test_cp_position_alias_matches_aerodynamic_center(calisto_robust): """``cp_position`` is a plain alias of ``aerodynamic_center`` (no warning).""" @@ -14,36 +28,45 @@ def test_cp_position_alias_matches_aerodynamic_center(calisto_robust): ) -def test_reconstructed_center_of_pressure_converges_to_aerodynamic_center( - calisto_robust, -): - """The nonlinear center of pressure, reconstructed from the aggregate - coefficients as ``x_cdm + csys * d * Cm / CN``, converges to the linear - aerodynamic center as the angle of attack goes to zero. (The singular - nonlinear CP is no longer a blessed method; this is its documented - reconstruction path.)""" +def test_length_spans_nose_tip_to_aft_surface(calisto_robust): + """The overall length runs from the nose tip to the aft-most surface, and + does not depend on the coordinate-system orientation.""" rocket = calisto_robust - mach = 0.3 - aerodynamic_center = rocket.aerodynamic_center.get_value_opt(mach) - csys = rocket._csys - diameter = 2 * rocket.radius - cdm = rocket.center_of_dry_mass_position + # Nose tip at z = 1.160; tail base at z = -1.313 - 0.060 = -1.373. + assert rocket.length == pytest.approx(1.160 - (-1.373), abs=1e-9) + - coeffs = rocket.aerodynamic_coefficients_full(np.radians(0.1), 0.0, mach) - reconstructed_cp = cdm + csys * diameter * coeffs["cm"] / coeffs["cN"] - assert reconstructed_cp == pytest.approx(aerodynamic_center, abs=1e-3) +def test_length_orientation_independent(calisto_robust, calisto_nose_to_tail): + """The same physical rocket has the same length in either orientation.""" + # Mirror of calisto_robust: nose tip at z=0, then every surface reference + # sits at the same distance from the nose tip as in calisto_robust, so the + # physical rocket (and its length) is identical. The aft-most point is the + # tail base at z = 2.473 + 0.060 = 2.533. + calisto_nose_to_tail.add_nose(length=0.55829, kind="vonKarman", position=0.0) + calisto_nose_to_tail.add_tail( + top_radius=0.0635, bottom_radius=0.0435, length=0.060, position=2.473 + ) + calisto_nose_to_tail.add_trapezoidal_fins( + n=4, root_chord=0.120, tip_chord=0.040, span=0.100, position=2.328 + ) + assert calisto_nose_to_tail.length == pytest.approx(calisto_robust.length, abs=1e-9) -def test_aerodynamic_coefficients_normal_force_grows_with_alpha(calisto_robust): - """Total normal-force coefficient increases with angle of attack and is zero - at zero incidence; the returned dict exposes normal force and pitch moment.""" +def test_length_extends_to_nozzle_past_surfaces(calisto_robust, cesaroni_m1670): + """When the motor nozzle extends aft of the last aerodynamic surface, the + length runs from the nose tip to the nozzle rather than to the surface.""" rocket = calisto_robust - coeffs = rocket.aerodynamic_coefficients(np.radians(5), 0.0, 0.3) - assert set(coeffs) == {"normal_force", "pitch_moment"} + # Move the motor aft so its nozzle (at the motor origin, z = -2.0 in the + # rocket frame) sits past the tail base at z = -1.373. + rocket.add_motor(cesaroni_m1670, position=-2.0) + assert rocket.nozzle_position == pytest.approx(-2.0, abs=1e-9) + assert rocket.length == pytest.approx(1.160 - (-2.0), abs=1e-9) - cn_2 = rocket.aerodynamic_coefficients(np.radians(2), 0.0, 0.3)["normal_force"] - cn_8 = rocket.aerodynamic_coefficients(np.radians(8), 0.0, 0.3)["normal_force"] - assert cn_8 > cn_2 > 0 + +def test_length_requires_a_surface(calisto): + """A rocket with no aerodynamic surfaces has no defined length.""" + with pytest.raises(ValueError, match="at least one aerodynamic surface"): + _ = calisto.length def test_axisymmetric_rocket_planes_coincide(calisto_robust): @@ -56,39 +79,252 @@ def test_axisymmetric_rocket_planes_coincide(calisto_robust): ) -def test_aerodynamic_coefficients_full_signed_set(calisto_robust): - """The full rocket coefficient set returns all six signed body-frame - coefficients; normal force grows with alpha, axial force comes from the - vehicle drag curve, and the pitch moment is restoring (negative) for a - stable rocket.""" +def test_add_full_body_aerodynamics(calisto_robust): + """A prebuilt full-body surface is added and contributes to the rocket + aggregate (rocket-as-GenericSurface).""" rocket = calisto_robust - coeffs = rocket.aerodynamic_coefficients_full(np.radians(5), 0.0, 0.3) - assert set(coeffs) == {"cN", "cY", "cA", "cm", "cn", "cl"} + base_slope = rocket.total_lift_coeff_der.get_value_opt(0.3) + n_before = len(rocket.aerodynamic_surfaces) + + surface = _full_body_surface(rocket, {"cN": lambda a, b, m, re, p, q, r: 2.0 * a}) + returned = rocket.add_full_body_aerodynamics(surface) + + assert returned is surface + assert len(rocket.aerodynamic_surfaces) == n_before + 1 + # A single surface is active during the whole flight by default. + assert surface.active_during == "always" + # The full-body surface exposes the uniform coefficient accessors. + assert surface.cN(np.radians(5), 0, 0.3, 0, 0, 0, 0) == pytest.approx( + 2.0 * np.radians(5) + ) + # Its normal-force slope adds to the rocket aggregate lift-curve slope. + assert rocket.total_lift_coeff_der.get_value_opt(0.3) > base_slope + + +def test_add_full_body_aerodynamics_linear_surface_with_damping(calisto_robust): + """A LinearGenericSurface stability-derivative set (with pitch and roll + damping) is accepted as a full-body model, and its damping derivatives + stay inspectable as named attributes.""" + rocket = calisto_robust + surface = LinearGenericSurface( + reference_area=rocket.area, + reference_length=2 * rocket.radius, + coefficients={ + "cN_alpha": 2.0, + "cm_alpha": -1.0, + "cm_q": -50.0, + "cl_p": -5.0, + }, + name="Full body derivatives", + ) + # overwrite clears the built-in drag even though this surface carries none. + with pytest.warns(UserWarning, match="were cleared"): + created = rocket.add_full_body_aerodynamics(surface, overwrite=True) + + assert created is surface + assert len(rocket.aerodynamic_surfaces) == 1 + assert surface.cm_q.get_value_opt(0, 0, 0.3, 0, 0, 0, 0) == pytest.approx(-50.0) + # The surface has no drag coefficient, so the rocket now has no drag at all. + assert rocket.power_off_drag_by_mach.get_value_opt(0.3) == pytest.approx(0.0) + assert rocket.power_on_drag_by_mach.get_value_opt(0.3) == pytest.approx(0.0) + + +def test_to_surface_round_trips(calisto_robust, cesaroni_m1670): + """``to_surface`` lumps the whole rocket into a power-off/power-on pair of + :class:`LinearGenericSurface` (carrying the pitch/yaw/roll damping and each + phase's drag) that reproduces the rocket's stability when added to a bare + rocket -- the inverse of ``add_full_body_aerodynamics``.""" + surfaces = calisto_robust.to_surface() + assert isinstance(surfaces, list) and len(surfaces) == 2 + power_off, power_on = surfaces + assert all(isinstance(s, LinearGenericSurface) for s in surfaces) + assert power_off.active_during == "power_off" + assert power_on.active_during == "power_on" + # The rate damping is captured as named, inspectable derivatives. + assert power_off.cm_q.get_value_opt(0, 0, 0.3, 0, 0, 0, 0) < 0 # pitch damping + assert power_off.cl_p.get_value_opt(0, 0, 0.3, 0, 0, 0, 0) < 0 # roll damping + # Each surface carries its own phase's drag as the axial coefficient. + assert power_off.cA.get_value_opt(0, 0, 0.3, 0, 0, 0, 0) == pytest.approx( + calisto_robust.power_off_drag_by_mach.get_value_opt(0.3), rel=1e-6 + ) + assert power_on.cA.get_value_opt(0, 0, 0.3, 0, 0, 0, 0) == pytest.approx( + calisto_robust.power_on_drag_by_mach.get_value_opt(0.3), rel=1e-6 + ) + + # A bare rocket (same body and motor) carrying only the lumped pair + # reproduces the modeled rocket's static margin and aerodynamic center. The + # surfaces carry the drag, so overwrite clears the bare rocket's curves. + bare = Rocket( + radius=0.0635, + mass=14.426, + inertia=(6.321, 6.321, 0.034), + power_off_drag="data/rockets/calisto/powerOffDragCurve.csv", + power_on_drag="data/rockets/calisto/powerOnDragCurve.csv", + center_of_mass_without_motor=0, + coordinate_system_orientation="tail_to_nose", + ) + bare.add_motor(cesaroni_m1670, position=-1.373) + with pytest.warns(UserWarning, match="were cleared"): + bare.add_full_body_aerodynamics(surfaces, overwrite=True) + + assert bare.power_off_drag_by_mach.get_value_opt(0.3) == pytest.approx(0.0) + assert bare.power_on_drag_by_mach.get_value_opt(0.3) == pytest.approx(0.0) + assert bare.static_margin(0) == pytest.approx( + calisto_robust.static_margin(0), rel=1e-3 + ) + for mach in (0.3, 0.8, 1.5): + assert bare.aerodynamic_center.get_value_opt(mach) == pytest.approx( + calisto_robust.aerodynamic_center.get_value_opt(mach), abs=1e-3 + ) + + +def test_to_coefficients_returns_phase_pair(calisto_robust): + """``to_coefficients`` returns a ``power_off``/``power_on`` pair of coefficient + sets, each a dict of Mach curves. The stability derivatives are the same in + both; only the drag ``cA_0`` differs by motor phase.""" + coeffs = calisto_robust.to_coefficients() + assert set(coeffs) == {"power_off", "power_on"} + body_keys = { + "cN_alpha", + "cm_alpha", + "cN_q", + "cm_q", + "cY_beta", + "cn_beta", + "cY_r", + "cn_r", + "cl_p", + "cA_0", + } + assert set(coeffs["power_off"]) == body_keys + assert set(coeffs["power_on"]) == body_keys + assert all(isinstance(c, Function) for c in coeffs["power_off"].values()) - low = rocket.aerodynamic_coefficients_full(np.radians(2), 0.0, 0.3) - assert coeffs["cN"] > low["cN"] > 0 - assert coeffs["cm"] < 0 # restoring pitch moment about the center of dry mass - assert coeffs["cA"] == pytest.approx( - rocket.power_off_drag_by_mach.get_value_opt(0.3) + # Stability derivatives are identical between phases; only drag differs. + assert coeffs["power_on"]["cN_alpha"].get_value_opt(0.3) == pytest.approx( + coeffs["power_off"]["cN_alpha"].get_value_opt(0.3) ) + assert coeffs["power_off"]["cA_0"].get_value_opt(0.3) == pytest.approx( + calisto_robust.power_off_drag_by_mach.get_value_opt(0.3), rel=1e-6 + ) + assert coeffs["power_on"]["cA_0"].get_value_opt(0.3) == pytest.approx( + calisto_robust.power_on_drag_by_mach.get_value_opt(0.3), rel=1e-6 + ) + + # It is exactly what ``to_surface`` wraps into surfaces. + power_off, _ = calisto_robust.to_surface() + assert power_off.cN_alpha.get_value_opt(0, 0, 0.3, 0, 0, 0, 0) == pytest.approx( + coeffs["power_off"]["cN_alpha"].get_value_opt(0.3) + ) + + # Wind naming flows through to each phase set. + wind = calisto_robust.to_coefficients(force_convention="wind") + assert "cL_alpha" in wind["power_off"] and "cD_0" in wind["power_off"] + with pytest.raises(ValueError, match="force_convention"): + calisto_robust.to_coefficients(force_convention="bogus") + + +def test_to_surface_force_convention(calisto_robust): + """``force_convention`` selects the coefficient naming (body ``cN``/``cA`` vs + wind ``cL``/``cD``) while yielding an equivalent surface pair.""" + args = (0, 0, 0.3, 0, 0, 0, 0) + drag = calisto_robust.power_off_drag_by_mach.get_value_opt(0.3) + + body_off, _ = calisto_robust.to_surface(force_convention="body") + wind_off, _ = calisto_robust.to_surface(force_convention="wind") + assert body_off.force_convention == "body" + assert wind_off.force_convention == "wind" + # Same underlying model either way (both store body-frame derivatives). + assert wind_off.cN_alpha.get_value_opt(*args) == pytest.approx( + body_off.cN_alpha.get_value_opt(*args) + ) + # The body axial and wind drag both equal the phase's drag. + assert body_off.cA.get_value_opt(*args) == pytest.approx(drag, rel=1e-6) + assert wind_off.cD.get_value_opt(*args) == pytest.approx(drag, rel=1e-6) + with pytest.raises(ValueError, match="force_convention"): + calisto_robust.to_surface(force_convention="bogus") -def test_add_vehicle_aerodynamic_surface(calisto_robust): - """A supplied full-vehicle coefficient set is added as a single generic - surface and contributes to the rocket aggregate (rocket-as-GenericSurface).""" + +def test_add_full_body_aerodynamics_power_on_off(calisto_robust): + """A power-on/power-off pair, passed as a list, adds two phase-gated + full-body surfaces.""" rocket = calisto_robust - base_cn = rocket.aerodynamic_coefficients_full(np.radians(5), 0.0, 0.3)["cN"] n_before = len(rocket.aerodynamic_surfaces) - surface = rocket.add_vehicle_aerodynamic_surface( - coefficients={"cN": lambda a, b, m, re, p, q, r: 2.0 * a} + power_on = _full_body_surface( + rocket, {"cD": 0.3}, name="Full body (power on)", active_during="power_on" + ) + power_off = _full_body_surface( + rocket, {"cD": 0.5}, name="Full body (power off)", active_during="power_off" ) + created = rocket.add_full_body_aerodynamics([power_on, power_off]) - assert len(rocket.aerodynamic_surfaces) == n_before + 1 - # The vehicle surface exposes the uniform coefficient accessors. - assert surface.cN(np.radians(5), 0, 0.3, 0, 0, 0, 0) == pytest.approx( - 2.0 * np.radians(5) + assert len(created) == 2 + assert len(rocket.aerodynamic_surfaces) == n_before + 2 + assert created[0].active_during == "power_on" + assert created[1].active_during == "power_off" + + +def test_add_full_body_aerodynamics_single_phase(calisto_robust): + """A single phase-gated surface (e.g. base drag after burnout) may be added + on its own.""" + rocket = calisto_robust + surface = _full_body_surface(rocket, {"cD": 0.5}, active_during="power_off") + created = rocket.add_full_body_aerodynamics(surface) + assert created is surface + assert surface.active_during == "power_off" + + +def test_add_full_body_aerodynamics_overwrite_replaces_surfaces_and_drag( + calisto_robust, +): + """overwrite=True removes existing surfaces and clears both built-in drag + curves; the supplied surface then provides the only drag.""" + rocket = calisto_robust + assert len(rocket.aerodynamic_surfaces) > 1 + assert rocket.power_off_drag_by_mach.get_value_opt(0.3) > 0 + + surface = _full_body_surface( + rocket, {"cA": 0.4, "cN": lambda a, b, m, re, p, q, r: 2.0 * a} ) - # Its normal force adds to the rocket aggregate. - new_cn = rocket.aerodynamic_coefficients_full(np.radians(5), 0.0, 0.3)["cN"] - assert new_cn > base_cn + with pytest.warns(UserWarning, match="were cleared"): + rocket.add_full_body_aerodynamics(surface, overwrite=True) + + # Only the full-body surface remains. + assert len(rocket.aerodynamic_surfaces) == 1 + assert rocket.aerodynamic_surfaces[0].component is surface + # Both built-in drag curves were cleared (the surface carries the drag now). + assert rocket.power_off_drag_by_mach.get_value_opt(0.3) == pytest.approx(0.0) + assert rocket.power_on_drag_by_mach.get_value_opt(0.3) == pytest.approx(0.0) + + +def test_add_full_body_aerodynamics_overwrite_always_clears_drag(calisto_robust): + """overwrite=True clears both built-in drag curves unconditionally, even for + a phase-gated surface that carries no drag of its own.""" + rocket = calisto_robust + assert rocket.power_off_drag_by_mach.get_value_opt(0.3) > 0 + assert rocket.power_on_drag_by_mach.get_value_opt(0.3) > 0 + + power_on = _full_body_surface(rocket, {"cD": 0.3}, active_during="power_on") + power_off = _full_body_surface(rocket, {"cN": 1.0}, active_during="power_off") + with pytest.warns(UserWarning, match="were cleared"): + rocket.add_full_body_aerodynamics([power_on, power_off], overwrite=True) + + # Both curves are cleared regardless of phase or whether a surface carries + # drag; the supplied surfaces are the complete aerodynamics. + assert rocket.power_on_drag_by_mach.get_value_opt(0.3) == pytest.approx(0.0) + assert rocket.power_off_drag_by_mach.get_value_opt(0.3) == pytest.approx(0.0) + + +def test_add_full_body_aerodynamics_overwrite_warns_on_later_add(calisto_robust): + """After an overwrite, adding another surface warns that it stacks on the + full-body model.""" + rocket = calisto_robust + surface = _full_body_surface(rocket, {"cD": 0.4}) + with pytest.warns(UserWarning): # the overwrite itself warns about drag + rocket.add_full_body_aerodynamics(surface, overwrite=True) + + later = _full_body_surface(rocket, {"cN": 1.0}) + with pytest.warns(UserWarning, match="stacks on|summed on top"): + rocket.add_full_body_aerodynamics(later) diff --git a/tests/unit/simulation/test_flight.py b/tests/unit/simulation/test_flight.py index 01c9e9f51..b74513554 100644 --- a/tests/unit/simulation/test_flight.py +++ b/tests/unit/simulation/test_flight.py @@ -688,6 +688,65 @@ def test_linear_generic_surface_flight_is_stable( assert np.nanmax(np.abs(angle_of_attack)) < 45 +@pytest.mark.parametrize( + "generic_rocket_name, apogee_rel_tol", + [ + ("calisto_linear_generic", 5e-3), + ("calisto_generic", 5e-3), + ("calisto_full_aerodynamics", 1e-2), + ], +) +def test_generic_surface_calisto_flight_matches_barrowman( + request, calisto_robust, example_plain_env, generic_rocket_name, apogee_rel_tol +): + """A Calisto rebuilt from generic surfaces flies the same as the Barrowman + Calisto. + + The reference ``calisto_robust`` models each surface with the classic + Barrowman method. The three generic-surface Calistos carry the same + aerodynamics through different code paths: per-surface + ``LinearGenericSurface`` (coefficient slopes), per-surface + ``GenericSurface`` (total coefficients), and a single full-body + ``LinearGenericSurface`` added with ``add_full_body_aerodynamics`` (the lumped + stability-derivative set, including the rate damping the distributed + surfaces produce through their lever arms). Flown from the same launcher in + still air, each must reach essentially the same apogee and leave the rail at + the same time and speed. Only the ascent is compared (``terminate_on_apogee``); + the descent under identical parachutes adds nothing to the comparison. + + The per-surface models reproduce the Barrowman forces almost exactly; the + lumped full-body model is a point approximation of the distributed + surfaces, so it is held to a slightly looser apogee tolerance. + """ + generic_rocket = request.getfixturevalue(generic_rocket_name) + + launch = dict( + environment=example_plain_env, + rail_length=5.2, + inclination=85, + heading=0, + terminate_on_apogee=True, + ) + reference_flight = Flight(rocket=calisto_robust, **launch) + generic_flight = Flight(rocket=generic_rocket, **launch) + + # Leaving the rail is driven by thrust and the shared drag curve, so every + # model must agree tightly here. + assert generic_flight.out_of_rail_time == pytest.approx( + reference_flight.out_of_rail_time, rel=1e-3 + ) + assert generic_flight.out_of_rail_velocity == pytest.approx( + reference_flight.out_of_rail_velocity, rel=1e-3 + ) + # Apogee reflects the whole aerodynamic ascent. + assert generic_flight.apogee == pytest.approx( + reference_flight.apogee, rel=apogee_rel_tol + ) + assert generic_flight.apogee_time == pytest.approx( + reference_flight.apogee_time, rel=apogee_rel_tol + ) + + def test_max_acceleration_power_off_time_with_controllers( flight_calisto_air_brakes, ): From c926bdd7d20ebf02572d7564884ca09f3c670589 Mon Sep 17 00:00:00 2001 From: MateusStano Date: Fri, 24 Jul 2026 17:34:13 -0300 Subject: [PATCH 10/22] DOC: creat CP rst page and review reference docs --- .pylintrc | 14 + .../stability_dispersion.errors.txt | 0 .../stability_dispersion.inputs.txt | 100 ++ .../stability_dispersion.outputs.txt | 100 ++ docs/reference/classes/Function.rst | 3 + docs/reference/classes/PointMassRocket.rst | 5 + .../ControllableGenericSurface.rst | 5 + .../reference/classes/aero_surfaces/index.rst | 3 +- .../monte_carlo/stochastic_models/index.rst | 1 + .../stochastic_air_brakes.rst | 5 + docs/reference/classes/motors/EmptyMotor.rst | 5 + .../classes/motors/PointMassMotor.rst | 5 + docs/reference/classes/motors/index.rst | 2 + docs/reference/index.rst | 1 + docs/static/rocket/aeroframe.png | Bin 40926 -> 44915 bytes docs/static/rocket/cal-per-length.png | Bin 0 -> 10274 bytes docs/static/rocket/damped-oscillation.png | Bin 0 -> 102752 bytes docs/static/rocket/stable-unstable.png | Bin 0 -> 110823 bytes .../center_of_pressure_and_stability.rst | 405 ------- .../aerodynamics/elliptical_fins.rst | 4 + .../technical/aerodynamics/roll_equations.rst | 4 + docs/technical/index.rst | 1 - .../user/center_of_pressure_and_stability.rst | 1064 +++++++++++++++++ docs/user/first_simulation.rst | 20 +- docs/user/flight.rst | 3 +- docs/user/index.rst | 3 +- docs/user/rocket/generic_surface.rst | 700 ++++++----- docs/user/rocket/rocket.rst | 3 +- 28 files changed, 1760 insertions(+), 696 deletions(-) create mode 100644 data/monte_carlo/stability_dispersion.errors.txt create mode 100644 data/monte_carlo/stability_dispersion.inputs.txt create mode 100644 data/monte_carlo/stability_dispersion.outputs.txt create mode 100644 docs/reference/classes/PointMassRocket.rst create mode 100644 docs/reference/classes/aero_surfaces/ControllableGenericSurface.rst create mode 100644 docs/reference/classes/monte_carlo/stochastic_models/stochastic_air_brakes.rst create mode 100644 docs/reference/classes/motors/EmptyMotor.rst create mode 100644 docs/reference/classes/motors/PointMassMotor.rst create mode 100644 docs/static/rocket/cal-per-length.png create mode 100644 docs/static/rocket/damped-oscillation.png create mode 100644 docs/static/rocket/stable-unstable.png delete mode 100644 docs/technical/aerodynamics/center_of_pressure_and_stability.rst create mode 100644 docs/user/center_of_pressure_and_stability.rst diff --git a/.pylintrc b/.pylintrc index 9e4fd8e89..feb85e897 100644 --- a/.pylintrc +++ b/.pylintrc @@ -233,6 +233,20 @@ good-names=FlightPhases, Re, # Reynolds number cL_alpha, cQ_beta, + cN_alpha, + cY_beta, + cL, + cD, + cQ, + cN, + cY, + cA, + cNf, + cNd, + cYf, + cYd, + cAf, + cAd, # Good variable names regexes, separated by a comma. If names match any regex, # they will always be accepted diff --git a/data/monte_carlo/stability_dispersion.errors.txt b/data/monte_carlo/stability_dispersion.errors.txt new file mode 100644 index 000000000..e69de29bb diff --git a/data/monte_carlo/stability_dispersion.inputs.txt b/data/monte_carlo/stability_dispersion.inputs.txt new file mode 100644 index 000000000..29f758544 --- /dev/null +++ b/data/monte_carlo/stability_dispersion.inputs.txt @@ -0,0 +1,100 @@ +{"elevation": 1400, "gravity": "Function from R1 to R1 : (height (m)) \u2192 (gravity (m/s\u00b2))", "latitude": 32.990254, "longitude": -106.974998, "wind_velocity_x_factor": 0.521507153174057, "wind_velocity_y_factor": 1.0, "datum": "SIRGAS2000", "timezone": null, "air_brakes": [], "parachutes": [], "radius": 0.0635, "mass": 14.822363841581048, "I_11_without_motor": 6.321, "I_22_without_motor": 6.321, "I_33_without_motor": 0.034, "I_12_without_motor": 0, "I_13_without_motor": 0, "I_23_without_motor": 0, "power_off_drag": "Function from R1 to R1 : (Mach Number) \u2192 (Drag Coefficient with Power Off)", "power_on_drag": "Function from R1 to R1 : (Mach Number) \u2192 (Drag Coefficient with Power On)", "power_off_drag_factor": 1.0, "power_on_drag_factor": 1.0, "center_of_mass_without_motor": -0.009075430781169808, "coordinate_system_orientation": "tail_to_nose", "motors": [{"thrust_source": [[0, 0], [0.055, 100.0], [0.092, 1500.0], [0.1, 2000.0], [0.15, 2200.0], [0.2, 1800.0], [0.5, 1950.0], [1.0, 2034.0], [1.5, 2000.0], [2.0, 1900.0], [2.5, 1760.0], [2.9, 1700.0], [3.0, 1650.0], [3.3, 530.0], [3.4, 350.0], [3.9, 0.0]], "total_impulse": 6351.369486474051, "burn_start_time": 0, "burn_out_time": 4.12503921194419, "dry_mass": 1.815, "dry_I_11": 0.125, "dry_I_22": 0.125, "dry_I_33": 0.002, "dry_I_12": 0, "dry_I_13": 0, "dry_I_23": 0, "nozzle_radius": 0.033, "grain_number": 5, "grain_density": 1815, "grain_outer_radius": 0.033, "grain_initial_inner_radius": 0.015, "grain_initial_height": 0.12, 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(height (m)) \u2192 (gravity (m/s\u00b2))", "latitude": 32.990254, "longitude": -106.974998, "wind_velocity_x_factor": 0.5101556242274552, "wind_velocity_y_factor": 1.0, "datum": "SIRGAS2000", "timezone": null, "air_brakes": [], "parachutes": [], "radius": 0.0635, "mass": 14.013242161800042, "I_11_without_motor": 6.321, "I_22_without_motor": 6.321, "I_33_without_motor": 0.034, "I_12_without_motor": 0, "I_13_without_motor": 0, "I_23_without_motor": 0, "power_off_drag": "Function from R1 to R1 : (Mach Number) \u2192 (Drag Coefficient with Power Off)", "power_on_drag": "Function from R1 to R1 : (Mach Number) \u2192 (Drag Coefficient with Power On)", "power_off_drag_factor": 1.0, "power_on_drag_factor": 1.0, "center_of_mass_without_motor": 0.004636885474406456, "coordinate_system_orientation": "tail_to_nose", "motors": [{"thrust_source": [[0, 0], [0.055, 100.0], [0.092, 1500.0], [0.1, 2000.0], [0.15, 2200.0], [0.2, 1800.0], [0.5, 1950.0], [1.0, 2034.0], [1.5, 2000.0], [2.0, 1900.0], [2.5, 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0.7888298933488584, "impact_velocity": 0, "index": 97, "rail_exit_aoa": 3.7407644254544694} +{"apogee": 4748.853222703051, "y_impact": 0, "out_of_rail_time": 0.4189806478219249, "apogee_x": -245.3940428582747, "apogee_time": 26.032866106266557, "frontal_surface_wind": 0.0, "apogee_y": 592.3312016696564, "out_of_rail_stability_margin": 2.2331166516236287, "t_final": 26.032866106266557, "x_impact": 0, "initial_stability_margin": 2.143500523700404, "out_of_rail_velocity": 30.284134036215082, "lateral_surface_wind": -2.812287615837798, "max_mach_number": 0.8706871062299387, "impact_velocity": 0, "index": 98, "rail_exit_aoa": 5.305464980025932} +{"apogee": 4882.446414812261, "y_impact": 0, "out_of_rail_time": 0.4025432629153109, "apogee_x": -312.0971572085366, "apogee_time": 26.3444702601522, "frontal_surface_wind": 0.0, "apogee_y": 604.6416705409873, "out_of_rail_stability_margin": 1.9729934597236711, "t_final": 26.3444702601522, "x_impact": 0, "initial_stability_margin": 1.8864112128893116, "out_of_rail_velocity": 31.51424342853964, "lateral_surface_wind": -3.7471560654077747, "max_mach_number": 0.9067071035078418, "impact_velocity": 0, "index": 99, "rail_exit_aoa": 6.780836398344449} +{"apogee": 4575.521046801354, "y_impact": 0, "out_of_rail_time": 0.4258092526029767, "apogee_x": -333.7826668054668, "apogee_time": 25.54783967052688, "frontal_surface_wind": 0.0, "apogee_y": 569.8781411643731, "out_of_rail_stability_margin": 2.290392314150821, "t_final": 25.54783967052688, "x_impact": 0, "initial_stability_margin": 2.201419586472164, "out_of_rail_velocity": 29.695445810354602, "lateral_surface_wind": -3.94310023852307, "max_mach_number": 0.8294619360772141, "impact_velocity": 0, "index": 100, "rail_exit_aoa": 7.563754664560585} diff --git a/docs/reference/classes/Function.rst b/docs/reference/classes/Function.rst index 423123945..88a1bc769 100644 --- a/docs/reference/classes/Function.rst +++ b/docs/reference/classes/Function.rst @@ -4,4 +4,7 @@ Function Classes .. seealso:: :doc:`Function Class Usage ` .. autoclass:: rocketpy.Function + :members: + +.. autoclass:: rocketpy.PiecewiseFunction :members: \ No newline at end of file diff --git a/docs/reference/classes/PointMassRocket.rst b/docs/reference/classes/PointMassRocket.rst new file mode 100644 index 000000000..f77a8ca54 --- /dev/null +++ b/docs/reference/classes/PointMassRocket.rst @@ -0,0 +1,5 @@ +PointMassRocket Class +--------------------- + +.. autoclass:: rocketpy.PointMassRocket + :members: diff --git a/docs/reference/classes/aero_surfaces/ControllableGenericSurface.rst b/docs/reference/classes/aero_surfaces/ControllableGenericSurface.rst new file mode 100644 index 000000000..94e9b7814 --- /dev/null +++ b/docs/reference/classes/aero_surfaces/ControllableGenericSurface.rst @@ -0,0 +1,5 @@ +Controllable Generic Surface Class +---------------------------------- + +.. autoclass:: rocketpy.ControllableGenericSurface + :members: diff --git a/docs/reference/classes/aero_surfaces/index.rst b/docs/reference/classes/aero_surfaces/index.rst index a3dad0417..d4b67e68a 100644 --- a/docs/reference/classes/aero_surfaces/index.rst +++ b/docs/reference/classes/aero_surfaces/index.rst @@ -19,4 +19,5 @@ AeroSurface Classes RailButtons AirBrakes GenericSurface - LinearGenericSurface \ No newline at end of file + LinearGenericSurface + ControllableGenericSurface \ No newline at end of file diff --git a/docs/reference/classes/monte_carlo/stochastic_models/index.rst b/docs/reference/classes/monte_carlo/stochastic_models/index.rst index ca8b2b1e2..028a48246 100644 --- a/docs/reference/classes/monte_carlo/stochastic_models/index.rst +++ b/docs/reference/classes/monte_carlo/stochastic_models/index.rst @@ -20,6 +20,7 @@ input parameters, enabling robust Monte Carlo simulations. stochastic_trapezoidal_fins stochastic_elliptical_fins stochastic_tail + stochastic_air_brakes stochastic_rail_buttons stochastic_rocket stochastic_parachute diff --git a/docs/reference/classes/monte_carlo/stochastic_models/stochastic_air_brakes.rst b/docs/reference/classes/monte_carlo/stochastic_models/stochastic_air_brakes.rst new file mode 100644 index 000000000..7f37852c0 --- /dev/null +++ b/docs/reference/classes/monte_carlo/stochastic_models/stochastic_air_brakes.rst @@ -0,0 +1,5 @@ +Stochastic Air Brakes +--------------------- + +.. autoclass:: rocketpy.stochastic.StochasticAirBrakes + :members: diff --git a/docs/reference/classes/motors/EmptyMotor.rst b/docs/reference/classes/motors/EmptyMotor.rst new file mode 100644 index 000000000..757c04937 --- /dev/null +++ b/docs/reference/classes/motors/EmptyMotor.rst @@ -0,0 +1,5 @@ +EmptyMotor Class +---------------- + +.. autoclass:: rocketpy.EmptyMotor + :members: diff --git a/docs/reference/classes/motors/PointMassMotor.rst b/docs/reference/classes/motors/PointMassMotor.rst new file mode 100644 index 000000000..4635ca274 --- /dev/null +++ b/docs/reference/classes/motors/PointMassMotor.rst @@ -0,0 +1,5 @@ +PointMassMotor Class +-------------------- + +.. autoclass:: rocketpy.PointMassMotor + :members: diff --git a/docs/reference/classes/motors/index.rst b/docs/reference/classes/motors/index.rst index 3bd6e2d96..99d905295 100644 --- a/docs/reference/classes/motors/index.rst +++ b/docs/reference/classes/motors/index.rst @@ -10,6 +10,8 @@ Motor Classes HybridMotor LiquidMotor GenericMotor + PointMassMotor + EmptyMotor Fluid Tank Classes Tank Geometry Classes diff --git a/docs/reference/index.rst b/docs/reference/index.rst index e86240dd9..f5503e12e 100644 --- a/docs/reference/index.rst +++ b/docs/reference/index.rst @@ -13,6 +13,7 @@ This reference manual details functions, modules, methods and attributes include AeroSurface Classes classes/Components classes/Rocket + classes/PointMassRocket classes/Parachute classes/sensors/index.rst classes/Flight diff --git a/docs/static/rocket/aeroframe.png b/docs/static/rocket/aeroframe.png index 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a/docs/technical/aerodynamics/center_of_pressure_and_stability.rst b/docs/technical/aerodynamics/center_of_pressure_and_stability.rst deleted file mode 100644 index 80e7de108..000000000 --- a/docs/technical/aerodynamics/center_of_pressure_and_stability.rst +++ /dev/null @@ -1,405 +0,0 @@ -.. _aero_cp_stability: - -========================================================== -Aerodynamics: Coefficients, Centers and Stability -========================================================== - -:Author: RocketPy Team -:Date: June 2026 - -Introduction -============ - -This document describes how RocketPy models aerodynamic forces and moments and -how the rocket's stability quantities — the **aerodynamic center**, the -**center of pressure**, the **static** and **stability margins**, and the -**dynamic-stability** parameters — are derived from them. - -The model is built as a strict set of layers, each derived only from the one -below it: - -#. **Surface coefficients** — every aerodynamic surface exposes the six - dimensionless aerodynamic coefficients. -#. **Rocket aggregate** — the surfaces are summed into the rocket's total force - and moment. -#. **Stability references** — the *aerodynamic center* (linear) and the - *center of pressure* (nonlinear). -#. **Margins** — the linear (aerodynamic-center) and realized (center-of- - pressure) stability margins. -#. **Dynamic stability** — the linearized attitude oscillator. - -Since the aerodynamic-surface refactor, :class:`rocketpy.GenericSurface` is the -**root of the aerodynamic-surface hierarchy**: nose cones, fin sets, individual -fins, tails/transitions and air brakes are all described by the same coefficient -set and computed through a single coefficient-based force-and-moment model. The -geometric (Barrowman) surfaces translate their geometry into those same -coefficients (see :ref:`barrowman_mapping`), so the rocket knows the full -aerodynamic coefficient set for every surface. - -.. note:: - The legacy ``AeroSurface`` base class is deprecated. It is retained only as a - compatibility shim: :class:`rocketpy.GenericSurface` is registered as a - virtual subclass, so ``isinstance(surface, AeroSurface)`` still returns - ``True``. - -Layer 0 — The aerodynamic coefficient model -============================================ - -A generic aerodynamic surface is defined by six dimensionless coefficients, -each a function of a set of independent variables: - -- force coefficients: lift :math:`C_L`, side force :math:`C_Q`, drag :math:`C_D`; -- moment coefficients: pitch :math:`C_m`, yaw :math:`C_n`, roll :math:`C_l`. - -The standard independent variables are the angle of attack :math:`\alpha`, the -sideslip angle :math:`\beta`, the Mach number :math:`M`, the Reynolds number -:math:`Re`, and the body angular rates (pitch :math:`q`, yaw :math:`r`, roll -:math:`p`): - -.. math:: - - C_i = C_i(\alpha,\ \beta,\ M,\ Re,\ q,\ r,\ p) - -Subclasses may append extra axes — control deflections for -:class:`rocketpy.ControllableGenericSurface`, or the unsteady terms -:math:`\dot\alpha,\ \dot\beta` when ``unsteady_aero=True``. - -Forces and moments of a surface -------------------------------- - -At each step the surface receives the freestream velocity in the body frame. -Reversing it into the standard aerodynamic frame, the incidence angles are - -.. math:: - - \alpha = \operatorname{atan2}(-v_y,\ -v_z), \qquad - \beta = \operatorname{atan2}(-v_x,\ -v_z) - -With the dynamic pressure times reference area -:math:`\bar q A = \tfrac{1}{2}\rho V^2 A_\text{ref}`, the aerodynamic force -:math:`(Q, -L, -D)` is rotated from the aerodynamic frame into the body frame, -giving :math:`\mathbf{R}=(R_1, R_2, R_3)`, and the moment about the rocket's -center of dry mass is - -.. math:: - :label: moment_transport - - \mathbf{M} = \bar q A L_\text{ref}\,(C_m, C_n, C_l) - + \mathbf{r}_\text{cp} \times \mathbf{R} - -The first term is the couple carried by the moment coefficients; the second -transports the resultant force from its application point -:math:`\mathbf{r}_\text{cp}` to the center of dry mass. This is implemented in -:meth:`rocketpy.GenericSurface.compute_forces_and_moments`. - -Layer 1 — Rocket aggregate -========================== - -The simulation, and every stability quantity below, sums the surfaces into the -rocket's total body-frame force and moment about the center of dry mass. The -nonlinear aggregate at a given state is -:meth:`rocketpy.Rocket._aerodynamic_forces_and_moments`; the dimensionless -totals are exposed by :meth:`rocketpy.Rocket.aerodynamic_coefficients` (total -normal-force coefficient :math:`C_N` and pitch-moment coefficient :math:`C_m`). -The **linear** aggregate — the normal-force-curve slope and the -slope-weighted positions — is built by -:meth:`rocketpy.Rocket.evaluate_center_of_pressure` (see Layer 2). - -Layer 2 — Aerodynamic center vs. center of pressure -=================================================== - -These two are the heart of the model and are frequently confused. They are the -same physics in two regimes. - -Aerodynamic center (linear) ---------------------------- - -The **aerodynamic center** (AC) is the *linearized*, small-incidence -(:math:`\alpha=\beta=0`) location about which the pitching moment is independent -of angle of attack: - -.. math:: - :label: ac - - x_\text{AC}(M) = x_\text{ref} - - \frac{\partial C_m/\partial\alpha}{\partial C_N/\partial\alpha}\,L_\text{ref} - -It is well-conditioned, a function of Mach alone, and is the classical reference -that the static margin is built on. At the rocket level it is the -normal-force-slope-weighted average of the component locations, - -.. math:: - :label: rocket_ac - - x_\text{AC,rocket}(M) = - \frac{\sum_i k_i\, C_{N,\alpha,i}(M)\,\big(p_i - c\, z_{\text{cp},i}(M)\big)} - {\sum_i k_i\, C_{N,\alpha,i}(M)} - -with the area-correction factor :math:`k_i = A_{\text{ref},i}/A_\text{rocket}`, -:math:`p_i` the surface position and :math:`c=\pm 1` the coordinate-system -orientation. Because the weight is the normal-force slope, a zero-lift surface -(e.g. a pure-drag element) drops out cleanly. This is computed by -:meth:`rocketpy.Rocket.evaluate_center_of_pressure` and stored as -``Rocket.aerodynamic_center``. - -.. note:: - ``Rocket.cp_position`` is an **alias** for ``Rocket.aerodynamic_center``. The - historical "center of pressure" attribute was always the aerodynamic center; - the alias is kept for backward compatibility and convenience. - -Center of pressure (nonlinear) ------------------------------- - -The **center of pressure** (CP) is the point at which the *actual* resultant -aerodynamic force acts with no residual moment, at a finite angle of -attack/sideslip: - -.. math:: - :label: cp - - x_\text{CP}(\alpha,\beta,M,Re) = - x_\text{cdm} + c\,\frac{M_2 R_1 - M_1 R_2}{R_1^2 + R_2^2} - -evaluated from the Layer-1 aggregate (:math:`M = r\times F`). Equivalently, per -plane, :math:`x_\text{CP} = x_\text{cdm} + c\,L_\text{ref}\,C_m/C_L` (pitch) and -:math:`+\,c\,L_\text{ref}\,C_n/C_Q` (yaw). Unlike the AC, the CP **moves with -incidence**. - -The CP is a **genuinely partial quantity**: it is a :math:`0/0` limit at zero -incidence (the normal force vanishes), undefined there, and converges to the AC -as :math:`\alpha,\beta \to 0`. Because it plays no role in the equations of -motion and the stability margins are correctly built on the AC (a slope, see -Layer 3), RocketPy does **not** expose it as a dedicated method — that would -force an arbitrary regularization of a real singularity. When the -force-application CP is genuinely wanted (e.g. comparing against wind-tunnel or -CFD CP-vs-:math:`\alpha` data), it is reconstructed on demand from the aggregate -coefficients :meth:`rocketpy.Rocket.aerodynamic_coefficients_full` using the -relation above, with the caller deciding how to treat the zero-incidence limit. - -Pitch and yaw planes --------------------- - -Because :class:`rocketpy.GenericSurface` allows **non-axisymmetric** rockets, the -*linear* AC is computed independently for the two planes: - -- pitch (``aerodynamic_center``) from :math:`\partial C_L/\partial\alpha` and - :math:`C_m`; -- yaw (``aerodynamic_center_yaw``) from the side-force slope and :math:`C_n`. - -They coincide for an axisymmetric rocket; ``Rocket.is_axisymmetric`` reports -whether they agree (to caliber tolerance) and -:meth:`rocketpy.Rocket.evaluate_center_of_pressure` warns when they do not, since -the scalar ``static_margin``/``stability_margin`` then describe the pitch plane -only, and the ``*_yaw`` counterparts expose the yaw plane. - -Layer 3 — Static and stability margins -====================================== - -A margin is the longitudinal center-of-mass-to-stability-reference distance in -calibers (rocket diameters). With the center of mass :math:`z_\text{cm}(t)`, the -rocket radius :math:`R` and the orientation factor :math:`c`, there are **two -co-equal families**: - -**Linear (aerodynamic-center) margins.** Built on the AC; well-conditioned and -never spiking. The conventional design parameters: - -.. math:: - :label: static_margin - - \text{static margin}(t) = c\,\frac{z_\text{cm}(t) - x_\text{AC}(0)}{2R}, - \qquad - \text{stability margin}(M, t) = c\,\frac{z_\text{cm}(t) - x_\text{AC}(M)}{2R} - -The static margin (:meth:`rocketpy.Rocket.evaluate_static_margin`) is the -incompressible (:math:`M=0`) limit, a function of time; the stability margin -(:meth:`rocketpy.Rocket.evaluate_stability_margin`) is a function of Mach and -time. The ``*_yaw`` counterparts use ``aerodynamic_center_yaw``. - -At the :class:`rocketpy.Flight` level, ``Flight.stability_margin`` (and -``stability_margin_yaw``) evaluates the linear margin along the realized Mach and -time — smooth, conventional, and the source of ``initial_stability_margin`` / -``out_of_rail_stability_margin`` / ``min_stability_margin`` / -``max_stability_margin``. - -A positive margin (stability reference behind the center of mass) is the classic -passive-stability condition. - -.. note:: - **Nonlinear (large-incidence) static stability.** For tabulated - :class:`rocketpy.GenericSurface` coefficients that are nonlinear in - :math:`\alpha`, the stability reference -- the *local neutral point*, the AC - re-linearized at the flown incidence -- migrates with angle of attack, - - .. math:: - - x_\text{NP}(\alpha,\beta,M) = x_\text{cdm} - + c\,L_\text{ref}\,\frac{\partial C_m/\partial\alpha} - {\partial C_L/\partial\alpha}, - - which (unlike the singular force-application CP :math:`-C_m/C_N`) is well - conditioned at every incidence and isolates the stability-relevant part of - the CP travel. It is reconstructed on demand from - :meth:`rocketpy.Rocket.aerodynamic_coefficients_full` by a central finite - difference in :math:`\alpha`. For linear Barrowman aerodynamics it reduces to - the :math:`\alpha=0` AC, so the linear margin already captures it; only - nonlinear tabulated coefficients make it move. Large-incidence stability is - usually read more meaningfully from the dynamic-stability coefficients - (Layer 4). - -Layer 4 — Dynamic stability -=========================== - -A static margin only gives the *sign* of the restoring moment. The actual -attitude response is the linearized pitch (or yaw) oscillator - -.. math:: - - I_L\,\ddot\theta + C_2\,\dot\theta + C_1\,\theta = 0 - -with the **corrective moment coefficient** (restoring moment per radian), - -.. math:: - - C_1 = \bar q\, A_\text{ref}\, C_{N,\alpha}\, (z_\text{cm} - x_\text{AC}), - -the **damping moment coefficient** (aerodynamic plus jet damping), - -.. math:: - - C_2 = \tfrac{1}{2}\rho V A_\text{ref} \sum_i k_i\,C_{N,\alpha,i}\,(x_i - z_\text{cm})^2 - \;+\; \dot m\,(x_\text{nozzle} - z_\text{cm})^2, - -and the lateral moment of inertia about the instantaneous center of mass -:math:`I_L`. From these, - -.. math:: - - \omega_n = \sqrt{C_1/I_L}, \qquad \zeta = \frac{C_2}{2\sqrt{C_1\,I_L}}. - -These are exposed on :class:`rocketpy.Flight` as -``corrective_moment_coefficient``, ``damping_moment_coefficient``, -``pitch_natural_frequency``, ``pitch_damping_ratio`` and the ``yaw_*`` -counterparts. :math:`\zeta < 1` is an underdamped (oscillatory) response; -RocketPy also exposes the empirical FFT ``attitude_frequency_response`` as a -cross-check. - -.. note:: - **Roll has no natural frequency.** A conventional rocket has no aerodynamic - roll-restoring moment, so roll is *neutrally stable* (a first-order system: - fin-cant forcing balanced by roll damping, spinning up to a steady rate). - The roll-pitch/yaw coupling of concern is **roll resonance** ("roll - lock-in"): when the roll rate crosses the pitch/yaw natural frequency, the - spin couples into the attitude oscillation and the amplitude can diverge. - ``Flight.plots.dynamic_stability_data`` therefore overlays the roll rate (as - a frequency) on the natural-frequency plot — the crossings are the points to - watch. - -Quick reference -=============== - -.. list-table:: - :header-rows: 1 - :widths: 32 18 50 - - * - Attribute - - Variables - - Meaning - * - ``Rocket.aerodynamic_center`` (``_yaw``) - - :math:`M` - - Linear (small-incidence) center of pressure; static-margin reference. - ``cp_position`` is an alias. - * - ``Rocket.aerodynamic_coefficients(α, β, M, Re)`` - - :math:`\alpha,\beta,M,Re` - - Total :math:`C_N`, :math:`C_m` about the center of dry mass. - * - ``Rocket.aerodynamic_coefficients_full(α, β, M, Re)`` - - :math:`\alpha,\beta,M,Re` - - Six signed coefficients; reconstruct the nonlinear CP as - :math:`x_\text{cdm} + c\,L_\text{ref}\,C_m/C_L`. - * - ``Rocket.static_margin`` (``_yaw``) - - :math:`t` - - Linear margin at :math:`M=0` (calibers). - * - ``Rocket.stability_margin`` (``_yaw``) - - :math:`M, t` - - Linear margin vs Mach and time (calibers). - * - ``Flight.stability_margin`` (``_yaw``) - - :math:`t` - - Linear margin along the realized Mach(t) — smooth. - * - ``Flight.{pitch,yaw}_natural_frequency`` - - :math:`t` - - Attitude oscillation natural frequency :math:`\omega_n`. - * - ``Flight.{pitch,yaw}_damping_ratio`` - - :math:`t` - - Attitude oscillation damping ratio :math:`\zeta`. - * - ``Flight.{corrective,damping}_moment_coefficient`` - - :math:`t` - - Oscillator coefficients :math:`C_1`, :math:`C_2`. - -Visualizing stability -===================== - -- ``Rocket.plots.stability_margin`` — linear margin vs Mach and time (surface). -- ``Rocket.plots.aerodynamic_coefficients`` — :math:`C_N`, :math:`C_m` vs - :math:`\alpha`; ``drag_curves`` for :math:`C_D` vs Mach. -- ``Flight.plots.stability_and_control_data`` — linear margin (pitch and yaw) vs - time, plus the FFT frequency response. -- ``Flight.plots.dynamic_stability_data`` — natural frequency and damping ratio - vs time (pitch and yaw). - -For non-axisymmetric rockets, ``Rocket.plots.all`` / ``Rocket.all_info`` also -draw both the pitch (``xz``) and yaw (``yz``) planes and the yaw-plane margins. - -.. _barrowman_mapping: - -Mapping Barrowman surfaces to coefficients -========================================== - -The geometric surfaces expose a lift-curve slope :math:`C_{N,\alpha}(M)` -(``clalpha``), a geometric cp :math:`z_\text{cp}` and — for fins — roll -forcing/damping. These are translated into the linear coefficient model: - -.. math:: - - C_{L,\alpha} = C_{N,\alpha}, \qquad - C_{Q,\beta} = -C_{N,\alpha} - -.. math:: - - C_{m,\alpha} = -C_{N,\alpha}\,\frac{z_\text{cp}}{L_\text{ref}}, \qquad - C_{n,\beta} = +C_{N,\alpha}\,\frac{z_\text{cp}}{L_\text{ref}} - -For an **individual fin** at angular position :math:`\phi`, the lift only resists -incidence in its own plane, so its slope is projected onto the two planes — -:math:`\sin^2\phi` to the pitch plane and :math:`\cos^2\phi` to the yaw plane. -An evenly spaced set of :math:`n` fins sums to :math:`n/2` in each plane, -reproducing the axisymmetric fin-set result; a one-plane layout (e.g. canards at -:math:`0^\circ/180^\circ`) makes the pitch- and yaw-plane aerodynamic centers -differ. - -For fin sets, the cant-angle roll forcing and roll damping add - -.. math:: - - C_{l,0} = C_{lf,\delta}(M)\,\delta, \qquad - C_{l,p} = C_{ld,\omega}(M) - -where :math:`\delta` is the cant angle. With this mapping the geometric surfaces -reproduce the Barrowman lift and roll behavior while flowing through the same -generic coefficient path as every other surface. - -.. note:: - The independent :math:`\alpha,\ \beta` decomposition of the linear model - coincides with the classical single-plane Barrowman projection to first - order and diverges only at large combined angle of attack, where the - underlying linear coefficients are themselves no longer valid; that regime is - captured by tabulated :class:`rocketpy.GenericSurface` coefficients, from - which the local neutral point and the nonlinear CP can be reconstructed via - :meth:`rocketpy.Rocket.aerodynamic_coefficients_full`. - -References -========== - -The Barrowman method and its coefficients are described in [Barrowman]_ and -[Niskanen]_. The dynamic-stability oscillator (corrective and damping moment -coefficients, natural frequency and damping ratio) follows [Niskanen]_. See also -the :ref:`individual_fins` and roll-moment technical documents for the fin -derivations. diff --git a/docs/technical/aerodynamics/elliptical_fins.rst b/docs/technical/aerodynamics/elliptical_fins.rst index 3b1cb113d..d856c60d3 100644 --- a/docs/technical/aerodynamics/elliptical_fins.rst +++ b/docs/technical/aerodynamics/elliptical_fins.rst @@ -1,3 +1,7 @@ +========================= +Elliptical Fins Equations +========================= + Nomenclature ============ diff --git a/docs/technical/aerodynamics/roll_equations.rst b/docs/technical/aerodynamics/roll_equations.rst index b35f1de68..394d6ea02 100644 --- a/docs/technical/aerodynamics/roll_equations.rst +++ b/docs/technical/aerodynamics/roll_equations.rst @@ -1,3 +1,7 @@ +======================================= +Roll equations for high-powered rockets +======================================= + Nomenclature ============ diff --git a/docs/technical/index.rst b/docs/technical/index.rst index 050c366e0..bb49ac56c 100644 --- a/docs/technical/index.rst +++ b/docs/technical/index.rst @@ -12,7 +12,6 @@ in their code. Equations of Motion v0 Equations of Motion v1 - Aerodynamics, Center of Pressure and Margins Elliptical Fins Individual Fin Roll Moment diff --git a/docs/user/center_of_pressure_and_stability.rst b/docs/user/center_of_pressure_and_stability.rst new file mode 100644 index 000000000..69962b581 --- /dev/null +++ b/docs/user/center_of_pressure_and_stability.rst @@ -0,0 +1,1064 @@ +.. _aero_cp_stability: + +================================ +Center of Pressure and Stability +================================ + +Introduction +============ + +Stability is a central consideration in rocket design. Two rules of thumb are +widely repeated in amateur and student rocketry: the center of pressure must be +behind the center of gravity, and a static margin of one to two calibers is +generally desirable. This document explains the reasoning behind those rules +and distinguishes between three related but distinct quantities: **static +margin**, **stability margin**, and **dynamic stability**. + +The presentation proceeds from physical principles to the exact definitions and +formulas used by RocketPy, illustrated throughout with the **Calisto** reference +rocket, introduced in the :ref:`firstsimulation` guide. + +.. contents:: On this page + :local: + :depth: 2 + +.. jupyter-execute:: + :hide-code: + :hide-output: + + # The Calisto reference rocket from the First Simulation guide, reused for + # every worked example below. See the firstsimulation guide for the full + # build. IMPORTANT: adjust the data paths below to match your own system. + from rocketpy import Environment, SolidMotor, Rocket, Flight + + env = Environment(latitude=32.990254, longitude=-106.974998, elevation=1400) + env.set_atmospheric_model(type="standard_atmosphere") + + Pro75M1670 = SolidMotor( + thrust_source="../data/motors/cesaroni/Cesaroni_M1670.eng", + dry_mass=1.815, + dry_inertia=(0.125, 0.125, 0.002), + nozzle_radius=33 / 1000, + grain_number=5, + grain_density=1815, + grain_outer_radius=33 / 1000, + grain_initial_inner_radius=15 / 1000, + grain_initial_height=120 / 1000, + grain_separation=5 / 1000, + grains_center_of_mass_position=0.397, + center_of_dry_mass_position=0.317, + nozzle_position=0, + burn_time=3.9, + throat_radius=11 / 1000, + coordinate_system_orientation="nozzle_to_combustion_chamber", + ) + + rocket = Rocket( + radius=127 / 2000, + mass=14.426, + inertia=(6.321, 6.321, 0.034), + power_off_drag="../data/rockets/calisto/powerOffDragCurve.csv", + power_on_drag="../data/rockets/calisto/powerOnDragCurve.csv", + center_of_mass_without_motor=0, + coordinate_system_orientation="tail_to_nose", + ) + rocket.add_motor(Pro75M1670, position=-1.255) + rocket.set_rail_buttons( + upper_button_position=0.0818, + lower_button_position=-0.618, + angular_position=45, + ) + rocket.add_nose(length=0.55829, kind="vonKarman", position=1.278) + rocket.add_trapezoidal_fins( + n=4, + root_chord=0.120, + tip_chord=0.060, + span=0.110, + cant_angle=0.0, + position=-1.04956, + airfoil=("../data/airfoils/NACA0012-radians.txt", "radians"), + ) + rocket.add_tail( + top_radius=0.0635, bottom_radius=0.0435, length=0.060, position=-1.194656 + ) + + test_flight = Flight( + rocket=rocket, environment=env, rail_length=5.2, inclination=85, heading=0 + ) + + +Part 1: Physical principles +============================ + +Static stability +----------------- + +.. admonition:: Static stability + :class: note + + A rocket is **statically stable** if, when a disturbance pushes its nose + away from the flight direction, the aerodynamic forces generate a + restoring moment that returns it toward that direction. + + An **unstable** rocket exhibits the opposite behavior: a disturbance is + never corrected, and the rocket tumbles. + +Whether a restoring moment exists depends on the relative position of two +points along the rocket's axis: the **center of mass** and the **center of +pressure**: + +- **Center of mass (CM), also called center of gravity (CG).** The point about + which the rocket's mass is balanced, and about which the rocket rotates in + flight. In RocketPy this quantity is ``Rocket.center_of_mass``, a function of + time since propellant consumption shifts it. + +- **Center of pressure (CP).** The point at which the net aerodynamic force can + be considered to act. At a small angle to the airflow, a sideways ("normal") + force is generated, and the CP is its effective point of application. In + RocketPy this quantity is ``Rocket.cp_position``, an alias of + ``Rocket.aerodynamic_center``. + +The relative position of these two points determines stability: + +.. admonition:: The stability rule + :class: important + + If the **center of pressure is located behind the center of mass** (toward + the tail), the rocket is stable. + + If the center of pressure is located ahead of the center of mass the rocket + is unstable. + +.. figure:: ../../static/rocket/stable-unstable.png + :align: center + :width: 80% + + The aerodynamic force acting at the CP creates a torque about the CM. + When the CP is aft of the CM the torque is restoring (left); when the CP + is forward of the CM the torque grows the disturbance (right). + +.. Role of the fins +.. ----------------- + +.. Fins are the primary means of shifting the CP toward the tail. As large +.. lifting surfaces positioned far aft, they move the rocket's aggregate center +.. of pressure behind the center of mass, establishing the restoring lever arm. +.. Nose cones and outward-flaring transitions tend to move the CP forward and are +.. destabilizing in isolation; the fins must more than compensate for this +.. effect. + +.. Increasing fin size, moving the fins aft, or adding mass to the nose (which +.. moves the CM forward) are the standard corrective measures for an unstable +.. design, since each increases the distance by which the CP trails the CM. + + +Part 2: Static margin +===================== + +Definition +---------- + +.. admonition:: Static margin + :class: note + + The **static margin** is the distance between the **CM** and the **CP**, + expressed in **calibers**, where one caliber equals the body diameter. + + .. math:: + :label: static_margin_intro + + \text{static margin} = + \frac{(\text{CP position}) - (\text{CM position})}{\text{diameter}} + \;\;[\text{calibers}] + + - A **positive** static margin indicates that the CP is behind the CM, and + therefore that the rocket is stable. A margin of two calibers indicates + that the two points are separated by two body diameters. + - A **negative** static margin indicates that the CP is ahead of the CM, and + therefore that the rocket is unstable. + + The diameter is used as the reference length because it is the natural + length scale of the aerodynamics: the normal force generated by a body + scales with its cross-sectional area. This convention is widely used in + rocketry. + +Here is a plot of the static margin of the Calisto rocket over time. The +variation of the static margin here is due to the forward shift of the center of +mass as the motor burns and propellant is consumed. The right-hand axis +expresses the same margin as a percentage of body length (discussed in +:ref:`percent_of_length`): + +.. jupyter-execute:: + + rocket.plots.static_margin() + +Recommended margin range +------------------------ + +The following ranges are widely used design rules of thumb, not precise or +authoritative thresholds: + +- **Below approximately 1 caliber:** marginal. Manufacturing tolerances, added + nose-cone mass, or a shifted CG can be enough to make the rocket unstable. +- **Approximately 1 to 2 calibers:** the standard target range for most + rockets, and the most common recommendation in hobby and student rocketry. +- **Approximately 2 to 3 calibers:** another common target range for high-power + rockets, providing extra margin for the CG shift that occurs as the motor + burns. +- **Above approximately 4 calibers (over-stable):** also undesirable, for the + reasons described below. + +**These figures are rules of thumb, not objective truths**: the transition +between ranges is gradual and depends on the specific rocket, so a margin +just outside one of them does not mean the rocket will not fly safely. Falling +within a target range also does not by itself guarantee good flight behavior. + +.. admonition:: Excessive static margin + :class: warning + + An over-stable rocket has a strong restoring moment, so in a crosswind it + turns into the wind and drifts further downwind ("weathercocking"). Aim for + enough margin to keep the rocket reliably stable, rather than the largest + margin achievable. The flight studies in :ref:`stability_in_flight` examine + how much this actually affects a real flight, and the + :ref:`practical studies ` show that the altitude usually + blamed on over-stability is really the cost of the added nose weight. + +.. _percent_of_length: + +Margin as a percentage of length +-------------------------------- + +An alternative convention expresses the margin as a **percentage of the +rocket's overall length** rather than in calibers: + +.. math:: + + \text{margin (\% of length)} = + \frac{(\text{CP position}) - (\text{CM position})}{\text{body length}} + \times 100 + +.. figure:: ../../static/rocket/cal-per-length.png + :align: center + :width: 80% + + The same CM-to-CP distance expressed against two reference lengths: the + body diameter (one caliber) and the overall body length. Both numbers + describe the identical physical gap. + +This measures the identical physical distance as the caliber margin, +normalized by the total length instead of the diameter, as illustrated +above. RocketPy exposes the overall length as +:attr:`rocketpy.Rocket.length`, defined as the axial span from the nose tip +to the aft-most point of the rocket, whether that is an aerodynamic surface +or the motor nozzle if it extends further aft. + +For Calisto (from :ref:`firstsimulation`), with a length of **2.53 m** and a +fineness ratio of approximately 20, the two conventions yield: + +.. list-table:: + :header-rows: 1 + :widths: 40 30 30 + + * - Condition + - Calibers + - % of length + * - Lift-off (``t = 0``) + - ``2.20 c`` + - ``11.0 %`` + * - Burnout (``t = 3.9 s``) + - ``3.11 c`` + - ``15.6 %`` + +As a rule of thumb, a percent-of-length margin of roughly **8 to 15%** is a +commonly cited target, with the lower bound the firmer of the two. For a +typical rocket with a fineness ratio near 10, one caliber is about 10% of the +length, so this range corresponds to the familiar 1 to 2 calibers. The two +conventions only diverge for unusually short or slender rockets, which is +precisely where the percentage form is meant to help. Like the caliber ranges, +these are **informal guidelines rather than authoritative thresholds**. + +.. admonition:: Why express margin as a percent of length? + :class: note + + The percent-of-length convention fixes a **gap in the caliber measure**: the + caliber says nothing about the rocket's overall length. Two rockets with + a static margin of 2 calibers, one short and one long and slender, don't + necessarily behave the same in flight. + + The slender rocket has + **substantially greater** rotational inertia (which scales with length + squared) and a longer aerodynamic damping arm. Expressing the margin as + a fraction of length scales the required + physical margin with rocket size. This is a rough correction for + slenderness. + + +.. _stability_margin_part: + +Part 3: Stability margin +======================== + +The **stability margin** is the same center-of-mass-to-center-of-pressure +distance, in the same calibers, but with the center of pressure taken at the +rocket's **actual flight condition**, its Mach number and angle of attack, +rather than at rest. + +Writing :math:`z_\text{cm}(t)` for the center of mass, :math:`2R` for the body +diameter (one caliber), the two margins have the same form and differ only in +the center-of-pressure reference they subtract: + +.. math:: + + \text{static margin}(t) = c\,\frac{z_\text{cm}(t) - x_\text{AC}(0)}{2R}, + \qquad + \text{stability margin}(\alpha, M, t) + = c\,\frac{z_\text{cm}(t) - x_\text{NP}(\alpha, M)}{2R}. + +- The static margin uses the **aerodynamic center** :math:`x_\text{AC}(0)`: the + center of pressure linearized about zero angle of attack and evaluated at zero + airspeed (:math:`M = 0`). It is a fixed reference, so the static margin varies + only through the center of mass, that is, with time. +- The stability margin uses the **neutral point** :math:`x_\text{NP}(\alpha, M)`, + the local center of pressure at the actual Mach number and angle of attack. + Because that reference moves with the flow, the stability margin depends on + angle of attack and Mach number as well as time. + +The static margin and the stability margin are the same underlying +quantity, evaluated under different +conditions: + +.. list-table:: + :header-rows: 1 + :widths: 24 24 26 + + * - + - **Static margin** + - **Stability margin** + * - Depends on + - Time only + - Angle of attack, Mach number **and** time + * - Evaluated at + - Zero incidence, zero airspeed (``M = 0``) + - *Any* angle of attack and Mach number (an aerodynamic map) + * - Answers + - Stability at rest + - Stability at any chosen flow state + * - RocketPy + - ``Rocket.static_margin`` (function of ``t``) + - ``Rocket.stability_margin`` (function of ``alpha, M, t``) + +The margin varies for up to three independent reasons: + +**1. Center-of-mass displacement (time).** As the motor consumes propellant the +CM moves. This changes the CM-to-CP distance. + +**2. Center-of-pressure displacement (Mach number).** Aerodynamic surfaces +effectivenss (e.g. fins) varies with speed, causing the CP +to shift with Mach number. + +**3. Center-of-pressure displacement (angle of attack).** For a rocket +carrying a surface that generates lift nonlinearly with incidence, the center +of pressure also migrates with the angle of attack. RocketPy's pre-set +geometric surfaces (``NoseCone``, ``TrapezoidalFins``, +``EllipticalFins``, ``FreeFormFins``, ``Tail``) are all linear in +incidence, so a rocket built only from them never sees this effect. It shows up +when a surface is added as a :class:`rocketpy.GenericSurface` with a nonlinear +coefficient, for example a Galejs body-lift term. + +The flight stability margin +---------------------------- + +``Flight.stability_margin`` samples the rocket's ``stability_margin`` map at +the angle of attack, Mach number and time realized during the simulated flight: + +.. jupyter-execute:: + + test_flight.prints.stability_margin() + +``Flight.plots.stability_margin_data()`` plots this curve for the pitch and yaw +planes, with a percent-of-length axis on the right: + +.. jupyter-execute:: + + test_flight.plots.stability_margin_data() + +.. tip:: + + The **out-of-rail stability margin** is frequently the most significant + single value: it is the margin at the instant of rail departure, when the + rocket is slowest and most susceptible to wind disturbance. A recommended + design practice is to ensure this value is suficcient. The out-of-rail + instant is examined in detail in :ref:`stability_in_flight`. + + +.. _part_dynamic: + +Part 4: Dynamic stability +========================= + +The static margin has a fundamental limitation: it indicates only the *sign* +of the restoring moment, not the rocket's actual dynamic behavior. A positive +margin guarantees the existence of a restoring tendency but provides no +information regarding the rate of return, the presence of overshoot, or +whether resulting oscillations decay or persist. These characteristics are +described by **dynamic stability**. + +.. jupyter-execute:: + :hide-code: + :hide-output: + + import numpy as np + import matplotlib.pyplot as plt + + def build_rocket(center_of_mass_without_motor=0.0, lateral_inertia=6.321, + mass=14.426, motor=Pro75M1670): + """Calisto with four adjustable parameters. Shifting the dry center of + mass toward the nose (positive) raises the static margin; toward the tail + (negative) lowers it. The lateral moment of inertia is adjustable + separately, which changes the dynamic response without changing the + static margin. The dry mass (in kilograms) and the motor can also be + swapped, for the mass and thrust studies later in this document. + Everything else, including the aerodynamics, is fixed. + """ + rocket = Rocket( + radius=127 / 2000, + mass=mass, + inertia=(lateral_inertia, lateral_inertia, 0.034), + power_off_drag="../data/rockets/calisto/powerOffDragCurve.csv", + power_on_drag="../data/rockets/calisto/powerOnDragCurve.csv", + center_of_mass_without_motor=center_of_mass_without_motor, + coordinate_system_orientation="tail_to_nose", + ) + rocket.add_motor(motor, position=-1.255) + rocket.add_nose(length=0.55829, kind="vonKarman", position=1.278) + rocket.add_trapezoidal_fins( + n=4, root_chord=0.120, tip_chord=0.060, span=0.110, + cant_angle=0.0, position=-1.04956, + airfoil=("../data/airfoils/NACA0012-radians.txt", "radians"), + ) + rocket.add_tail( + top_radius=0.0635, bottom_radius=0.0435, length=0.060, position=-1.194656 + ) + return rocket + + def windy_site(wind_speed): + """A standard atmosphere with a constant eastward crosswind, in m/s.""" + site = Environment(latitude=32.990254, longitude=-106.974998, elevation=1400) + site.set_atmospheric_model( + type="custom_atmosphere", wind_u=wind_speed, wind_v=0 + ) + return site + +The attitude oscillator +----------------------- + +A stable rocket disturbed by angle :math:`\theta` behaves as a damped +spring-mass oscillator: + +.. math:: + + I_L\,\ddot\theta + C_2\,\dot\theta + C_1\,\theta = 0 + +with three governing parameters: + +- :math:`C_1`, the **corrective (restoring) moment coefficient**, analogous + to a spring constant. It is proportional to the static margin: + :math:`C_1 = \bar q\, A\, C_{N,\alpha}\, (z_\text{cm} - x_\text{cp})`, where + :math:`\bar q` is the dynamic pressure. A larger margin or higher airspeed + produces a stiffer restoring moment. +- :math:`C_2`, the **damping moment coefficient**, analogous to a damping + coefficient. It arises from aerodynamic resistance of the fins to rotation, + together with **jet damping** resulting from mass ejection through the + nozzle. +- :math:`I_L`, the **lateral moment of inertia** about the center of mass, + representing the rotational inertia opposing angular acceleration. + +These parameters determine the two quantities that characterize the dynamic +response: + +.. math:: + + \omega_n = \sqrt{\frac{C_1}{I_L}} + \quad(\text{natural frequency}), + \qquad + \zeta = \frac{C_2}{2\sqrt{C_1\,I_L}} + \quad(\text{damping ratio}). + +- The **natural frequency** :math:`\omega_n` sets the oscillation rate, in + rad/s (divide by :math:`2\pi` for Hz), and increases with static margin and + airspeed. +- The **damping ratio** :math:`\zeta` sets how quickly the oscillation + decays. Rockets are normally *underdamped* (:math:`\zeta < 1`), oscillating + with the amplitude shrinking over several cycles. + +.. figure:: ../../static/rocket/damped-oscillation.png + :align: center + :width: 90% + + Attitude response to a single disturbance. The spacing between peaks is set + by the natural frequency; the rate at which the peaks shrink toward zero, the + dashed envelope, is set by the damping ratio. A larger damping ratio decays + faster for the same natural frequency. + +RocketPy exposes each of these quantities on the ``Flight`` object: +``corrective_moment_coefficient`` (:math:`C_1`), +``damping_moment_coefficient`` (:math:`C_2`), +``pitch_natural_frequency``, ``pitch_damping_ratio``, and the corresponding +``yaw_*`` quantities. ``Flight.prints.dynamic_stability()`` summarizes them at +the key ascent instants, rail departure and burnout, together with the roll +rate at burnout: + +.. jupyter-execute:: + + test_flight.prints.dynamic_stability() + +``Flight.plots.dynamic_stability_data()`` plots natural frequency and damping +ratio as functions of time, with roll rate overlaid. + +.. jupyter-execute:: + + test_flight.plots.dynamic_stability_data() + +.. _stability_in_flight: + +Part 5: Stability in a real flight +================================== + +Parts 1 to 4 defined each stability quantity on a single nominal flight. This +part watches those quantities at work: the wind disturbance at rail exit, the +margin and damping correcting it, and how stable the rocket stays across the +spread of conditions a real launch day brings. + +.. admonition:: The out-of-rail instant + :class: note + + Rail departure is the most critical moment of a rocket's flight. The rail + buttons hold its orientation until the last one clears the rail. From then + on, aerodynamics takes over, right as the rocket is at its slowest and + least able to resist a disturbance. + +Stall is not modeled +-------------------- + +**RocketPy does not model stall.** On a real fin, lift grows with angle of +attack only up to a point. Past roughly 10 to 15 degrees the flow +**separates** from the surface, the lift collapses, and the drag climbs +sharply: the fin *stalls*. A stalled fin **stops correcting**, and the rocket +can tumble out of controlled flight. + +RocketPy's Barrowman fins never do this. A fin takes its lift *slope* at zero +angle of attack and applies that **same slope at every angle**, so its normal +force keeps growing without limit: + +.. jupyter-execute:: + :hide-code: + + # Left: the measured NACA 0012 lift curve (the file stores angle in radians). + # Right: the RocketPy fin built from that same airfoil. RocketPy keeps only + # the curve's slope at 0 degrees, so the fin's coefficient never stalls. + airfoil = np.loadtxt("../data/airfoils/NACA0012-radians.txt", delimiter=",") + aoa_deg, cl = np.degrees(airfoil[:, 0]), airfoil[:, 1] + + fin = build_rocket().fins[0] + clalpha = fin.clalpha(0.1) # normal-force slope at low Mach + aoa = np.linspace(0, 15, 200) + cn_fin = clalpha * np.radians(aoa) + peak = np.argmax(cl[aoa_deg <= 20]) # airfoil peak, just before stall + + fig, (axL, axR) = plt.subplots(1, 2, figsize=(10, 3.8)) + + shown = aoa_deg <= 15 + axL.plot(aoa_deg[shown], cl[shown], "-o", color="#c0392b", lw=2, ms=4) + axL.annotate("stall", xy=(aoa_deg[peak], cl[peak]), + xytext=(aoa_deg[peak] + 1.5, cl[peak] + 0.03), + fontsize=11, fontweight="bold", color="#c0392b", + arrowprops=dict(arrowstyle="->", color="#c0392b")) + axL.set_title("Real airfoil (NACA 0012 data)", fontweight="bold") + axL.set_ylabel(r"lift coefficient $C_L$") + + axR.plot(aoa, cn_fin, color="#2980b9", lw=2.4) + axR.text(0.05, 0.9, "linear: never stalls", transform=axR.transAxes, + fontsize=11, fontweight="bold", color="#2980b9") + axR.set_title("RocketPy fin (Barrowman)", fontweight="bold") + axR.set_ylabel(r"normal-force coefficient $C_N$") + + for ax in (axL, axR): + ax.set_xlabel("angle of attack (deg)") + ax.set_xlim(0, 15) + ax.set_ylim(0, 0.9) + ax.grid(alpha=0.25) + ax.spines[["top", "right"]].set_visible(False) + fig.tight_layout() + plt.show() + +The airfoil on the left carries the whole story of the flow: lift rises, +**peaks near 9 degrees, then falls off a cliff** as the flow separates. The +RocketPy fin on the right, built from that very airfoil, **keeps the same +initial slope forever**. + +So **a high computed rail-exit angle of attack marks a failure, not a +survivable condition.** The simulation shows the rocket swinging back into +line even past the angle where a real fin would have stalled. Keep the +out-of-rail velocity high relative to the wind and the rocket stays below the +stall range; the recovery the simulation shows past it **would not happen in +reality**. In the sweeps below, read a large computed angle of attack as a +warning sign, not a number the simulation can be trusted to reproduce. + +To actually simulate stall, or any other measured nonlinear aerodynamics, +provide the coefficients directly with a generic surface (see +:ref:`genericsurfaces`). + +The angle of attack at rail exit +-------------------------------- + +At rail departure the body points along the rail. But the air it meets is the +vector sum of the rocket's own velocity and the wind. That gives an angle of +attack of approximately + +.. math:: + + \alpha_\text{exit} \approx \arctan\!\left(\frac{V_\text{wind}}{V_\text{exit}}\right), + +where :math:`V_\text{wind}` is the crosswind and :math:`V_\text{exit}` is the +out-of-rail velocity. Only their *ratio* matters: a stronger wind and a +slower exit velocity push the angle up the same way. The flight below, the +nominal Calisto in an 8 m/s crosswind, checks the estimate against a real +simulation: + +.. jupyter-execute:: + + import numpy as np + + windy_flight = Flight( + rocket=build_rocket(), environment=windy_site(5), + rail_length=5.2, inclination=85, heading=0, terminate_on_apogee=True, + ) + + v_exit = windy_flight.out_of_rail_velocity + aoa_exit = windy_flight.angle_of_attack(windy_flight.out_of_rail_time) + estimate = np.degrees(np.arctan(5 / v_exit)) + + print(f"out-of-rail velocity : {v_exit:5.1f} m/s") + print(f"rail-exit AoA : {aoa_exit:5.2f} deg (simulated)") + print(f"arctan(wind / V_exit): {estimate:5.2f} deg (geometric estimate)") + +The simulated angle tracks the arctangent estimate closely. This is the +disturbance set the instant the rail lets go, and the rest of the flight has +to correct it. Keeping it well below the stall range of the fins is the +first stability requirement of any launch. + +The disturbance, corrected +-------------------------- + +The damped oscillation from :ref:`part_dynamic` can be represented in a flight +simulation. Tracking the angle of +attack after rail exit shows the rocket swinging back toward the relative +wind, and the swing dying away: + +.. jupyter-execute:: + + t0 = windy_flight.out_of_rail_time + t = np.linspace(t0, t0 + 4, 400) + aoa = [windy_flight.angle_of_attack(ti) for ti in t] + + fig, ax = plt.subplots(figsize=(8, 4)) + ax.plot(t, aoa) + ax.axvline(t0, color="0.6", ls="--", lw=1, label="rail exit") + ax.set_xlabel("Time (s)") + ax.set_ylabel("Angle of attack (deg)") + ax.set_title("Response to the rail-exit disturbance (8 m/s crosswind)") + ax.legend() + ax.grid(True) + plt.show() + +Every stability quantity from earlier parts shows up here. The rocket returns +toward zero at all because its **stability margin** +(:ref:`stability_margin_part`) is positive. The center of pressure sits +behind the center of mass, so the aerodynamic force restores rather than +diverges. The *rate* of the wobble is the **natural frequency**. The *speed* +it settles at is the **damping ratio** (:ref:`part_dynamic`). +``windy_flight.prints.dynamic_stability()`` reports both for this flight. A +positive margin only guarantees the curve trends back to zero. It says +nothing about how fast or how smoothly, which is exactly the distinction +Part 4 draws. Here the angle of attack swings through several cycles before it +settles, a sign that this rocket is only lightly damped. It recovers either +way, but for a cleaner flight, one that settles after an overshoot or two, it +could do with more damping. + +Stability across a launch day +----------------------------- + +**A single nominal flight is not what a launch actually delivers.** The wind +shifts from minute to minute. The finished mass differs from the design +value. The center of mass is never exactly where the drawing puts it. That +spread of conditions moves two quantities that matter at rail exit: the +stability margin and the angle of attack. + +RocketPy's Monte Carlo tooling measures exactly that. The ``Stochastic*`` +classes wrap the nominal environment, rocket, motor and flight, and attach a +spread to each uncertain input. :class:`rocketpy.MonteCarlo` then runs the +flight many times, each with a fresh draw, and saves the results. Here the +balance (dry mass and center of mass), the motor's total impulse and burn +time, and the crosswind are dispersed: + +.. code-block:: python + + from rocketpy import MonteCarlo, NoseCone, TrapezoidalFins, Tail + from rocketpy.stochastic import ( + StochasticEnvironment, + StochasticRocket, + StochasticSolidMotor, + StochasticFlight, + StochasticNoseCone, + StochasticTrapezoidalFins, + StochasticTail, + ) + + # Environment: 3 m/s mean crosswind, scaled by a normal factor so the wind + # spans roughly 3 +/- 2.5 m/s from flight to flight. + stochastic_env = StochasticEnvironment( + environment=windy_site(3), + wind_velocity_x_factor=(1.0, 0.42, "normal"), + ) + + # Motor: total impulse and burn time vary together, the way two motors from + # the same production lot differ. Roughly +/- 3%, a typical manufacturing + # tolerance; reshapes the whole thrust curve to match each draw. + stochastic_motor = StochasticSolidMotor( + solid_motor=Pro75M1670, + total_impulse=(6026, 180, "normal"), # newton-seconds + burn_out_time=(3.9, 0.12, "normal"), # seconds + ) + + # Rocket: same Calisto, but the dry mass and the balance point vary too. + # The surfaces are added back with no spread (fixed geometry). + stochastic_rocket = StochasticRocket( + rocket=rocket, + mass=(14.426, 0.4, "normal"), # dry mass, kg + center_of_mass_without_motor=(0.0, 0.03, "normal"), # balance, m + ) + stochastic_rocket.add_motor(stochastic_motor, position=(-1.255, 0)) + fixed_surface = { + NoseCone: (StochasticNoseCone, stochastic_rocket.add_nose), + TrapezoidalFins: (StochasticTrapezoidalFins, + stochastic_rocket.add_trapezoidal_fins), + Tail: (StochasticTail, stochastic_rocket.add_tail), + } + # Re-add each surface with no spread; a (value, 0) position means "fixed". + for surface, position in rocket.aerodynamic_surfaces: + stochastic_surface, add = fixed_surface[type(surface)] + add(stochastic_surface(surface), (position.z, 0)) + + # Flight: fixed rail and launch angles; stop each run at apogee to save time. + base_flight = Flight( + rocket=rocket, environment=windy_site(5), + rail_length=5.2, inclination=85, heading=0, terminate_on_apogee=True, + ) + stochastic_flight = StochasticFlight(flight=base_flight, terminate_on_apogee=True) + + # A data_collector callback receives each finished flight and returns a value, + # here the rail-exit angle of attack (not one of the standard exports). + analysis = MonteCarlo( + filename="../data/monte_carlo/stability_dispersion", + environment=stochastic_env, + rocket=stochastic_rocket, + flight=stochastic_flight, + data_collector={ + "rail_exit_aoa": lambda f: f.angle_of_attack(f.out_of_rail_time), + }, + ) + analysis.simulate(number_of_simulations=100, include_function_data=False) + + analysis.set_results() + margins = np.array(analysis.results["out_of_rail_stability_margin"]) + aoa_exits = np.array(analysis.results["rail_exit_aoa"]) + +.. jupyter-execute:: + :hide-code: + :hide-output: + + # The docs build does not run the Monte Carlo above; it loads the committed + # output below instead. Regenerate it by running the code above (which writes + # to the same path) after changing any distribution. + import json + + results_file = "../data/monte_carlo/stability_dispersion.outputs.txt" + with open(results_file, encoding="utf-8") as f: + records = [json.loads(line) for line in f] + margins = np.array([r["out_of_rail_stability_margin"] for r in records]) + aoa_exits = np.array([r["rail_exit_aoa"] for r in records]) + +The two quantities are now distributions over a simulated launch day: + +.. jupyter-execute:: + + fig, (axm, axa) = plt.subplots(1, 2, figsize=(10, 4)) + axm.hist(margins, bins=20, color="#4c72b0") + axm.axvspan(2.0, 3.0, color="green", alpha=0.12, label="2-3 cal target") + axm.set_xlabel("Out-of-rail stability margin (cal)") + axm.set_ylabel("Simulations") + axm.legend() + axa.hist(aoa_exits, bins=20, color="#c44e52") + axa.axvline(10, color="k", ls="--", label="stall onset (~10 deg)") + axa.set_xlabel("Rail-exit angle of attack (deg)") + axa.legend() + fig.suptitle(f"Launch-day dispersion ({margins.size} simulations)") + fig.tight_layout() + plt.show() + + print(f"out-of-rail margin : {np.percentile(margins, 5):.2f} to " + f"{np.percentile(margins, 95):.2f} cal (5th-95th percentile)") + print(f"rail-exit AoA : 95th pct {np.percentile(aoa_exits, 95):.1f} deg, " + f"worst {aoa_exits.max():.1f} deg") + print(f"flights above stall: {100 * np.mean(aoa_exits > 10):.0f}%") + +This is a concrete deliverable of a stability analysis. Not a single +margin, but a *distribution* read against the design thresholds. If a meaningful +fraction of flights cross either line, the rail, the mass or the margin needs +another look before flying. + +.. seealso:: + + A full dispersion study varies every input, not just five, and runs the + flights in parallel. ``analysis.simulate(..., parallel=True)`` does that, + and the ``Stochastic*`` classes cover parachutes and rail buttons too. See + :ref:`stochastic_usage` for the class walkthrough, and :ref:`MRS` for + weighting a finished sample toward measured launch-day conditions. + +Part 6: How much does the static margin matter? +=============================================== + +The dispersion above shows a Calisto-class rocket that is comfortably stable, +and yet a great deal of design effort in rocketry goes into chasing static +margin. The simulation lets us weigh that margin against the other things a +builder can change, and see **how much it really decides**, following +Thomas Fetter's flight-data study *How Far Does a Rocket Turn Into the +Wind?* (NARCON-2024). + +A rocket launched straight up into a crosswind turns as it climbs, and by the +time the motor burns out its flight path has tilted some degrees away from +vertical. This tilt is the *turn*, and because the launch was vertical it is +**entirely the rocket's response to the wind**, the weathercocking that +stability is meant to hold in check. Sweeping each design parameter on its own across a +realistic range, with the others left at their nominal values, shows how much +each one moves the turn: + +.. jupyter-execute:: + + def turn_at_burnout(flight): + """Flight-path tilt away from vertical at motor burnout, in degrees.""" + return 90 - flight.path_angle(flight.rocket.motor.burn_out_time) + + def vertical_flight(rocket, wind, rail=5.2): + return Flight( + rocket=rocket, environment=windy_site(wind), + rail_length=rail, inclination=90, heading=0, terminate_on_apogee=True, + ) + + def sweep(values, make_flight): + flights = [make_flight(v) for v in values] + return np.array([turn_at_burnout(f) for f in flights]), flights + + # Each lever swept alone across a realistic range; others nominal, 5 m/s wind. + winds = np.linspace(0, 14, 9) # crosswind, m/s + rails = np.linspace(1.2, 9.0, 8) # rail length sets the exit velocity + cgs = np.linspace(-0.25, 0.9, 10) # sets the static margin + masses = np.linspace(9, 30, 8) # dry mass, kg + + turn_wind, _ = sweep(winds, lambda w: vertical_flight(build_rocket(), w)) + turn_rail, rail_f = sweep(rails, lambda r: vertical_flight(build_rocket(), 5, rail=r)) + turn_cg, cg_f = sweep(cgs, lambda c: vertical_flight(build_rocket(c), 5)) + turn_mass, _ = sweep(masses, lambda m: vertical_flight(build_rocket(mass=m), 5)) + + exit_v = np.array([f.out_of_rail_velocity for f in rail_f]) + margins = np.array([f.rocket.static_margin(0) for f in cg_f]) + + panels = [ + (winds, turn_wind, "crosswind (m/s)", "wind speed", "#c0392b"), + (exit_v, turn_rail, "exit velocity (m/s)", "exit velocity (rail length)", "#e67e22"), + (margins, turn_cg, "static margin (cal)", "static margin", "#2980b9"), + (masses, turn_mass, "dry mass (kg)", "mass", "#27ae60"), + ] + ymax = max(y.max() for _, y, *_ in panels) + + fig, axs = plt.subplots(2, 2, figsize=(9.5, 7), sharey=True) + for ax, (x, y, xlabel, title, color) in zip(axs.flat, panels): + ax.fill_between(x, 0, y, color=color, alpha=0.12) + ax.plot(x, y, "-o", color=color, lw=2.4, ms=6, mfc=color, mec="white", mew=0.8) + ax.annotate(f"swing {y[-1] - y[0]:+.1f}°", # signed: low end -> high end + xy=(0.04, 0.92), xycoords="axes fraction", ha="left", va="top", + fontsize=11, fontweight="bold", color=color) + ax.set_title(title, fontweight="bold") + ax.set_xlabel(xlabel) + ax.grid(True, alpha=0.3) + ax.set_ylim(0, ymax * 1.12) + axs[0, 0].set_ylabel("turn at burnout (deg)") + axs[1, 0].set_ylabel("turn at burnout (deg)") + fig.suptitle("What moves the turn into the wind? (each lever alone; others nominal, " + "5 m/s wind)", fontsize=12, fontweight="bold") + fig.tight_layout() + plt.show() + +Each panel is labeled with its *swing*, meaning how many degrees the turn +changes as that parameter goes from the low end of its range to the high end. + +The wind dominates, mass comes next, and a higher exit velocity has a +moderate effect the other way, lowering the turn. + +**The static margin is the weakest of the four**: across a change in margin +the turn barely moves, and it levels off at high margins, holding steady well +past the over-stable range. For a Calisto-class rocket the margin is simply +not what decides how far it weathercocks. A rocket that turns hard into the +wind is easy to **misjudge as over- or super-stable**. + +What static margin it does instead is *correction*. +Keeping the center of pressure behind the center of mass is what lets a +disturbance correct itself at all (:ref:`stability_margin_part` and +:ref:`part_dynamic`), and the rail-exit angle of attack still has to stay +below the stall range. Beyond that, **a larger margin does little for a +flight like this one**. + +Part 7: Pitch and yaw planes +============================ + +A rocket with evenly spaced fins, like Calisto, is **axisymmetric**. Its +geometry does not change under rotation about the body axis, so its +stability is the same in every plane, and a single margin describes it +fully. For these rockets, ``Rocket.is_axisymmetric`` returns ``True``, and +the rest of this section does not apply. + +Some configurations are **not** axisymmetric: canards on a single axis, +off-center payloads, fins arranged asymmetrically. For these, stability +differs between the **pitch** plane and the **yaw** plane, and RocketPy +computes each one independently: + +- pitch: ``aerodynamic_center``, ``static_margin``, ``stability_margin``; +- yaw: ``aerodynamic_center_yaw``, ``static_margin_yaw``, ``stability_margin_yaw``. + +For an axisymmetric rocket, the two planes coincide. When they do not, +RocketPy issues a warning, because the unqualified ``static_margin`` then +describes the pitch plane only. In that case, the plotting and print +methods report both planes. + +Helper code +=========== + +Every worked example on this page is built on the same Calisto reference +rocket and a handful of small helper functions. Most of that code runs behind +the scenes so the examples above can stay focused on one idea at a time. It is +gathered here so it can be read and reused. The data-file paths are written +relative to the RocketPy ``docs`` directory; adjust them to your own setup. + +.. code-block:: python + + import numpy as np + import matplotlib.pyplot as plt + + from rocketpy import Environment, SolidMotor, Rocket, Flight + + # The Calisto reference rocket from the First Simulation guide, reused for + # every worked example on this page. + env = Environment(latitude=32.990254, longitude=-106.974998, elevation=1400) + env.set_atmospheric_model(type="standard_atmosphere") + + Pro75M1670 = SolidMotor( + thrust_source="../data/motors/cesaroni/Cesaroni_M1670.eng", + dry_mass=1.815, + dry_inertia=(0.125, 0.125, 0.002), + nozzle_radius=33 / 1000, + grain_number=5, + grain_density=1815, + grain_outer_radius=33 / 1000, + grain_initial_inner_radius=15 / 1000, + grain_initial_height=120 / 1000, + grain_separation=5 / 1000, + grains_center_of_mass_position=0.397, + center_of_dry_mass_position=0.317, + nozzle_position=0, + burn_time=3.9, + throat_radius=11 / 1000, + coordinate_system_orientation="nozzle_to_combustion_chamber", + ) + + rocket = Rocket( + radius=127 / 2000, + mass=14.426, + inertia=(6.321, 6.321, 0.034), + power_off_drag="../data/rockets/calisto/powerOffDragCurve.csv", + power_on_drag="../data/rockets/calisto/powerOnDragCurve.csv", + center_of_mass_without_motor=0, + coordinate_system_orientation="tail_to_nose", + ) + rocket.add_motor(Pro75M1670, position=-1.255) + rocket.set_rail_buttons( + upper_button_position=0.0818, + lower_button_position=-0.618, + angular_position=45, + ) + rocket.add_nose(length=0.55829, kind="vonKarman", position=1.278) + rocket.add_trapezoidal_fins( + n=4, + root_chord=0.120, + tip_chord=0.060, + span=0.110, + cant_angle=0.0, + position=-1.04956, + airfoil=("../data/airfoils/NACA0012-radians.txt", "radians"), + ) + rocket.add_tail( + top_radius=0.0635, bottom_radius=0.0435, length=0.060, position=-1.194656 + ) + + test_flight = Flight( + rocket=rocket, environment=env, rail_length=5.2, inclination=85, heading=0 + ) + +The design studies build fresh rockets and windy environments through two +small factories: + +.. code-block:: python + + def build_rocket(center_of_mass_without_motor=0.0, lateral_inertia=6.321, + mass=14.426, motor=Pro75M1670): + """Calisto with four adjustable parameters. Shifting the dry center of + mass toward the nose (positive) raises the static margin; toward the tail + (negative) lowers it. The lateral moment of inertia is adjustable + separately, which changes the dynamic response without changing the + static margin. The dry mass (in kilograms) and the motor can also be + swapped, for the mass and thrust studies later in this document. + Everything else, including the aerodynamics, is fixed. + """ + rocket = Rocket( + radius=127 / 2000, + mass=mass, + inertia=(lateral_inertia, lateral_inertia, 0.034), + power_off_drag="../data/rockets/calisto/powerOffDragCurve.csv", + power_on_drag="../data/rockets/calisto/powerOnDragCurve.csv", + center_of_mass_without_motor=center_of_mass_without_motor, + coordinate_system_orientation="tail_to_nose", + ) + rocket.add_motor(motor, position=-1.255) + rocket.add_nose(length=0.55829, kind="vonKarman", position=1.278) + rocket.add_trapezoidal_fins( + n=4, root_chord=0.120, tip_chord=0.060, span=0.110, + cant_angle=0.0, position=-1.04956, + airfoil=("../data/airfoils/NACA0012-radians.txt", "radians"), + ) + rocket.add_tail( + top_radius=0.0635, bottom_radius=0.0435, length=0.060, position=-1.194656 + ) + return rocket + + def windy_site(wind_speed): + """A standard atmosphere with a constant eastward crosswind, in m/s.""" + site = Environment(latitude=32.990254, longitude=-106.974998, elevation=1400) + site.set_atmospheric_model( + type="custom_atmosphere", wind_u=wind_speed, wind_v=0 + ) + return site + +The three helpers that measure the turn and sweep one parameter at a time +(``turn_at_burnout``, ``vertical_flight`` and ``sweep``) are shown inline where +they are used, in Part 6 above. + diff --git a/docs/user/first_simulation.rst b/docs/user/first_simulation.rst index 18e4b9882..85b418705 100644 --- a/docs/user/first_simulation.rst +++ b/docs/user/first_simulation.rst @@ -310,6 +310,12 @@ We can then see if the rocket is stable by plotting the static margin: If it is unreasonably **high**, your rocket is **super stable** and the simulation will most likely **fail**. +.. seealso:: + + For a full treatment of static margin, stability margin and dynamic + stability, including what these numbers mean, what values to aim for, and + how they play out over a real flight, see :ref:`aero_cp_stability`. + To guarantee that the rocket is stable, the positions of all added components must be correct. The ``Rocket`` class can help you with the ``draw`` method: @@ -580,15 +586,21 @@ following method: test_flight.plots.fluid_mechanics_data() -Stability Margin and Frequency Response +Stability Margin and Dynamic Stability ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -The Stability margin can be checked along with the frequency response of the -rocket: +The stability margin over the flight: + +.. jupyter-execute:: + + test_flight.plots.stability_margin_data() + +The dynamic-stability quantities (natural frequency, damping ratio and the +attitude frequency response): .. jupyter-execute:: - test_flight.plots.stability_and_control_data() + test_flight.plots.dynamic_stability_data() Visualizing the Trajectory in Google Earth diff --git a/docs/user/flight.rst b/docs/user/flight.rst index 31e7ab588..a356c7dfe 100644 --- a/docs/user/flight.rst +++ b/docs/user/flight.rst @@ -474,7 +474,8 @@ Energy Analysis flight.plots.energy_data() # Stability analysis - flight.plots.stability_and_control_data() + flight.plots.stability_margin_data() + flight.plots.dynamic_stability_data() Comprehensive Analysis ~~~~~~~~~~~~~~~~~~~~~~ diff --git a/docs/user/index.rst b/docs/user/index.rst index c44527670..218fb9e4f 100644 --- a/docs/user/index.rst +++ b/docs/user/index.rst @@ -47,4 +47,5 @@ RocketPy's User Guide :caption: Further Analysis Function - Utilities \ No newline at end of file + Utilities + Center of Pressure and Stability \ No newline at end of file diff --git a/docs/user/rocket/generic_surface.rst b/docs/user/rocket/generic_surface.rst index 70cbd4d36..ffe210be8 100644 --- a/docs/user/rocket/generic_surface.rst +++ b/docs/user/rocket/generic_surface.rst @@ -3,7 +3,7 @@ Generic Surfaces and Custom Aerodynamic Coefficients ==================================================== -Generic aerodynamic surfaces can be used to model aerodynamic forces based on +Generic aerodynamic surfaces can be used to model aerodynamic forces based on force and moment coefficients. The :class:`rocketpy.GenericSurface` receives the coefficients as functions of the angle of attack, side slip angle, Mach number, Reynolds number, pitch rate, yaw rate, and roll rate. @@ -16,119 +16,88 @@ slip angle, Mach number, Reynolds number, pitch rate, yaw rate, and roll rate. These classes allows the user to be less dependent on the built-in aerodynamic surfaces and to define their own aerodynamic coefficients. -Both classes base their coefficient on the definition of the aerodynamic frame -of reference. +Wind Frame and Body Frame +------------------------- -Aerodynamic Frame ------------------ - -The aerodynamic frame of reference of the rocket is defined as follows: - -- The origin is at the rocket's center of dry mass (``center_of_dry_mass_position``). -- The ``z`` axis is defined along the rocket's centerline, pointing from the center of dry mass towards the nose. -- The ``x`` and ``y`` axes are perpendicular. -- The partial angle of attack (``alpha``) is defined as the angle, in the y-z - plane, from the velocity vector to the z axis. -- The partial side slip angle (``beta``) is defined as the angle, in the x-z - plane, from the velocity vector to the z axis. +A surface's aerodynamic force is the same physical vector written in two +coordinate frames: the **body frame**, fixed to the rocket, and the **wind +frame**, aligned with the airflow. You can provide the coefficients in either +frame; the two are related by the angle of attack and sideslip defined below. -The following figure shows the aerodynamic frame of reference: +The following figure shows the body frame (subscript :math:`B`) and the wind +frame (subscript :math:`W`): .. figure:: ../../static/rocket/aeroframe.png :align: center - :alt: Aerodynamic frame of reference + :alt: Wind frame of reference In the figure we define: - :math:`\mathbf{\vec{V}}` as rocket velocity vector. - :math:`x_B`, :math:`y_B`, and :math:`z_B` as the body axes. -- :math:`x_A`, :math:`y_A`, and :math:`z_A` as the aerodynamic axes. +- :math:`x_W`, :math:`y_W`, and :math:`z_W` as the wind-frame axes. - :math:`\alpha` as the partial angle of attack. - :math:`\beta` as the side slip angle. - :math:`L` as the lift force. - :math:`D` as the drag force. -- :math:`Q` as the side force. +- :math:`Q` as the wind frame side force. +- :math:`N` as the normal force. +- :math:`A` as the axial force. +- :math:`Y` as the body frame side force. -Here we define the aerodynamic forces in the aerodynamic frame of reference as: +Body frame +~~~~~~~~~~ -.. math:: - \vec{\mathbf{F}}_A=\begin{bmatrix}X_A\\Y_A\\Z_A\end{bmatrix}_A=\begin{bmatrix}Q\\-L\\-D\end{bmatrix}_A - -The aerodynamic forces in the body axes coordinate system are defined as -:math:`\vec{\mathbf{F}}_B`. +The body frame is fixed to the rocket: -.. math:: - \vec{\mathbf{F}}_B=\begin{bmatrix}X_A\\Y_A\\Z_A\end{bmatrix}_B=\mathbf{M}_{BA}\cdot\begin{bmatrix}Q\\-L\\-D\end{bmatrix}_A +- The origin is at the rocket's center of dry mass (``center_of_dry_mass_position``). +- The :math:`z_B` axis lies along the rocket's centerline, pointing from the center of dry mass towards the nose. +- The :math:`x_B` and :math:`y_B` axes are perpendicular to it. -Where the transformation matrix :math:`\mathbf{M}_{BA}`, which transforms the -aerodynamic forces from the aerodynamic frame of reference to the body axes -coordinate system, is defined as: +In this frame the aerodynamic force is made up of the normal force :math:`N`, +the side force :math:`Y` and the axial force :math:`A`. As in the wind frame, the +side force is the :math:`x_B` component, while the normal and axial forces enter +the :math:`y_B` and :math:`z_B` components with a negative sign: .. math:: - \mathbf{M}_{BA} = \begin{bmatrix} - 1 & 0 & 0 \\ - 0 & \cos(\alpha) & -\sin(\alpha) \\ - 0 & \sin(\alpha) & \cos(\alpha) - \end{bmatrix} - \begin{bmatrix} - \cos(\beta) & 0 & -\sin(\beta) \\ - 0 & 1 & 0 \\ - \sin(\beta) & 0 & \cos(\beta) - \end{bmatrix} - + \vec{\mathbf{F}}_B=\begin{bmatrix}X_B\\Y_B\\Z_B\end{bmatrix}_B=\begin{bmatrix}Y\\-N\\-A\end{bmatrix}_B -The forces coefficients can finally be defined as: +Wind frame +~~~~~~~~~~ -- :math:`C_L` as the lift coefficient. -- :math:`C_Q` as the side force coefficient (or cross stream force coefficient). -- :math:`C_D` as the drag coefficient. - -And the forces from the coefficients are defined as: +The wind frame is aligned with the airflow: its :math:`z_W` axis runs along the +velocity vector. In this frame the aerodynamic force is the lift :math:`L`, the +drag :math:`D` and the (wind-frame) side force :math:`Q`: .. math:: - \begin{bmatrix}X_A\\Y_A\\Z_A\end{bmatrix}_B =\mathbf{M}_{BA}\cdot\overline{q}\cdot A_{ref}\cdot\begin{bmatrix}C_Q\\-C_L\\-C_D\end{bmatrix}_A - -Where: + \vec{\mathbf{F}}_W=\begin{bmatrix}X_W\\Y_W\\Z_W\end{bmatrix}_W=\begin{bmatrix}Q\\-L\\-D\end{bmatrix}_W -- :math:`\bar{q}` is the dynamic pressure. -- :math:`A_{ref}` is the reference area used to calculate the coefficients. - Commonly the rocket's cross-sectional area is used as the reference area. - -The moment coefficients can be defined as: - -- :math:`C_l` as the rolling moment coefficient. -- :math:`C_m` as the pitching moment coefficient. -- :math:`C_n` as the yawing moment coefficient. +Relating the two frames +~~~~~~~~~~~~~~~~~~~~~~~~ -And the moments from the coefficients are defined as: +The two are the same force, related by the angle-of-attack/sideslip rotation +:math:`\mathbf{M}_{BW}`, which transforms the wind frame into the body frame: .. math:: - \vec{\mathbf{M}}_B=\begin{bmatrix}M_{x_A}\\M_{y_A}\\M_{z_A}\end{bmatrix}_B =\overline{q}\cdot A_{ref}\cdot L_{ref}\cdot\begin{bmatrix}C_m\\C_n\\C_l\end{bmatrix} - -Where: - -- :math:`L_{ref}` is the reference length used to calculate the coefficients. - Commonly the rocket's diameter is used as the reference length. - - -Wind-frame and body-frame force coefficients -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -The three force coefficients above are given in the **aerodynamic (wind) frame**, -relative to the velocity vector: + \vec{\mathbf{F}}_B=\mathbf{M}_{BW}\cdot\begin{bmatrix}Q\\-L\\-D\end{bmatrix}_W -- :math:`C_L` (lift), :math:`C_Q` (side force) and :math:`C_D` (drag). +where -The same force can be expressed in the **body frame**, relative to the rocket's -axes, which is what tools such as Missile DATCOM, wind tunnels and Barrowman -report: - -- :math:`C_N` (normal force, perpendicular to the body axis), -- :math:`C_Y` (body side force), -- :math:`C_A` (axial force, along the body axis). +.. math:: + \mathbf{M}_{BW} = \begin{bmatrix} + 1 & 0 & 0 \\ + 0 & \cos(\alpha) & \sin(\alpha) \\ + 0 & -\sin(\alpha) & \cos(\alpha) + \end{bmatrix} + \begin{bmatrix} + \cos(\beta) & 0 & \sin(\beta) \\ + 0 & 1 & 0 \\ + -\sin(\beta) & 0 & \cos(\beta) + \end{bmatrix} -The two sets are the same force in different frames, related by the -angle-of-attack/sideslip rotation :math:`\mathbf{M}_{BA}`: +The force coefficients follow the same rotation. In the wind frame they are the +lift :math:`C_L`, side :math:`C_Q` and drag :math:`C_D`; in the body frame the +normal :math:`C_N`, side :math:`C_Y` and axial :math:`C_A`: .. math:: \begin{aligned} @@ -140,56 +109,47 @@ angle-of-attack/sideslip rotation :math:`\mathbf{M}_{BA}`: At small angles these reduce to :math:`C_N \approx C_L`, :math:`C_Y \approx C_Q` and :math:`C_A \approx C_D`. -Every aerodynamic surface exposes **all nine** coefficients as attributes -(``cL``, ``cQ``, ``cD``, ``cN``, ``cY``, ``cA``, ``cm``, ``cn``, ``cl``). The -coefficients you did not provide are computed on demand from the ones you did, -so you can always read a surface's forces in whichever frame you need, for -example ``surface.cN`` for the normal-force coefficient. +The force itself is recovered from the coefficients with the dynamic pressure +:math:`\bar q` and the reference area :math:`A_{ref}`. From **body-frame** +coefficients the force is obtained directly, with no rotation: -**Choosing the input frame.** Because rocket aerodynamic data (DATCOM, wind -tunnel, CFD, Barrowman) is usually reported in the body frame, you can supply -your coefficients in either frame and RocketPy converts them for you. Provide the -wind-frame names (``cL``/``cQ``/``cD``) or the body-frame names -(``cN``/``cY``/``cA``); the moment coefficients (``cm``/``cn``/``cl``) are the -same in both. +.. math:: + \vec{\mathbf{F}}_B =\begin{bmatrix}Y\\-N\\-A\end{bmatrix}_B= \overline{q}\cdot A_{ref}\cdot\begin{bmatrix}C_Y\\-C_N\\-C_A\end{bmatrix}_B -Moment reference point -~~~~~~~~~~~~~~~~~~~~~~~~ +while **wind-frame** coefficients are rotated into the body frame first: -The moment coefficients :math:`C_m`, :math:`C_n` and :math:`C_l` are taken about -the surface's own reference point (its ``center_of_pressure``). When the rocket -assembles the total aerodynamic moment it transports each surface's force from -that point to the rocket's **center of dry mass**, adding the -:math:`\vec{r}_{\text{cp} \to \text{cdm}} \times \vec{F}` term, so the rocket's -reported pitch/yaw moment and static margin are about the center of dry mass. +.. math:: + \vec{\mathbf{F}}_B =\mathbf{M}_{BW}\cdot\overline{q}\cdot A_{ref}\cdot\begin{bmatrix}C_Q\\-C_L\\-C_D\end{bmatrix}_W -This matters when your coefficients come from a source that uses a different -reference. Aerodynamic decks frequently give the pitch moment **about the nose -tip** (or another fixed station) rather than about the center of dry mass. A -pitch-moment coefficient referenced to a point a distance :math:`d` ahead of the -surface's center of pressure must be shifted before use: +where :math:`\bar{q}` is the dynamic pressure and :math:`A_{ref}` the reference +area (commonly the rocket's cross-sectional area). -.. math:: - C_{m,\,\text{cp}} = C_{m,\,\text{ref}} + \frac{d}{L_{ref}}\, C_N +Moments +~~~~~~~ -Provide the coefficient about the surface's center of pressure (or set -``center_of_pressure`` so the transport lands the moment at the intended point); -otherwise the static margin will be off by the reference-point offset. +The moment coefficients are the same in both frames: the rolling moment +:math:`C_l`, the pitching moment :math:`C_m` and the yawing moment :math:`C_n`. +The moments about the body axes follow with the reference area and the reference +length :math:`L_{ref}`: +.. math:: + \vec{\mathbf{M}}_B=\begin{bmatrix}M_{x}\\M_{y}\\M_{z}\end{bmatrix}_B =\overline{q}\cdot A_{ref}\cdot L_{ref}\cdot\begin{bmatrix}C_m\\C_n\\C_l\end{bmatrix} + +where :math:`L_{ref}` is the reference length (commonly the rocket's diameter). -Aerodynamic angles -~~~~~~~~~~~~~~~~~~ +Angles of attack and sideslip +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -The aerodynamic angles are defined in two different ways in RocketPy: +RocketPy uses the flow angles two ways: -- As the angle of attack (:math:`\alpha`) and the side slip \ - angle (:math:`\beta`), which are defined in the image above. These are used \ - in the calculation of the generic surface forces and moments. -- As the total angle of attack (:math:`\alpha_{\text{tot}}`), defined as the \ - angle between the total velocity vector and the rocket's centerline. This is \ - used in the calculation of the standard aerodynamic surface forces and moments. +- the partial **angle of attack** :math:`\alpha` and **sideslip angle** + :math:`\beta` (shown in the figure above), used by the generic-surface forces + and moments; +- the **total angle of attack** :math:`\alpha_{\text{tot}}`, the angle between + the total velocity vector and the rocket's centerline, used by the standard + (Barrowman) surfaces. -The partial angles are calculated as: +The partial angles are .. math:: \begin{aligned} @@ -197,69 +157,119 @@ The partial angles are calculated as: \beta &= \arctan\left(\frac{V_x}{V_z}\right) \end{aligned} -The total angle of attack is calculated as: +and the total angle of attack is .. math:: \alpha_{\text{tot}} = \arccos\left(\frac{\mathbf{\vec{V}}\cdot\mathbf{z_B}}{||\mathbf{\vec{V}}||\cdot||\mathbf{z_B}||}\right) .. note:: When the simulation is done, the total angle of attack is accessed through - the :attr:`rocketpy.Flight.angle_of_attack` attribute. - The partial angles of attack and side slip are accessed through the - :attr:`rocketpy.Flight.partial_angle_of_attack` and + the :attr:`rocketpy.Flight.angle_of_attack` attribute. The partial angles of + attack and sideslip are accessed through the + :attr:`rocketpy.Flight.partial_angle_of_attack` and :attr:`rocketpy.Flight.angle_of_sideslip` attributes, respectively. + .. _genericsurface: Generic Surface Class --------------------- -The :class:`rocketpy.GenericSurface` class is used to define an aerodynamic -surface based on force and moment coefficients. A generic surface is defined -as follows: +The :class:`rocketpy.GenericSurface` class defines an aerodynamic surface +directly from its force and moment coefficients. A surface is created by giving +it a reference area and length, the coefficients, and a few optional settings: .. seealso:: - For more information on class initialization, see - :class:`rocketpy.GenericSurface.__init__` + For more information on class initialization, see + :class:`rocketpy.GenericSurface.__init__` .. code-block:: python + import numpy as np from rocketpy import GenericSurface - + radius = 0.0635 - + generic_surface = GenericSurface( reference_area=np.pi * radius**2, reference_length=2 * radius, coefficients={ - "cL": "cL.csv", - "cQ": "cQ.csv", - "cD": "cD.csv", + "cN": "cN.csv", + "cY": "cY.csv", + "cA": "cA.csv", "cm": "cm.csv", "cn": "cn.csv", "cl": "cl.csv", }, + center_of_pressure=(0, 0, 0), name="Generic Surface", + reynolds_length=2 * radius, + interpolation="linear", + extrapolation="constant", + force_convention="body", + active_during="always", ) -The ``coefficients`` argument is a dictionary containing the coefficients of the -generic surface. The keys of the dictionary are the coefficient names, and the -values are the coefficients. The possible coefficient names are: +Constructor parameters +~~~~~~~~~~~~~~~~~~~~~~~~ -- ``cL``: Lift coefficient. -- ``cQ``: Side force coefficient. -- ``cD``: Drag coefficient. +:class:`rocketpy.GenericSurface` takes the following parameters: + +- ``reference_area`` (int or float): reference area used to non-dimensionalize + the coefficients, in :math:`m^2`. Commonly the rocket's cross-sectional area. +- ``reference_length`` (int or float): reference length, in meters, used to + non-dimensionalize the moment coefficients and the reduced rotation rates. + Commonly the rocket's diameter. +- ``coefficients`` (dict): the force and moment coefficients, by name (detailed + in `Coefficients`_ below). +- ``center_of_pressure`` (tuple, optional): the point where the surface's forces + and moments are applied, in the surface's local frame. Default ``(0, 0, 0)``. + See `Moment reference point`_. +- ``name`` (str, optional): a name for the surface. Default + ``"Generic Surface"``. +- ``reynolds_length`` (int or float, optional): length scale, in meters, of the + Reynolds number fed to the coefficients. Default ``None`` (uses + ``reference_length``). +- ``interpolation`` (str or dict, optional): how tabulated coefficients are + interpolated between their data points. Default ``None``. See + :ref:`generic_surface_interpolation`. +- ``extrapolation`` (str or dict, optional): how tabulated coefficients behave + outside their tabulated range. Default ``None``. See + :ref:`generic_surface_interpolation`. +- ``force_convention`` (str, optional): the frame the force coefficients are + given in, ``"body"`` or ``"wind"``. Default ``None`` (inferred from the + coefficient names). +- ``active_during`` (str or callable, optional): when the surface produces + aerodynamic force during the flight. Default ``"always"``. See + :ref:`active_during`. + +Coefficients +~~~~~~~~~~~~~ + +The ``coefficients`` argument is a dictionary mapping each coefficient's name to +its value. The body-frame coefficient names are: + +- ``cN``: Normal force coefficient (perpendicular to the body axis). +- ``cY``: Side force coefficient. +- ``cA``: Axial force coefficient (along the body axis). - ``cm``: Pitching moment coefficient. - ``cn``: Yawing moment coefficient. - ``cl``: Rolling moment coefficient. -Only one of the coefficients is required to be provided, but any combination of -the coefficients can be used. The coefficient values can be provided as a -single value, a callable function of seven arguments, or a path to a ``.csv`` -file containing the values. +Alternatively, you can supply the force coefficients in the **wind frame** as +``cL`` (lift), ``cQ`` (side) and ``cD`` (drag) in place of ``cN``/``cY``/``cA`` +(the moment coefficients ``cm``/``cn``/``cl`` are shared by both frames). By +default the frame is inferred from the names you pass; set ``force_convention`` +(``"body"`` or ``"wind"``) to state it explicitly. Whichever frame you choose, +all nine coefficients remain available as attributes (``surface.cN``, +``surface.cL``, ...), converted on demand from the ones you provided using the +rotation described in `Relating the two frames`_ above. + +Only one coefficient is required, and any combination can be provided; the ones +you omit are treated as zero. -The coefficients are all functions of: +Each coefficient is a function of the same seven independent variables: - Angle of attack (:math:`\alpha`) in radians. - Side slip angle (:math:`\beta`) in radians. @@ -279,35 +289,23 @@ The coefficients are all functions of: p^{*} = \frac{p \, L_{ref}}{2 V} where :math:`L_{ref}` is the surface reference length and :math:`V` the - freestream speed. This matches how published and tool-generated aerotables - (Missile DATCOM, OpenVSP, CFD/wind-tunnel sweeps) tabulate rate derivatives, - so such tables can be used directly. RocketPy non-dimensionalizes the body - rates internally before evaluating the coefficients (the factor is 0 at zero - airspeed). Define your tables against the reduced rates. + freestream speed. RocketPy non-dimensionalizes the body rates internally + before evaluating the coefficients (the factor is 0 at zero airspeed). + Define your tables against the reduced rates. -.. math:: - \begin{aligned} - C_L &= f(\alpha, \beta, Ma, Re, q, r, p) \\ - C_Q &= f(\alpha, \beta, Ma, Re, q, r, p) \\ - C_D &= f(\alpha, \beta, Ma, Re, q, r, p) \\ - C_m &= f(\alpha, \beta, Ma, Re, q, r, p) \\ - C_n &= f(\alpha, \beta, Ma, Re, q, r, p) \\ - C_l &= f(\alpha, \beta, Ma, Re, q, r, p) - \end{aligned} +Once evaluated, the coefficients are turned into body-frame forces and moments +exactly as described in `Wind Frame and Body Frame`_ above (wind-frame inputs +are rotated into the body frame first). -From the coefficients, the forces and moments are calculated with +Each coefficient value can be given three ways: a single number for a constant, +a callable function of the seven variables, or a path to a ``.csv`` file of +tabulated data. -.. math:: - \begin{aligned} - L &= \overline{q}\cdot A_{ref}\cdot C_L \\ - Q &= \overline{q}\cdot A_{ref}\cdot C_Q \\ - D &= \overline{q}\cdot A_{ref}\cdot C_D \\ - M_{m} &= \overline{q}\cdot A_{ref}\cdot L_{ref}\cdot C_m \\ - M_{n} &= \overline{q}\cdot A_{ref}\cdot L_{ref}\cdot C_n \\ - M_{l} &= \overline{q}\cdot A_{ref}\cdot L_{ref}\cdot C_l - \end{aligned} +Defining a coefficient as a callable +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -These coefficients can be defined as a callable such as: +A coefficient can be any callable that takes the seven independent variables and +returns the value: .. code-block:: python @@ -315,11 +313,14 @@ These coefficients can be defined as a callable such as: ... return value -In which any algorithm can be implemented to calculate the coefficient values. +Any algorithm can be implemented inside to compute the coefficient. + +Defining a coefficient from a CSV file +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -Otherwise, the coefficients can be defined as a ``.csv`` file. The file must -contain a header with at least one of the following columns representing the -independent variables: +A coefficient can also be tabulated in a ``.csv`` file. The file must have a +header naming its columns. The independent-variable columns are optional, but +those present must use these exact names: - ``alpha``: Angle of attack. - ``beta``: Side slip angle. @@ -329,28 +330,16 @@ independent variables: - ``yaw_rate``: Yaw rate. - ``roll_rate``: Roll rate. -When the surface is created with ``unsteady_aero=True``, the coefficients may -additionally depend on the time derivatives of the flow angles, appended after -``roll_rate``: - -- ``alpha_dot``: Rate of change of the angle of attack. -- ``beta_dot``: Rate of change of the side slip angle. - -Callables must then accept the two extra trailing arguments -(``coefficient(alpha, beta, Ma, Re, q, r, p, alpha_dot, beta_dot)``) and -``.csv`` files may include ``alpha_dot``/``beta_dot`` columns. - -The last column must be the coefficient value, and must contain a header, -though the header name can be anything. +The **last** column holds the coefficient value; it **must** have a header, but +the header name can be anything. .. important:: - Not all columns need to be present in the file, but the columns that are - present must be correctly named as described above. Independent variable - columns can be in any order. + Not all independent-variable columns need to be present, but the columns that + are present must be named exactly as above. They can be in any order. -An example of a ``.csv`` file is shown below: +An example ``.csv`` file, tabulated against angle of attack and Mach: -.. code-block:: +.. code-block:: "alpha", "mach", "coefficient" -0.017, 0, -0.11 @@ -366,12 +355,22 @@ An example of a ``.csv`` file is shown below: 0.017, 2, 0.084 0.017, 3, 0.061 -After the definition of the ``GenericSurface`` object, it must be added to the -rocket's configuration: +.. note:: + The ``reynolds`` axis is by default based on the reference length (the + rocket diameter). Published rocket data often bases the Reynolds number on + the **body length** instead, which for a slender rocket is much larger. If + your table uses a different length, pass it as ``reynolds_length`` when + creating the surface so the Reynolds number the simulation feeds your table + matches the one it was built against. + +Adding the surface to the rocket +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Once defined, the surface is added to the rocket like any other: .. seealso:: For more information on how to add a generic surface to the rocket, see - :class:`rocketpy.Rocket.add_generic_surface` + :class:`rocketpy.Rocket.add_surfaces` .. code-block:: python :emphasize-lines: 5 @@ -382,20 +381,41 @@ rocket's configuration: ) rocket.add_surfaces(generic_surface, position=(0,0,0)) -The position of the generic surface is defined in the User Defined coordinate -System, see :ref:`rocket_axes` for more information. +The position is given in the User Defined Coordinate System; see +:ref:`rocket_axes` for more information. + +.. attention:: + If the generic surface is positioned **not** at the center of dry mass, the + forces generated by the force coefficients (``cN``, ``cY``, ``cA``) generate + a moment about the center of dry mass. This moment is computed and added to + the moment generated by the moment coefficients (``cm``, ``cn``, ``cl``). .. tip:: - If defining the coefficients of the entire rocket is desired, only a single - generic surface can be added to the rocket, positioned at the center of dry - mass. This will be equivalent to defining the coefficients of the entire - rocket. + To describe the whole vehicle with a single set of coefficients (rather than + one surface per component), use + :meth:`rocketpy.Rocket.add_full_body_aerodynamics`. See + :ref:`fullbodyaerodynamics`. -.. attention:: - If there generic surface is positioned **not** at the center of dry mass, the - forces generated by the force coefficients (cL, cQ, cD) will generate a - moment around the center of dry mass. This moment will be calculated and - added to the moment generated by the moment coefficients (cm, cn, cl). +Moment reference point +~~~~~~~~~~~~~~~~~~~~~~~~ + +The moment coefficients :math:`C_m`, :math:`C_n` and :math:`C_l` are taken about +the surface's own reference point (its ``center_of_pressure``). When the rocket +assembles the total aerodynamic moment it transports each surface's force from +that point to the rocket's **center of dry mass**, adding the +:math:`\vec{r}_{\text{cp} \to \text{cdm}} \times \vec{F}` term, so the rocket's +reported pitch/yaw moment and static margin are about the center of dry mass. + +This matters when your coefficients come from a source that uses a different +reference. A pitch-moment coefficient referenced to a point a distance :math:`d` +ahead of the surface's center of pressure must be shifted before use: + +.. math:: + C_{m,\,\text{cp}} = C_{m,\,\text{ref}} + \frac{d}{L_{ref}}\, C_N + +Provide the coefficient about the surface's center of pressure (or set +``center_of_pressure`` so the transport lands the moment at the intended point), +otherwise the static margin will be off by the reference-point offset. .. _lineargenericsurface: @@ -403,102 +423,122 @@ System, see :ref:`rocket_axes` for more information. Linear Generic Surface Class ---------------------------- -The :class:`rocketpy.LinearGenericSurface` class is used to define a aerodynamic -surface based on the forces and moments coefficient derivatives. A linear generic -surface will receive the derivatives of each coefficient with respect to the -independent variables. The derivatives are defined as: +The :class:`rocketpy.LinearGenericSurface` class defines an aerodynamic surface +from the **derivatives** of its force and moment coefficients, instead of the +coefficients themselves. This is convenient when you have stability-derivative +data rather than full tables: the surface builds each coefficient by summing its +derivatives times the independent variables. -- :math:`C_{\alpha}=\frac{dC}{d\alpha}`: Coefficient derivative with respect to angle of attack. -- :math:`C_{\beta}=\frac{dC}{d\beta}`: Coefficient derivative with respect to side slip angle. -- :math:`C_{Ma}=\frac{dC}{dMa}`: Coefficient derivative with respect to Mach number. -- :math:`C_{Re}=\frac{dC}{dRe}`: Coefficient derivative with respect to Reynolds number. -- :math:`C_{q}=\frac{dC}{dq}`: Coefficient derivative with respect to pitch rate. -- :math:`C_{r}=\frac{dC}{dr}`: Coefficient derivative with respect to yaw rate. -- :math:`C_{p}=\frac{dC}{dp}`: Coefficient derivative with respect to roll rate. +For every one of the six coefficients (``cN``, ``cY``, ``cA``, ``cm``, ``cn``, +``cl``), you provide a constant term and one derivative per independent variable: -A non derivative coefficient :math:`C_{0}` is also included. +- :math:`C_{0}`: the coefficient value at the reference condition. +- :math:`C_{\alpha}=\frac{dC}{d\alpha}`: derivative with respect to angle of attack. +- :math:`C_{\beta}=\frac{dC}{d\beta}`: derivative with respect to side slip angle. +- :math:`C_{Ma}=\frac{dC}{dMa}`: derivative with respect to Mach number. +- :math:`C_{Re}=\frac{dC}{dRe}`: derivative with respect to Reynolds number. +- :math:`C_{q}=\frac{dC}{dq}`: derivative with respect to pitch rate. +- :math:`C_{r}=\frac{dC}{dr}`: derivative with respect to yaw rate. +- :math:`C_{p}=\frac{dC}{dp}`: derivative with respect to roll rate. -Each coefficient derivative is defined as a function of all the seven -independent variables. +Just like the plain generic surface, each of these terms is itself a function of +all seven independent variables, and may be a constant, a callable, or a +tabulated ``.csv`` file. -The coefficients are then grouped into **forcing** coefficients: +How the coefficients are assembled +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +The derivatives are first combined into **forcing** coefficients, which depend on +the steady flow state (angles, Mach, Reynolds): .. math:: \begin{aligned} - C_{Lf} &= C_{L0} + C_{L\alpha}\cdot\alpha + C_{L\beta}\cdot\beta + C_{LMa}\cdot Ma + C_{LRe}\cdot Re \\ - C_{Qf} &= C_{Q0} + C_{Q\alpha}\cdot\alpha + C_{Q\beta}\cdot\beta + C_{QMa}\cdot Ma + C_{QRe}\cdot Re \\ - C_{Df} &= C_{D0} + C_{D\alpha}\cdot\alpha + C_{D\beta}\cdot\beta + C_{DMa}\cdot Ma + C_{DRe}\cdot Re \\ + C_{Nf} &= C_{N0} + C_{N\alpha}\cdot\alpha + C_{N\beta}\cdot\beta + C_{NMa}\cdot Ma + C_{NRe}\cdot Re \\ + C_{Yf} &= C_{Y0} + C_{Y\alpha}\cdot\alpha + C_{Y\beta}\cdot\beta + C_{YMa}\cdot Ma + C_{YRe}\cdot Re \\ + C_{Af} &= C_{A0} + C_{A\alpha}\cdot\alpha + C_{A\beta}\cdot\beta + C_{AMa}\cdot Ma + C_{ARe}\cdot Re \\ C_{mf} &= C_{m0} + C_{m\alpha}\cdot\alpha + C_{m\beta}\cdot\beta + C_{mMa}\cdot Ma + C_{mRe}\cdot Re \\ C_{nf} &= C_{n0} + C_{n\alpha}\cdot\alpha + C_{n\beta}\cdot\beta + C_{nMa}\cdot Ma + C_{nRe}\cdot Re \\ - C_{lf} &= C_{l0} + C_{l\alpha}\cdot\alpha + C_{l\beta}\cdot\beta + C_{lMa}\cdot Ma + C_{lRe}\cdot Re + C_{lf} &= C_{l0} + C_{l\alpha}\cdot\alpha + C_{l\beta}\cdot\beta + C_{lMa}\cdot Ma + C_{lRe}\cdot Re \end{aligned} -And **damping** coefficients: +and **damping** coefficients, which depend on the rotation rates: .. math:: \begin{aligned} - C_{Ld} &= C_{L_{q}}\cdot q + C_{L_{r}}\cdot r + C_{L_{p}}\cdot p \\ - C_{Qd} &= C_{Q_{q}}\cdot q + C_{Q_{r}}\cdot r + C_{Q_{p}}\cdot p \\ - C_{Dd} &= C_{D_{q}}\cdot q + C_{D_{r}}\cdot r + C_{D_{p}}\cdot p \\ + C_{Nd} &= C_{N_{q}}\cdot q + C_{N_{r}}\cdot r + C_{N_{p}}\cdot p \\ + C_{Yd} &= C_{Y_{q}}\cdot q + C_{Y_{r}}\cdot r + C_{Y_{p}}\cdot p \\ + C_{Ad} &= C_{A_{q}}\cdot q + C_{A_{r}}\cdot r + C_{A_{p}}\cdot p \\ C_{md} &= C_{m_{q}}\cdot q + C_{m_{r}}\cdot r + C_{m_{p}}\cdot p \\ C_{nd} &= C_{n_{q}}\cdot q + C_{n_{r}}\cdot r + C_{n_{p}}\cdot p \\ - C_{ld} &= C_{l_{q}}\cdot q + C_{l_{r}}\cdot r + C_{l_{p}}\cdot p + C_{ld} &= C_{l_{q}}\cdot q + C_{l_{r}}\cdot r + C_{l_{p}}\cdot p \end{aligned} -The forces and moments are then calculated as: +The body-frame forces and moments then follow, the damping terms scaled by the +reduced-rate factor :math:`\frac{L_{ref}}{2V}`: .. math:: \begin{aligned} - L &= \overline{q}\cdot A_{ref}\cdot C_{Lf} + \overline{q}\cdot A_{ref}\cdot \frac{L_{ref}}{2V} C_{Ld} \\ - Q &= \overline{q}\cdot A_{ref}\cdot C_{Qf} + \overline{q}\cdot A_{ref}\cdot \frac{L_{ref}}{2V} C_{Qd} \\ - D &= \overline{q}\cdot A_{ref}\cdot C_{Df} + \overline{q}\cdot A_{ref}\cdot \frac{L_{ref}}{2V} C_{Dd} \\ + N &= \overline{q}\cdot A_{ref}\cdot C_{Nf} + \overline{q}\cdot A_{ref}\cdot \frac{L_{ref}}{2V} C_{Nd} \\ + Y &= \overline{q}\cdot A_{ref}\cdot C_{Yf} + \overline{q}\cdot A_{ref}\cdot \frac{L_{ref}}{2V} C_{Yd} \\ + A &= \overline{q}\cdot A_{ref}\cdot C_{Af} + \overline{q}\cdot A_{ref}\cdot \frac{L_{ref}}{2V} C_{Ad} \\ M_{m} &= \overline{q}\cdot A_{ref}\cdot L_{ref}\cdot C_{mf} + \overline{q}\cdot A_{ref}\cdot L_{ref}\cdot \frac{L_{ref}}{2V} C_{md} \\ M_{n} &= \overline{q}\cdot A_{ref}\cdot L_{ref}\cdot C_{nf} + \overline{q}\cdot A_{ref}\cdot L_{ref}\cdot \frac{L_{ref}}{2V} C_{nd} \\ M_{l} &= \overline{q}\cdot A_{ref}\cdot L_{ref}\cdot C_{lf} + \overline{q}\cdot A_{ref}\cdot L_{ref}\cdot \frac{L_{ref}}{2V} C_{ld} \end{aligned} -The linear generic surface is defined very similarly to the generic surface. -The coefficients are defined in the same way, but with the addition of the -derivative values. +Defining a linear generic surface +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -An example of a linear generic surface defined with **all** the coefficients is -shown below: +A linear generic surface takes the **same parameters** as +:class:`rocketpy.GenericSurface`, only the +``coefficients`` dictionary is different. Each key follows the pattern +``_``. For example ``cN_alpha`` is +:math:`C_{N\alpha}`, ``cm_q`` is :math:`C_{m_q}`, and ``cN_0`` is the constant +term :math:`C_{N0}`. Any term you omit is zero. + +.. note:: + The derivative names carry the force frame: ``cN_alpha``, ``cY_beta``, ... + in the body frame versus ``cL_alpha``, ``cQ_beta``, ... in the wind frame. + ``force_convention`` selects the frame just as it does for + :class:`rocketpy.GenericSurface`. .. seealso:: - For more information on class initialization, see + For more information on class initialization, see :class:`rocketpy.LinearGenericSurface.__init__` +An example defining **all** the coefficient derivatives: + .. code-block:: python - + from rocketpy import LinearGenericSurface linear_generic_surface = LinearGenericSurface( reference_area=np.pi * 0.0635**2, reference_length=2 * 0.0635, coefficients={ - "cL_0": "cL_0.csv", - "cL_alpha": "cL_alpha.csv", - "cL_beta": "cL_beta.csv", - "cL_Ma": "cL_Ma.csv", - "cL_Re": "cL_Re.csv", - "cL_q": "cL_q.csv", - "cL_r": "cL_r.csv", - "cL_p": "cL_p.csv", - "cQ_0": "cQ_0.csv", - "cQ_alpha": "cQ_alpha.csv", - "cQ_beta": "cQ_beta.csv", - "cQ_Ma": "cQ_Ma.csv", - "cQ_Re": "cQ_Re.csv", - "cQ_q": "cQ_q.csv", - "cQ_r": "cQ_r.csv", - "cQ_p": "cQ_p.csv", - "cD_0": "cD_0.csv", - "cD_alpha": "cD_alpha.csv", - "cD_beta": "cD_beta.csv", - "cD_Ma": "cD_Ma.csv", - "cD_Re": "cD_Re.csv", - "cD_q": "cD_q.csv", - "cD_r": "cD_r.csv", - "cD_p": "cD_p.csv", + "cN_0": "cN_0.csv", + "cN_alpha": "cN_alpha.csv", + "cN_beta": "cN_beta.csv", + "cN_Ma": "cN_Ma.csv", + "cN_Re": "cN_Re.csv", + "cN_q": "cN_q.csv", + "cN_r": "cN_r.csv", + "cN_p": "cN_p.csv", + "cY_0": "cY_0.csv", + "cY_alpha": "cY_alpha.csv", + "cY_beta": "cY_beta.csv", + "cY_Ma": "cY_Ma.csv", + "cY_Re": "cY_Re.csv", + "cY_q": "cY_q.csv", + "cY_r": "cY_r.csv", + "cY_p": "cY_p.csv", + "cA_0": "cA_0.csv", + "cA_alpha": "cA_alpha.csv", + "cA_beta": "cA_beta.csv", + "cA_Ma": "cA_Ma.csv", + "cA_Re": "cA_Re.csv", + "cA_q": "cA_q.csv", + "cA_r": "cA_r.csv", + "cA_p": "cA_p.csv", "cm_0": "cm_0.csv", "cm_alpha": "cm_alpha.csv", "cm_beta": "cm_beta.csv", @@ -537,9 +577,8 @@ When a coefficient is provided as tabulated data (a ``.csv`` file or a list of points), RocketPy stores it as a :class:`rocketpy.Function` and must decide two things: how to **interpolate** *between* the tabulated points, and how to **extrapolate** *outside* the tabulated range. Both :class:`rocketpy.GenericSurface` -and :class:`rocketpy.LinearGenericSurface` (and -:class:`rocketpy.ControllableGenericSurface`) expose these as the -``interpolation`` and ``extrapolation`` arguments. +and :class:`rocketpy.LinearGenericSurface` expose these as the ``interpolation`` +and ``extrapolation`` arguments. .. note:: Interpolation and extrapolation only apply to **tabulated** coefficients. @@ -562,31 +601,30 @@ Each argument accepts either: reference_area=np.pi * radius**2, reference_length=2 * radius, coefficients={ - "cD": "cD.csv", - "cL": "cL.csv", + "cA": "cA.csv", + "cN": "cN.csv", }, # A single method applied to every coefficient: extrapolation="constant", # ... or per coefficient (unlisted ones keep the default): - interpolation={"cD": "linear", "cL": "akima"}, + interpolation={"cA": "linear", "cN": "akima"}, ) Choosing an interpolation method -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Interpolation controls the behavior *between* tabulated points. For 1-D tables the options are ``"linear"``, ``"akima"``, ``"spline"`` and ``"polynomial"``. - ``"linear"`` (**default**) is the safe choice. It never overshoots and introduces no spurious oscillations, which matters most across the - **transonic drag rise** (:math:`Ma \approx 0.8`–:math:`1.2`), where a spline - will oscillate and invent non-physical wiggles in :math:`C_D`. Prefer it for - coarse tables and for anything with a sharp feature. + **transonic drag rise** (:math:`Ma \approx 0.8`--:math:`1.2`), where a spline + will oscillate and invent non-physical wiggles in the axial coefficient + :math:`C_A`. Prefer it for coarse tables and for anything with a sharp feature. - ``"akima"`` gives continuous first derivatives (smoother :math:`C_{m_\alpha}`, cleaner stability curves) while resisting the overshoot of a natural cubic spline near kinks. It is the best "smooth" option for - **dense, smooth** data, such as lift/moment slopes in the attached-flow - region. + **dense, smooth** data. - ``"spline"`` produces the smoothest derivatives but overshoots near sharp features (stall, :math:`Ma = 1`). Use it only for genuinely smooth, well-resolved data. @@ -605,7 +643,7 @@ about smooth derivatives. raises. Choosing an extrapolation method -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Extrapolation controls the behavior *outside* the tabulated range. The options are ``"constant"``, ``"natural"`` and ``"zero"``. This choice matters more than @@ -621,7 +659,7 @@ an extreme condition beyond your data. but it introduces a discontinuity at the edge. - ``"natural"`` continues the fitted curve past the data. **Avoid this for tabulated coefficients**: extrapolating a linear or spline fit can send - :math:`C_D` or a moment slope to large, non-physical values right when the + :math:`C_A` or a moment slope to large, non-physical values right when the rocket is at an extreme condition. .. tip:: @@ -636,3 +674,103 @@ an extreme condition beyond your data. :meth:`rocketpy.Function.set_extrapolation` for the full list of methods. +.. _active_during: + +Activation Window +----------------- + +By default a surface produces aerodynamic force throughout the flight. The +``active_during`` argument restricts it to part of the flight. This is useful +for a surface that only exists (or only matters) during a phase. It is accepted +by both :class:`rocketpy.GenericSurface` and +:class:`rocketpy.LinearGenericSurface`, and accepts: + +- ``"always"`` (default): the surface always contributes force. +- ``"power_on"``: only while the motor is burning (up to burnout). +- ``"power_off"``: only after the motor has burned out. +- a callable ``active_during(t, flight)`` returning ``True`` when the surface is + active at time ``t`` (in seconds) of the given :class:`rocketpy.Flight`, for + any custom window. + +.. code-block:: python + + # base drag that only applies after burnout + base_drag = GenericSurface( + reference_area=rocket.area, + reference_length=2 * rocket.radius, + coefficients={"cA": 0.4}, # axial (drag) coefficient + active_during="power_off", + ) + +This is also how a full-vehicle model captures the powered/coasting drag +difference: build one ``"power_on"`` and one ``"power_off"`` surface and add them +together (see :ref:`fullbodyaerodynamics`). + + +.. _fullbodyaerodynamics: + +Whole-vehicle aerodynamics +-------------------------- + +Instead of (or in addition to) modelling each component, you can describe the +**entire rocket** with a single generic surface that already carries the whole +vehicle's coefficients, for example a set exported from CFD, a wind tunnel or +OpenRocket. + +Adding a prebuilt full-vehicle model +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +:meth:`rocketpy.Rocket.add_full_body_aerodynamics` adds such a surface (or a list +of them). Reference its coefficients to the rocket cross-section area and diameter +so it sums consistently with the rest of the vehicle: + +.. code-block:: python + + surface = GenericSurface( + reference_area=rocket.area, + reference_length=2 * rocket.radius, + coefficients={...}, + ) + rocket.add_full_body_aerodynamics(surface) + +Because it is just another aerodynamic surface, a full-vehicle model can be +**mixed** with modelled add-on surfaces (e.g. a measured body plus modelled +canards). Pass ``overwrite=True`` to make it the rocket's **only** aerodynamics: +every existing aerodynamic surface is removed and both built-in drag curves are +cleared, so the supplied surface(s) provide the complete force set. + +A rocket's drag differs between powered and coasting flight (the motor plume +lowers the base drag). To capture this, build two surfaces, set each one's +``active_during`` to ``"power_on"`` and ``"power_off"``, and pass them as a list; +each then produces force only during its phase. + +Extracting a rocket's coefficients +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +You can also collapses an assembled rocket into a single +stability-derivative model about its center of dry mass: + +- :meth:`rocketpy.Rocket.to_coefficients` returns the coefficient curves as a + dict split into ``"power_off"`` and ``"power_on"`` sets (only the drag differs + between them), each mapping a coefficient name to a :class:`rocketpy.Function` + of Mach. +- :meth:`rocketpy.Rocket.to_surface` wraps those into a ready-to-use pair of + :class:`rocketpy.LinearGenericSurface` objects, one gated to each motor phase -- + the inverse of :meth:`~rocketpy.Rocket.add_full_body_aerodynamics`. + +.. code-block:: python + + coefficients = rocket.to_coefficients() # {"power_off": {...}, "power_on": {...}} + surfaces = rocket.to_surface() # [power_off surface, power_on surface] + + # a bare rocket carrying only this pair flies the same as the full model + bare.add_full_body_aerodynamics(surfaces, overwrite=True) + +.. important:: + The extracted model is a **linear summary tabulated only against Mach**: the + derivatives are taken at zero angle of attack, zero sideslip and zero rates, + so incidence/rate nonlinearity, Reynolds dependence and control-surface + dependence are dropped. These are exactly the assumptions of the built-in + Barrowman surfaces (nose cones, fins, and tails), so a rocket built only from + those is reproduced exactly. + See :ref:`aero_cp_stability` for the extraction math and its limitations. diff --git a/docs/user/rocket/rocket.rst b/docs/user/rocket/rocket.rst index 956175409..7a7636815 100644 --- a/docs/user/rocket/rocket.rst +++ b/docs/user/rocket/rocket.rst @@ -16,6 +16,5 @@ Rocket Usage .. toctree:: :maxdepth: 3 :caption: Generic Surfaces and Custom Aerodynamic Coefficients - + Generic Surfaces and Custom Aerodynamic Coefficients - \ No newline at end of file From 32605737ad125cff22caca48ab4b80ac5cdfa692 Mon Sep 17 00:00:00 2001 From: MateusStano Date: Sat, 25 Jul 2026 09:59:54 -0300 Subject: [PATCH 11/22] TST: minor fixes --- rocketpy/_encoders.py | 3 + rocketpy/control/controller.py | 55 ++++++++++++++++++- rocketpy/plots/flight_plots.py | 11 +++- rocketpy/prints/flight_prints.py | 10 +++- rocketpy/rocket/rocket.py | 47 +++++++++++----- .../unit/test_rail_buttons_bending_moments.py | 15 +++-- 6 files changed, 121 insertions(+), 20 deletions(-) diff --git a/rocketpy/_encoders.py b/rocketpy/_encoders.py index e5bc99e29..5fc037a26 100644 --- a/rocketpy/_encoders.py +++ b/rocketpy/_encoders.py @@ -208,6 +208,9 @@ def set_minimal_flight_attributes(flight, obj): flight._controllers = getattr(flight.rocket, "_controllers", [])[:] flight.sensors = flight.rocket.sensors.get_components() flight.sensors_by_name = flight.rocket.sensors_by_name + # No simulation was run, so there is no recorded sensor data. Set an empty + # mapping so code that reads ``sensor_data`` behaves like a real flight. + flight.sensor_data = {} # TODO: custom_events are lost when loading from .rpy because they hold # user-defined callables that are not currently serialized. Add proper # serialization/deserialization for custom_events and restore them here diff --git a/rocketpy/control/controller.py b/rocketpy/control/controller.py index 204cc028a..10b24abe4 100644 --- a/rocketpy/control/controller.py +++ b/rocketpy/control/controller.py @@ -422,6 +422,16 @@ def to_dict(self, **kwargs): "sampling_rate": self.sampling_rate, "name": self.name, "controlled_objects_name": getattr(self, "controlled_objects_name", None), + # Hash(es) identifying the controlled object(s), so Rocket + # deserialization can reconnect the controller to the rocket's own + # reconstructed objects (see Rocket.from_dict). + "controlled_objects_hash": self._controlled_objects_hash(), + # Which expensive simulation values the controller reads; needed so a + # re-simulated flight computes them (e.g. state_history). Stored as a + # list because JSON has no set type. + "controller_needs": ( + None if self.controller_needs is None else list(self.controller_needs) + ), "context": self.context.copy(), # Preserve context state "enabled": self.enabled, "disable_on": disable_on, @@ -430,6 +440,24 @@ def to_dict(self, **kwargs): # object reference matching in Rocket deserialization } + def _controlled_objects_hash(self): + """Return the identity hash of the controlled object(s), matching the + shape of ``controlled_objects`` (a single hash for a single object, a + list of hashes for a list). These hashes match the ones the encoder + stores in each object's signature, letting Rocket deserialization find + the reconstructed objects. Returns ``None`` for anything unhashable.""" + + def safe_hash(obj): + try: + return hash(obj) + except TypeError: + return None + + controlled_objects = self.controlled_objects + if isinstance(controlled_objects, (list, tuple)): + return [safe_hash(obj) for obj in controlled_objects] + return safe_hash(controlled_objects) + @classmethod def from_dict(cls, data, controlled_objects=None): """Reconstruct controller from dictionary. @@ -455,6 +483,7 @@ def from_dict(cls, data, controlled_objects=None): enabled = data.get("enabled", True) disable_on = data.get("disable_on") enable_on = data.get("enable_on") + controller_needs = data.get("controller_needs") try: controller_function = from_hex_decode(controller_function) @@ -476,7 +505,7 @@ def from_dict(cls, data, controlled_objects=None): if controlled_objects is None: controlled_objects = [] - return cls( + controller = cls( controller_function=controller_function, controlled_objects=controlled_objects, sampling_rate=sampling_rate, @@ -486,4 +515,28 @@ def from_dict(cls, data, controlled_objects=None): enabled=enabled, disable_on=disable_on, enable_on=enable_on, + controller_needs=controller_needs, + ) + # Stash the serialized controlled-object hash(es) so Rocket.from_dict + # can reconnect the controller to the rocket's reconstructed objects. + controller._serialized_controlled_objects_hash = data.get( + "controlled_objects_hash" ) + return controller + + def rebind_controlled_objects(self, controlled_objects): + """Point the controller at reconstructed controlled object(s) and + refresh the callback name bindings. + + Used when a rocket is loaded from a file: the controller is rebuilt + without its controlled objects (they are separate objects in the saved + data), so this reconnects it to the rocket's own objects, ensuring the + controller mutates them rather than orphaned copies. + + Parameters + ---------- + controlled_objects : object or list of object + The reconstructed object(s) the controller should control. + """ + self.controlled_objects = controlled_objects + self._controlled_objects_bindings = self.__verify_controlled_objects_name() diff --git a/rocketpy/plots/flight_plots.py b/rocketpy/plots/flight_plots.py index 99e4d2fa8..f266732c6 100644 --- a/rocketpy/plots/flight_plots.py +++ b/rocketpy/plots/flight_plots.py @@ -1500,12 +1500,21 @@ def sensor_data(self, *, filename=None): print("No sensors were registered in this flight.") return + # A flight loaded from a file without being re-simulated has no recorded + # measurements, so fall back to an empty mapping instead of raising. + sensor_data = getattr(self.flight, "sensor_data", None) or {} + seen = [] for sensor in self.flight.sensors: if sensor in seen: # a multiply-added sensor is listed more than once continue seen.append(sensor) - measured_data = self.flight.sensor_data[sensor] + measured_data = sensor_data.get(sensor) + if not measured_data: + print( + f"\n\n{sensor.name}: no data available (flight was not simulated)." + ) + continue print(f"\n\n{sensor.name} Sensor Data\n") if filename is None: sensor.plots.all(data=measured_data) diff --git a/rocketpy/prints/flight_prints.py b/rocketpy/prints/flight_prints.py index 34e63a22b..0a48b0a82 100644 --- a/rocketpy/prints/flight_prints.py +++ b/rocketpy/prints/flight_prints.py @@ -266,12 +266,20 @@ def sensors(self): print("No sensors were registered.") return + # A flight loaded from a file without being re-simulated has no recorded + # measurements, so fall back to an empty mapping instead of raising. + sensor_data = getattr(self.flight, "sensor_data", None) or {} + for sensor in self.flight.sensors: sensor_name = sensor.name if sensor.name else "Unnamed Sensor" - measured_data = self.flight.sensor_data[sensor] print(f"Sensor: {sensor_name}") print(f"\tType: {sensor.__class__.__name__}") print(f"\tSampling Rate: {sensor.sampling_rate:.3f} Hz") + measured_data = sensor_data.get(sensor) + if not measured_data: + print("\tNo measurement data available (flight was not simulated).") + print() + continue print(f"\tMeasurements Recorded: {len(measured_data)}") sensor.prints.data_summary(data=measured_data) print() diff --git a/rocketpy/rocket/rocket.py b/rocketpy/rocket/rocket.py index 32f32a231..6367009d8 100644 --- a/rocketpy/rocket/rocket.py +++ b/rocketpy/rocket/rocket.py @@ -1035,7 +1035,9 @@ def _evaluate_is_incidence_linear(self): self, 0.0, probe_alpha, probe_mach, "yaw" ) else: - point_zero = neutral_point_and_slope(self, 0.0, 0.0, probe_mach, "pitch") + point_zero = neutral_point_and_slope( + self, 0.0, 0.0, probe_mach, "pitch" + ) point_five = neutral_point_and_slope( self, probe_alpha, 0.0, probe_mach, "pitch" ) @@ -2639,7 +2641,14 @@ def controller_wrapper(**kwargs): state = kwargs.get("state") state_history = kwargs.get("state_history") observed_variables = controller_context.get("observed_variables", []) - interactive_objects = kwargs.get("interactive_objects", air_brakes) + # Prefer the controller's live ``controlled_objects`` (set by the + # callback) over the captured ``air_brakes``: after a flight is + # loaded from a file the two differ, and only the former is the air + # brakes the rocket actually uses for drag. For a normal flight they + # are the same object, so this is unchanged behavior. + interactive_objects = kwargs.get( + "interactive_objects", kwargs.get("controlled_objects", air_brakes) + ) sensors = kwargs.get("sensors") environment = kwargs.get("environment") @@ -3048,22 +3057,34 @@ def from_dict(cls, data): rocket.air_brakes.append(air_brake) for controller in data["_controllers"]: - interactive_objects_hash = getattr(controller, "_interactive_objects_hash") - if interactive_objects_hash is not None: - is_iterable = isinstance(interactive_objects_hash, Iterable) - if not is_iterable: - interactive_objects_hash = [interactive_objects_hash] - for hash_ in interactive_objects_hash: + # Reconnect the controller to the rocket's own reconstructed objects + # by matching the hash(es) it stored for its controlled objects + # against the reconstructed objects (see _Controller.to_dict). + controlled_objects_hash = getattr( + controller, "_serialized_controlled_objects_hash", None + ) + if controlled_objects_hash is not None: + is_iterable = isinstance(controlled_objects_hash, Iterable) + hashes = ( + controlled_objects_hash + if is_iterable + else [controlled_objects_hash] + ) + found = [] + for hash_ in hashes: + if hash_ is None: # unhashable controlled object; cannot match + continue if (hashed_obj := find_obj_from_hash(data, hash_)) is not None: - if not is_iterable: - controller.interactive_objects = hashed_obj - else: - controller.interactive_objects.append(hashed_obj) + found.append(hashed_obj) else: warnings.warn( - "Could not find controller interactive objects." + "Could not find controller controlled objects. " "Deserialization will proceed, results may not be accurate." ) + if found: + controller.rebind_controlled_objects( + found if is_iterable else found[0] + ) rocket._add_controllers(controller) return rocket diff --git a/tests/unit/test_rail_buttons_bending_moments.py b/tests/unit/test_rail_buttons_bending_moments.py index b61720d3d..72717ecf9 100644 --- a/tests/unit/test_rail_buttons_bending_moments.py +++ b/tests/unit/test_rail_buttons_bending_moments.py @@ -147,12 +147,19 @@ def test_railbuttons_no_aero_contribution(): """Test RailButtons provide zero aerodynamic contributions.""" rb = RailButtons(buttons_distance=0.5) - rb.evaluate_center_of_pressure() + # Center of pressure sits at the surface origin. assert rb.cp == (0, 0, 0) - rb.evaluate_lift_coefficient() - assert rb.clalpha(1.0) == 0 # Zero lift derivative - assert rb.cl(0.1, 1.0) == 0 # Zero lift coefficient + # Every force and moment coefficient is identically zero. + full_state = (0.1, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0) # alpha, beta, mach, re, rates + for name in ("cN", "cY", "cA", "cm", "cn", "cl"): + coefficient = getattr(rb, name) + assert coefficient.is_zero + assert coefficient(*full_state) == 0 + + # Zero stability derivatives, so rail buttons never shift the static margin. + assert rb.cN_alpha(*full_state) == 0 + assert rb.cm_alpha(*full_state) == 0 def test_rail_button_bending_moments_prints(flight_calisto_robust, capsys): From 84d3b6a9f0b4a8074d764130dc3d189bdc402036 Mon Sep 17 00:00:00 2001 From: MateusStano Date: Sat, 25 Jul 2026 10:07:21 -0300 Subject: [PATCH 12/22] TST: minor fixes --- tests/unit/test_logging.py | 13 ++++++++++++- 1 file changed, 12 insertions(+), 1 deletion(-) diff --git a/tests/unit/test_logging.py b/tests/unit/test_logging.py index 37e69c36a..4069ae43c 100644 --- a/tests/unit/test_logging.py +++ b/tests/unit/test_logging.py @@ -10,9 +10,20 @@ @pytest.fixture(autouse=True) def _restore_logger_state(): - """Snapshot and restore the rocketpy logger so tests don't leak state.""" + """Snapshot and restore the rocketpy logger so tests don't leak state. + + Other tests (e.g. verbose ``Flight`` runs, which call ``enable_logging``) + may have attached the console handler to the shared logger and left it + there. Strip it before each test so these tests start from the library's + default state, then restore the original handlers afterwards. + """ saved_handlers = logger.handlers[:] saved_level = logger.level + logger.handlers[:] = [ + h + for h in logger.handlers + if getattr(h, "name", None) != "rocketpy_console_handler" + ] yield logger.handlers[:] = saved_handlers logger.setLevel(saved_level) From aac6a1bc6454c941e72f3343b625fc605c10a740 Mon Sep 17 00:00:00 2001 From: MateusStano Date: Sun, 4 Oct 2026 19:39:01 -0300 Subject: [PATCH 13/22] ENH: stability, generic surface and disturbance response rework - Rocket.stability_margin takes (mach, time) again, at zero angle of attack - aerodynamic center counts each surface's whole moment (pure couples, axial force at a sideways offset, canted individual fins) - Rocket.disturbance_response and Flight.disturbance_response, built on attitude oscillator helpers shared with the flight's dynamic stability - GenericSurface: data against alpha_total is split between the pitch and yaw planes from the variable name alone, also for lift and drag; force_convention is only "body" or "wind" - LinearGenericSurface(axisymmetric=True) fills in the yaw-plane derivatives; add_full_body_aerodynamics no longer takes symmetric - remove angular_position from GenericSurface --- requirements.txt | 2 +- rocketpy/__init__.py | 1 + rocketpy/control/controller.py | 2 +- rocketpy/mathutils/_regular_grid.py | 286 +++ rocketpy/mathutils/function.py | 421 ++--- rocketpy/plots/aero_surface_plots.py | 188 +- rocketpy/plots/flight_plots.py | 414 ++-- rocketpy/plots/rocket_plots.py | 36 +- rocketpy/prints/aero_surface_prints.py | 36 +- rocketpy/prints/flight_prints.py | 39 +- rocketpy/prints/rocket_prints.py | 37 +- rocketpy/rocket/__init__.py | 1 + rocketpy/rocket/_helpers.py | 1434 ++++++++++++++ .../rocket/aero_surface/_barrowman_surface.py | 135 +- rocketpy/rocket/aero_surface/_helpers.py | 314 ++++ .../rocket/aero_surface/aero_coefficient.py | 898 +++++---- rocketpy/rocket/aero_surface/air_brakes.py | 24 +- .../controllable_generic_surface.py | 293 +-- .../rocket/aero_surface/fins/_base_fin.py | 145 +- .../rocket/aero_surface/fins/_geometry.py | 14 +- .../aero_surface/fins/elliptical_fin.py | 50 +- .../aero_surface/fins/elliptical_fins.py | 49 +- rocketpy/rocket/aero_surface/fins/fin.py | 192 +- rocketpy/rocket/aero_surface/fins/fins.py | 131 +- .../rocket/aero_surface/fins/free_form_fin.py | 50 +- .../aero_surface/fins/free_form_fins.py | 49 +- .../aero_surface/fins/trapezoidal_fin.py | 59 +- .../aero_surface/fins/trapezoidal_fins.py | 63 +- .../rocket/aero_surface/generic_surface.py | 1350 +++++++++----- .../aero_surface/linear_generic_surface.py | 1038 +++++------ rocketpy/rocket/aero_surface/nose_cone.py | 61 +- rocketpy/rocket/aero_surface/tail.py | 52 +- rocketpy/rocket/components.py | 36 + rocketpy/rocket/helpers.py | 280 --- rocketpy/rocket/point_mass_rocket.py | 6 - rocketpy/rocket/rocket.py | 1658 ++++++++++------- rocketpy/simulation/events/event.py | 2 +- rocketpy/simulation/flight.py | 446 +++-- rocketpy/simulation/helpers/event_commands.py | 3 + .../simulation/helpers/flight_derivatives.py | 239 ++- rocketpy/simulation/monte_carlo.py | 8 + rocketpy/stochastic/stochastic_model.py | 4 +- rocketpy/stochastic/stochastic_rocket.py | 35 +- tests/integration/simulation/test_event.py | 2 +- tests/integration/simulation/test_flight.py | 6 +- .../simulation/test_flight_3dof.py | 26 +- tests/unit/mathutils/test_function.py | 101 +- tests/unit/mathutils/test_regular_grid.py | 342 ++++ .../aero_surface/test_aero_coefficient.py | 119 +- .../rocket/aero_surface/test_aero_surfaces.py | 27 + .../test_barrowman_generic_equivalence.py | 112 +- .../test_controllable_generic_surface.py | 41 + .../aero_surface/test_generic_surfaces.py | 921 ++++++++- .../aero_surface/test_individual_fins.py | 149 +- .../test_linear_generic_surfaces.py | 87 +- .../test_surface_coefficient_completeness.py | 2 +- .../aero_surface/test_surface_encoding.py | 144 ++ tests/unit/rocket/test_rocket.py | 389 +++- tests/unit/rocket/test_stability_rework.py | 1248 ++++++++++++- .../simulation/test_aerodynamic_drag_force.py | 204 ++ tests/unit/simulation/test_event.py | 4 +- tests/unit/simulation/test_flight.py | 289 ++- .../unit/stochastic/test_stochastic_rocket.py | 90 + 63 files changed, 11060 insertions(+), 3824 deletions(-) create mode 100644 rocketpy/mathutils/_regular_grid.py create mode 100644 rocketpy/rocket/_helpers.py create mode 100644 rocketpy/rocket/aero_surface/_helpers.py delete mode 100644 rocketpy/rocket/helpers.py create mode 100644 tests/unit/mathutils/test_regular_grid.py create mode 100644 tests/unit/rocket/aero_surface/test_surface_encoding.py create mode 100644 tests/unit/simulation/test_aerodynamic_drag_force.py diff --git a/requirements.txt b/requirements.txt index 4206c8c15..c7ea9c1a1 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,5 +1,5 @@ numpy>=1.13 -scipy>=1.13.0 # RegularGridInterpolator "pchip"/spline methods (Apr 2024) +scipy>=1.13.0 matplotlib>=3.9.0 # Released May 15th 2024 netCDF4>=1.6.4 requests diff --git a/rocketpy/__init__.py b/rocketpy/__init__.py index 42024fb9d..9baae40d0 100644 --- a/rocketpy/__init__.py +++ b/rocketpy/__init__.py @@ -27,6 +27,7 @@ ) from .plots.compare import Compare, CompareFlights from .rocket import ( + AeroCoefficient, AeroSurface, AirBrakes, Components, diff --git a/rocketpy/control/controller.py b/rocketpy/control/controller.py index 28031a8bf..4b58135a1 100644 --- a/rocketpy/control/controller.py +++ b/rocketpy/control/controller.py @@ -285,7 +285,7 @@ def return_log(self, value): def __verify_controlled_objects_name(self): """Validate controlled_objects_name and build callback bindings.""" if self.controlled_objects_name is None: - return None + return {} # nothing to bind by name single_name = isinstance(self.controlled_objects_name, str) list_names = isinstance(self.controlled_objects_name, (list, tuple)) diff --git a/rocketpy/mathutils/_regular_grid.py b/rocketpy/mathutils/_regular_grid.py new file mode 100644 index 000000000..e16ecf928 --- /dev/null +++ b/rocketpy/mathutils/_regular_grid.py @@ -0,0 +1,286 @@ +import warnings +from bisect import bisect_right + +import numpy as np +from scipy.interpolate import RegularGridInterpolator + +# The methods of SciPy's ``RegularGridInterpolator``, under the names a +# ``Function`` accepts. ``spline``, ``akima`` and ``polynomial`` are written for +# a single variable, so they are read as their closest counterparts on a grid. +GRID_METHODS = { + "linear": "linear", + "nearest": "nearest", + "slinear": "slinear", + "cubic": "cubic", + "quintic": "quintic", + "pchip": "pchip", + "spline": "cubic", + "akima": "pchip", + "polynomial": "cubic", +} +# Points each method needs along every axis. SciPy raises on a coarser grid. +GRID_MIN_POINTS = { + "nearest": 1, + "linear": 2, + "slinear": 2, + "pchip": 4, + "cubic": 4, + "quintic": 6, +} + + +class _RegularGrid: + """Values on a regular grid, and their interpolation. + + A table of points over two or more variables covers a regular grid when it + holds every combination of the values taken by each variable, exactly once. + Such a table can be interpolated one variable at a time, which is both + faster and more accurate than treating its points as scattered. The + ``Function`` class uses a ``_RegularGrid`` whenever its data allows it. + + Attributes + ---------- + axes : list of numpy.ndarray + Values taken by each variable, in increasing order. + values : numpy.ndarray + Value at each node, with one dimension per variable: ``values[i, j]`` + is the value at ``axes[0][i]`` and ``axes[1][j]``. + method : str + How values are interpolated between the nodes: ``"linear"``, + ``"nearest"``, ``"slinear"``, ``"cubic"``, ``"quintic"`` or ``"pchip"``. + extrapolation : str + What is returned outside the grid: ``"natural"`` continues the + interpolation, ``"constant"`` holds the value at the nearest edge and + ``"zero"`` returns 0. It may be changed at any time. + """ + + def __init__(self, axes, values, method=None, extrapolation="natural", name=None): + """Store the grid and prepare its interpolation. + + Parameters + ---------- + axes : list of array_like + Values taken by each variable, in increasing order, one sequence + per variable. Each needs at least two values. + values : array_like + Value at each node, with one dimension per variable: + ``values[i, j]`` is the value at ``axes[0][i]`` and ``axes[1][j]``. + method : str, optional + Interpolation asked for, under any name a ``Function`` accepts: + ``"linear"``, ``"nearest"``, ``"slinear"``, ``"cubic"``, + ``"quintic"``, ``"pchip"``, or ``"spline"``, ``"akima"`` and + ``"polynomial"``, which are read as ``"cubic"``, ``"pchip"`` and + ``"cubic"``. An unknown name, or a method needing more points + along an axis than the grid has (4 for ``"cubic"`` and + ``"pchip"``, 6 for ``"quintic"``), gives ``"linear"`` with a + warning. Default is ``"linear"``. + extrapolation : str, optional + ``"natural"``, ``"constant"`` or ``"zero"``, see the class + attributes. Default is ``"natural"``. + name : str, optional + What the data is called, used in the warnings only. + """ + self.axes = [np.asarray(axis, dtype=np.float64) for axis in axes] + self.values = np.asarray(values, dtype=np.float64) + self.extrapolation = extrapolation + self._scipy_interpolator = None # built when first needed + + of_name = f" of '{name}'" if isinstance(name, str) else "" + self.method = GRID_METHODS.get((method or "linear").lower()) + if self.method is None: + warnings.warn( + f"Interpolation method set to 'linear' because the {method} " + f"method is not supported for the regular grid{of_name}. The " + "supported methods are 'linear', 'nearest', 'slinear', 'cubic', " + "'quintic' and 'pchip'." + ) + self.method = "linear" + smallest_axis = min(len(axis) for axis in self.axes) + if smallest_axis < GRID_MIN_POINTS[self.method]: + warnings.warn( + f"Interpolation method '{self.method}' needs at least " + f"{GRID_MIN_POINTS[self.method]} points per axis, but the coarsest " + f"axis{of_name} has {smallest_axis}; falling back to 'linear'." + ) + self.method = "linear" + + # A simulation asks for one point at a time, which SciPy is slow at. + # For that case keep plain lists, faster to index one number at a time + # than arrays: the axes, the values in a flat list, the step in that + # list between neighbors along each axis, and, for each corner of a + # cell, whether it lies on the lower (0) or upper (1) grid line of + # each variable together with its offset in the flat list. + strides = [int(np.prod(self.values.shape[i + 1 :])) for i in range(len(axes))] + corners = [ + (corner, sum(bit * stride for bit, stride in zip(corner, strides))) + for corner in np.ndindex(*([2] * len(axes))) + ] + self._lookup = ( + [axis.tolist() for axis in self.axes], + strides, + corners, + self.values.ravel().tolist(), + ) + + @classmethod + def from_points(cls, points, method=None, extrapolation="natural", name=None): + """Read a table of points as a regular grid, if it is one. + + Parameters + ---------- + points : numpy.ndarray + One row per point: the value of each variable, then the value of + the data there. The rows may come in any order. + method, extrapolation, name + See ``__init__``. + + Returns + ------- + _RegularGrid or None + The grid, or ``None`` when the points are not to be read as one: + fewer than two variables, fewer than two values of any of them, a + combination missing or repeated, or a ``method`` written for + scattered points (``"shepard"`` or ``"rbf"``). + """ + if method is not None and method.lower() in ("shepard", "rbf"): + return None + coordinates = points[:, :-1] + if coordinates.shape[1] < 2 or not np.isfinite(coordinates).all(): + return None + # The values each variable takes, and where each point sits along them + axes, indices = zip( + *(np.unique(column, return_inverse=True) for column in coordinates.T) + ) + shape = tuple(len(axis) for axis in axes) + if min(shape) < 2 or np.prod(shape) != len(points): + return None + # There are as many points as nodes, so a node left empty means that + # another one is repeated + values = np.empty(shape) + filled = np.zeros(shape, dtype=bool) + values[indices] = points[:, -1] + filled[indices] = True + if not filled.all(): + return None + return cls(axes, values, method, extrapolation, name) + + @staticmethod + def is_axes_and_values(source): + """Check whether ``source`` is given as a grid, ``(axes, values)``. + + That is one sequence of values for each variable, then the data on the + grid they span. A table of points never matches, since its rows hold + numbers where the axes hold sequences. + + Parameters + ---------- + source : object + The data to check. + + Returns + ------- + bool + True if ``source`` is an ``(axes, values)`` pair. + """ + return ( + isinstance(source, (tuple, list)) + and len(source) == 2 + and isinstance(source[0], (tuple, list)) + and len(source[0]) > 0 + and all(np.ndim(axis) == 1 for axis in source[0]) + ) + + @staticmethod + def points_from_axes(axes, values): + """Write values given on a grid as a table of points. + + Parameters + ---------- + axes : sequence of array_like + Values taken by each variable, in any order, without repetition. + values : array_like + Value at each node, with one dimension per variable. + + Returns + ------- + numpy.ndarray + One row per node: the value of each variable, then the value of + the data there. + """ + axes = [np.asarray(axis, dtype=np.float64) for axis in axes] + values = np.asarray(values, dtype=np.float64) + if len(axes) != values.ndim: + raise ValueError( + f"Number of axes ({len(axes)}) must match grid_data dimensions " + f"({values.ndim})." + ) + for i, axis in enumerate(axes): + if axis.size != values.shape[i]: + raise ValueError( + f"Axis {i} has {axis.size} points but grid dimension {i} has " + f"{values.shape[i]} points." + ) + if len(np.unique(axis)) != axis.size: + raise ValueError(f"Axis {i} has repeated coordinates.") + mesh = np.meshgrid(*axes, indexing="ij") + return np.column_stack([m.ravel() for m in mesh] + [values.ravel()]) + + def evaluate(self, *args): + """Evaluate the grid at one point or at several. + + Parameters + ---------- + args : float or array_like + Value of each variable: numbers for a single point, or sequences + of the same length for several. + + Returns + ------- + float or numpy.ndarray + A number for a single point, an array otherwise. + """ + extrapolation = self.extrapolation + single_point = not isinstance(args[0], (np.ndarray, list, tuple)) + + if single_point and self.method == "linear": + # Find the cell around the point and blend the values at its + # corners, which gives the values of SciPy's interpolator + axes, strides, corners, values = self._lookup + base = 0 + fractions = [] + for x, axis, stride in zip(args, axes, strides): + i = min(max(bisect_right(axis, x) - 1, 0), len(axis) - 2) + fraction = (x - axis[i]) / (axis[i + 1] - axis[i]) + if fraction < 0 or fraction > 1: # outside the grid + if extrapolation == "zero": + return 0.0 + if extrapolation == "constant": + fraction = 0.0 if fraction < 0 else 1.0 + base += i * stride + fractions.append(fraction) + result = 0.0 + for corner, offset in corners: + weight = 1.0 + for bit, fraction in zip(corner, fractions): + weight *= fraction if bit else 1.0 - fraction + result += weight * values[base + offset] + return result + + if self._scipy_interpolator is None: + self._scipy_interpolator = RegularGridInterpolator( + self.axes, + self.values, + method=self.method, + bounds_error=False, + fill_value=None, # continue the interpolation outside the grid + ) + points = np.column_stack(args).astype(np.float64) + lower = [axis[0] for axis in self.axes] + upper = [axis[-1] for axis in self.axes] + if extrapolation == "natural": + result = self._scipy_interpolator(points) + else: + result = self._scipy_interpolator(np.clip(points, lower, upper)) + if extrapolation == "zero": + result[((points < lower) | (points > upper)).any(axis=1)] = 0.0 + return float(result[0]) if len(result) == 1 else result diff --git a/rocketpy/mathutils/function.py b/rocketpy/mathutils/function.py index 2787f569e..503d57b47 100644 --- a/rocketpy/mathutils/function.py +++ b/rocketpy/mathutils/function.py @@ -23,9 +23,9 @@ LinearNDInterpolator, NearestNDInterpolator, RBFInterpolator, - RegularGridInterpolator, ) +from rocketpy.mathutils._regular_grid import _RegularGrid from rocketpy.plots.plot_helpers import show_or_save_plot from rocketpy.tools import deprecated, from_hex_decode, to_hex_encode @@ -37,35 +37,8 @@ "spline": 3, "shepard": 4, "rbf": 5, - "regular_grid": 6, } EXTRAPOLATION_TYPES = {"zero": 0, "natural": 1, "constant": 2} -# Maps a requested interpolation name onto a scipy ``RegularGridInterpolator`` -# ``method`` for gridded (N-D Cartesian) data. The 1-D-only names ``spline`` and -# ``akima`` fall back to their closest grid analogs (``cubic`` and the -# shape-preserving ``pchip``); anything unrecognized defaults to ``linear``. -REGULAR_GRID_METHODS = { - "linear": "linear", - "nearest": "nearest", - "slinear": "slinear", - "cubic": "cubic", - "quintic": "quintic", - "pchip": "pchip", - "spline": "cubic", - "akima": "pchip", - "polynomial": "cubic", -} -# Minimum points per axis required by each ``RegularGridInterpolator`` method. -# A grid with fewer samples on any axis cannot use the higher-order methods, so -# the caller falls back to linear rather than letting SciPy raise mid-build. -REGULAR_GRID_MIN_POINTS = { - "nearest": 1, - "linear": 2, - "slinear": 2, - "pchip": 2, - "cubic": 4, - "quintic": 6, -} class SourceType(Enum): @@ -127,10 +100,12 @@ def __init__( interpolation : string, optional Interpolation method to be used if source type is ndarray. For 1-D functions, linear, polynomial, akima and spline are - supported. For N-D functions, linear, shepard, rbf and - regular_grid are supported. - Default for 1-D functions is spline and for N-D functions is - shepard. + supported. For N-D functions, linear, shepard and rbf are + supported. N-D data on a regular grid is interpolated on the + grid, where linear, nearest, slinear, cubic, quintic and pchip + are supported, unless shepard or rbf is asked for. + Default for 1-D functions is spline, for N-D functions is + shepard and for N-D data on a regular grid is linear. extrapolation : string, optional Extrapolation method to be used if source type is ndarray. Options are 'natural', which keeps interpolation, 'constant', @@ -182,105 +157,38 @@ def __init__( self.set_title(self.title) @classmethod + @deprecated( + reason="The `Function.from_regular_grid_csv` method is deprecated. A " + "Function now checks on its own whether the points of a table cover a " + "regular grid and, if so, interpolates on it, so a separate method for " + "CSV files is no longer needed", + version="v1.16.0", + alternative="Function(csv_source, inputs=variable_names, " + "outputs=coeff_name, interpolation='linear', " + "extrapolation=extrapolation)` and check `Function.is_regular_grid", + ) def from_regular_grid_csv( - cls, - csv_source, - variable_names, - coeff_name, - extrapolation, - interpolation="linear", + cls, csv_source, variable_names, coeff_name, extrapolation ): - """Create a regular-grid Function from CSV samples when possible. - - Parameters - ---------- - csv_source : str - Path to the CSV file. - variable_names : list[str] - Ordered independent variable names present in the CSV. - coeff_name : str - Name of the output coefficient. - extrapolation : str - Extrapolation method passed to the Function constructor. - interpolation : str, optional - Requested interpolation. Mapped onto a - :class:`scipy.interpolate.RegularGridInterpolator` ``method`` via - :data:`REGULAR_GRID_METHODS` (e.g. ``"spline"`` -> ``"cubic"``, - ``"akima"`` -> ``"pchip"``); unrecognized names fall back to - ``"linear"``. Smooth methods require enough points per axis - (``"cubic"`` needs at least 4), otherwise SciPy raises. Default - ``"linear"``. + """Create a Function from a CSV file if its points cover a regular + grid. Deprecated: ``Function(csv_source)`` now finds the grid itself. Returns ------- Function or None - A ``Function`` configured with ``regular_grid`` interpolation when - the CSV forms a strict Cartesian grid, otherwise ``None``. + The Function, or ``None`` when the points do not cover a grid. """ try: - data = np.loadtxt(csv_source, delimiter=",", skiprows=1, dtype=np.float64) + function = cls( + csv_source, + inputs=list(variable_names), + outputs=[coeff_name], + interpolation="linear", + extrapolation=extrapolation, + ) except (OSError, ValueError): return None - - data = np.atleast_2d(data) - expected_columns = len(variable_names) + 1 - if data.shape[1] != expected_columns: - return None - - coordinates = data[:, :-1] - values = data[:, -1] - - if np.unique(coordinates, axis=0).shape[0] != coordinates.shape[0]: - return None - - axes = [np.unique(coordinates[:, i]) for i in range(len(variable_names))] - expected_size = int(np.prod([axis.size for axis in axes])) - if expected_size != coordinates.shape[0]: - return None - - sorting_keys = [ - coordinates[:, i] for i in range(len(variable_names) - 1, -1, -1) - ] - sorted_indices = np.lexsort(tuple(sorting_keys)) - sorted_coordinates = coordinates[sorted_indices] - sorted_values = values[sorted_indices] - - expected_coordinates = np.column_stack( - [axis_values.ravel() for axis_values in np.meshgrid(*axes, indexing="ij")] - ) - if not np.allclose( - sorted_coordinates, expected_coordinates, rtol=0, atol=1e-12 - ): - return None - - grid_data = sorted_values.reshape(tuple(axis.size for axis in axes)) - grid_function = cls( - (axes, grid_data), - inputs=variable_names, - outputs=[coeff_name], - interpolation="regular_grid", - extrapolation=extrapolation, - ) - # Honor the requested interpolation on the grid by rebuilding the - # interpolator/extrapolator with the mapped scipy ``method``. The - # constructor above always builds the default ("linear"); only rebuild - # when a different method was asked for. - grid_method = REGULAR_GRID_METHODS.get(interpolation, "linear") - smallest_axis = min(axis.size for axis in axes) - if smallest_axis < REGULAR_GRID_MIN_POINTS.get(grid_method, 2): - warnings.warn( - f"Grid interpolation method '{grid_method}' needs at least " - f"{REGULAR_GRID_MIN_POINTS[grid_method]} points per axis, but the " - f"coarsest axis of '{coeff_name}' has {smallest_axis}; falling " - "back to 'linear'.", - UserWarning, - ) - grid_method = "linear" - if grid_method != "linear": - grid_function._grid_method = grid_method - grid_function.set_interpolation("regular_grid") - grid_function.set_extrapolation(grid_function.get_extrapolation_method()) - return grid_function + return function if function.is_regular_grid else None # Define all set methods def set_inputs(self, inputs): @@ -355,6 +263,8 @@ def set_source(self, source): # pylint: disable=too-many-statements self : Function Returns the Function instance with the new source set. """ + # Forget the regular grid of the previous source + self._grid = None source = self.__validate_source(source) # Handle callable source or number source @@ -433,22 +343,60 @@ def set_interpolation(self, method="spline"): ---------- method : string, optional Interpolation method to be used if source type is ndarray. - For 1-D functions, linear, polynomial, akima and spline is - supported. For N-D functions, linear, shepard, rbf and - regular_grid are supported. - Default for 1-D functions is spline and for N-D functions is - shepard. + For 1-D functions, linear, polynomial, akima and spline are + supported. For N-D functions, linear, shepard and rbf are + supported. N-D data on a regular grid is interpolated on the + grid, where linear, nearest, slinear, cubic, quintic and pchip + are supported, unless shepard or rbf is asked for. + Default for 1-D functions is spline, for N-D functions is + shepard and for N-D data on a regular grid is linear. Returns ------- self : Function """ if self._source_type is SourceType.ARRAY: + if method == "regular_grid": + warnings.warn( + "interpolation='regular_grid' is deprecated and will be " + "removed in v1.16.0. A Function now detects a regular grid " + "in its data automatically. Use interpolation='linear' (or " + "'cubic', 'pchip', ...) instead.", + DeprecationWarning, + stacklevel=2, + ) + method = None + was_grid = getattr(self, "_grid", None) is not None + name = self.__outputs__ + if isinstance(name, (list, tuple)): + name = name[0] if name else None + self._grid = _RegularGrid.from_points( + self.source, method, self.__extrapolation__ or "natural", name + ) + if self._grid is not None: + self.__interpolation__ = self._grid.method + self._coeffs = [] + # The grid interpolates and extrapolates on its own + self.get_value_opt = self._grid.evaluate + return self self.__interpolation__ = self.__validate_interpolation(method) self.__update_interpolation_coefficients(self.__interpolation__) self.__set_interpolation_func() + if was_grid: + # The data stops being read as a grid: evaluate it as points + self.get_value_opt = self.__get_value_opt_nd + if self.__extrapolation__ in EXTRAPOLATION_TYPES: + self.__set_extrapolation_func() return self + @property + def is_regular_grid(self): + """bool: Whether the data of the Function covers a regular grid and is + interpolated on it. That is the case for a table of two or more inputs + holding every combination of the values of each input exactly once, + unless the 'shepard' or 'rbf' interpolation was asked for.""" + return getattr(self, "_grid", None) is not None + def __update_interpolation_coefficients(self, method): """Update interpolation coefficients for the given method.""" # Spline, akima and polynomial need data processing @@ -489,90 +437,12 @@ def set_extrapolation(self, method="constant"): """ if self._source_type is SourceType.ARRAY: self.__extrapolation__ = self.__validate_extrapolation(method) - self.__set_extrapolation_func() + if self.is_regular_grid: + self._grid.extrapolation = self.__extrapolation__ + else: + self.__set_extrapolation_func() return self - def __process_grid_source(self, source): - """Validate and process a ``(axes, grid_data)`` tuple into a flat - scatter :class:`numpy.ndarray` ready for :meth:`set_source`. - - As a side-effect, stores ``self._grid_axes`` and ``self._grid_data`` - so that :meth:`__set_interpolation_func` (case 6) and - :meth:`__set_extrapolation_func` can build the - :class:`~scipy.interpolate.RegularGridInterpolator`. - - Parameters - ---------- - source : tuple - A 2-element tuple ``(axes, grid_data)`` where *axes* is a list of - 1-D arrays sorted in ascending order (one per input dimension) and - *grid_data* is a matching N-dimensional :class:`numpy.ndarray` of - values. - - Returns - ------- - flat_source : numpy.ndarray - Array of shape ``(n_points, n_dims + 1)`` with all grid points - unrolled in row-major (C) order. - - Raises - ------ - ValueError - If *source* is not a 2-element tuple, if the number of axes - mismatches the grid dimensionality, or if an axis length mismatches - the corresponding grid dimension. - """ - if not (isinstance(source, Iterable) and len(source) == 2): - raise ValueError( - "For 'regular_grid' interpolation, source must be a " - "(axes, grid_data) tuple where axes is a list of 1-D arrays " - "and grid_data is a matching N-dimensional ndarray." - ) - - raw_axes, raw_data = source - if not isinstance(raw_axes, Iterable): - raise ValueError( - "The first element of the source tuple must be a list or tuple " - "of 1-D arrays representing the grid axes." - ) - - axes = [np.asarray(ax) for ax in raw_axes] - grid_data = np.asarray(raw_data, dtype=np.float64) - - if len(axes) != grid_data.ndim: - raise ValueError( - f"Number of axes ({len(axes)}) must match grid_data dimensions " - f"({grid_data.ndim})." - ) - for i, ax in enumerate(axes): - if len(ax) != grid_data.shape[i]: - raise ValueError( - f"Axis {i} has {len(ax)} points but grid dimension {i} has " - f"{grid_data.shape[i]} points." - ) - if not np.all(np.diff(ax) > 0): - # RegularGridInterpolator requires strictly ascending axes. Sort - # this axis (and reorder the grid data along it) so descending or - # shuffled inputs are accepted; repeated coordinates cannot form - # a regular grid and are rejected with a clear error rather than - # a cryptic SciPy failure. - order = np.argsort(ax, kind="stable") - ax = ax[order] - grid_data = np.take(grid_data, order, axis=i) - axes[i] = ax - if not np.all(np.diff(ax) > 0): - raise ValueError( - f"Axis {i} has repeated coordinates; a regular grid " - "requires strictly increasing values along each axis." - ) - - self._grid_axes = axes - self._grid_data = grid_data - - mesh = np.meshgrid(*axes, indexing="ij") - domain_points = np.column_stack([m.ravel() for m in mesh]) - return np.column_stack([domain_points, grid_data.ravel()]) - def __set_interpolation_func(self): # pylint: disable=too-many-statements """Defines interpolation function used by the Function. Each interpolation method has its own function`. @@ -658,27 +528,6 @@ def rbf_interpolation(x, x_min, x_max, x_data, y_data, coeffs): # pylint: disab self._interpolation_func = rbf_interpolation - case 6: # regular_grid (RegularGridInterpolator) - if not hasattr(self, "_grid_axes") or not hasattr(self, "_grid_data"): - raise AttributeError( - "The 'regular_grid' interpolation requires '_grid_axes' and " - "'_grid_data' to be set on the Function instance before calling " - "set_interpolation('regular_grid')." - ) - grid_interpolator = RegularGridInterpolator( - self._grid_axes, - self._grid_data, - method=getattr(self, "_grid_method", "linear"), - bounds_error=True, - ) - # Store so extrapolation funcs can reuse it - self._grid_interpolator = grid_interpolator - - def grid_interpolation(x, x_min, x_max, x_data, y_data, coeffs): # pylint: disable=unused-argument - return grid_interpolator(x) - - self._interpolation_func = grid_interpolation - case _: raise ValueError( f"Interpolation {interpolation} method not recognized." @@ -788,20 +637,6 @@ def natural_extrapolation( # pylint: disable=function-redefined ): # pylint: disable=unused-argument return interpolator(x) - case 6: # regular_grid - grid_extrapolator = RegularGridInterpolator( - self._grid_axes, - self._grid_data, - method=getattr(self, "_grid_method", "linear"), - bounds_error=False, - fill_value=None, # extrapolation beyond edges - ) - - def natural_extrapolation( # pylint: disable=function-redefined - x, x_min, x_max, x_data, y_data, coeffs - ): # pylint: disable=unused-argument - return grid_extrapolator(x) - case _: raise ValueError( f"Natural extrapolation not defined for {interpolation}." @@ -814,19 +649,6 @@ def natural_extrapolation( # pylint: disable=function-redefined def constant_extrapolation(x, x_min, x_max, x_data, y_data, coeffs): # pylint: disable=unused-argument return y_data[0] if x < x_min else y_data[-1] - elif self.__interpolation__ == "regular_grid": - grid_axes = self._grid_axes - grid_interpolator_const = self._grid_interpolator - - def constant_extrapolation(x, x_min, x_max, x_data, y_data, coeffs): # pylint: disable=unused-argument - # Clamp each coordinate to its axis bounds, then interpolate - x_clamped = np.copy(x) - for i, axis in enumerate(grid_axes): - x_clamped[:, i] = np.clip( - x_clamped[:, i], axis[0], axis[-1] - ) - return grid_interpolator_const(x_clamped) - else: extrapolator = NearestNDInterpolator(self._domain, self._image) @@ -848,6 +670,8 @@ def set_get_value_opt(self): """ if self._source_type is SourceType.CALLABLE: self.get_value_opt = self.source + elif self.is_regular_grid: + self.get_value_opt = self._grid.evaluate elif self.__dom_dim__ == 1: self.get_value_opt = self.__get_value_opt_1d elif self.__dom_dim__ > 1: @@ -1030,9 +854,10 @@ def __discretize_2d_function(self, func, lower, upper, samples): # Evaluate function at all mesh nodes and convert it to matrix zs = np.array(func.get_value(xs, ys)) - func.set_source(np.concatenate(([xs], [ys], [zs])).transpose()) - func.__interpolation__ = "shepard" + # The nodes are a regular mesh, which is interpolated as such + func.__interpolation__ = None func.__extrapolation__ = "natural" + func.set_source(np.concatenate(([xs], [ys], [zs])).transpose()) def set_discrete( self, @@ -1064,10 +889,12 @@ def set_discrete( interpolation : string Interpolation method to be used if source type is ndarray. For 1-D functions, linear, polynomial, akima and spline are - supported. For N-D functions, linear, shepard, rbf and - regular_grid are supported. - Default for 1-D functions is spline and for N-D functions is - shepard. + supported. For N-D functions, linear, shepard and rbf are + supported. N-D data on a regular grid is interpolated on the + grid, where linear, nearest, slinear, cubic, quintic and pchip + are supported, unless shepard or rbf is asked for. + Default for 1-D functions is spline, for N-D functions is + shepard and for N-D data on a regular grid is linear. extrapolation : string, optional Extrapolation method to be used if source type is ndarray. Options are 'natural', which keeps interpolation, 'constant', @@ -4041,6 +3868,9 @@ def __validate_source(self, source): # pylint: disable=too-many-statements or a callable function. """ if isinstance(source, Function): + if source.is_regular_grid and self.__interpolation__ is None: + # Keep the method of a copied grid, not only its points + self.__interpolation__ = source.get_interpolation_method() return source.get_source() if isinstance(source, (str, Path)): @@ -4065,10 +3895,15 @@ def __validate_source(self, source): # pylint: disable=too-many-statements ) from e if isinstance(source, Iterable): - # Triggers an error if source is not a list of numbers - if self.__interpolation__ == "regular_grid": - return self.__process_grid_source(source) + if ( + self.__interpolation__ == "regular_grid" + and _RegularGrid.is_axes_and_values(source) + ): + # Deprecated way of giving a grid, read as a table of points + # To be removed on v1.16 + source = _RegularGrid.points_from_axes(*source) + # Triggers an error if source is not a list of numbers source = np.array(source, dtype=np.float64) # Checks if 2D array @@ -4190,13 +4025,12 @@ def __validate_interpolation(self, interpolation): "shepard", "linear", "rbf", - "regular_grid", ]: warnings.warn( ( "Interpolation method set to 'shepard'. The methods " - "'linear', 'shepard', 'rbf' and 'regular_grid' are supported for " - "multiple dimensions." + "'linear', 'shepard' and 'rbf' are supported for " + "scattered points over multiple dimensions." ), ) interpolation = "shepard" @@ -4241,7 +4075,7 @@ def to_dict(self, **kwargs): # pylint: disable=unused-argument else: source = source.__name__ - function_dict = { + return { "source": source, "title": self.title, "inputs": self.__inputs__, @@ -4250,20 +4084,6 @@ def to_dict(self, **kwargs): # pylint: disable=unused-argument "extrapolation": self.__extrapolation__, } - # A regular-grid Function cannot be rebuilt from its flat scatter - # ``source``; persist the ``(axes, grid_data)`` structure (and the mapped - # scipy method) instead, so it round-trips through ``from_dict``. - if self.__interpolation__ == "regular_grid": - function_dict["source"] = [ - [np.asarray(axis).tolist() for axis in self._grid_axes], - np.asarray(self._grid_data).tolist(), - ] - grid_method = getattr(self, "_grid_method", "linear") - if grid_method != "linear": - function_dict["grid_method"] = grid_method - - return function_dict - @classmethod def from_dict(cls, func_dict): """Creates a Function instance from a dictionary. @@ -4275,27 +4095,34 @@ def from_dict(cls, func_dict): """ source = func_dict["source"] if func_dict["interpolation"] is None and func_dict["extrapolation"] is None: - source = from_hex_decode(source) - - function = cls( + try: + source = from_hex_decode(source) + except ValueError as error: + # Saved with allow_pickle=False: only the function's name is kept + raise ValueError( + f"The Function '{func_dict.get('title')}' cannot be loaded: it " + f"was saved as the name of a Python function ({source!r}) " + "rather than the function itself, which happens when saving " + "with allow_pickle=False. Save it with allow_pickle=True, or " + "give the data as a table instead of a function." + ) from error + + interpolation = func_dict["interpolation"] + if interpolation == "regular_grid": + # Older files name the layout here and may hold (axes, values) + interpolation = func_dict.get("grid_method", "linear") + if _RegularGrid.is_axes_and_values(source): + source = _RegularGrid.points_from_axes(*source) + + return cls( source=source, - interpolation=func_dict["interpolation"], + interpolation=interpolation, extrapolation=func_dict["extrapolation"], inputs=func_dict["inputs"], outputs=func_dict["outputs"], title=func_dict["title"], ) - # Restore a non-default regular-grid method (the constructor above builds - # the "linear" default); rebuild the interpolator/extrapolator with it. - grid_method = func_dict.get("grid_method") - if grid_method and grid_method != "linear": - function._grid_method = grid_method - function.set_interpolation("regular_grid") - function.set_extrapolation(function.get_extrapolation_method()) - - return function - @staticmethod def __make_arith_lambda( operator, func, other, func_dim, other_dim=0, reverse=False diff --git a/rocketpy/plots/aero_surface_plots.py b/rocketpy/plots/aero_surface_plots.py index 17d2e3a87..de27c3ae0 100644 --- a/rocketpy/plots/aero_surface_plots.py +++ b/rocketpy/plots/aero_surface_plots.py @@ -26,35 +26,114 @@ def __init__(self, aero_surface): def draw(self, *, filename=None): """A plain generic surface has no geometry to draw.""" - # Coefficients swept against their most relevant incidence angle: pitch-plane - # coefficients vs. angle of attack, yaw-plane ones vs. sideslip. - _COEFFICIENT_SWEEP = [ - ("cL", "alpha"), - ("cQ", "beta"), - ("cD", "alpha"), - ("cm", "alpha"), - ("cn", "beta"), - ] - - def coefficients(self, *, mach=0.3, angle_range_deg=15.0, filename=None): - """Plot the surface's main aerodynamic coefficients. - - Each available, non-zero coefficient (``cL, cQ, cD, cm, cn``) is swept - against its most relevant incidence angle (pitch-plane coefficients vs. - angle of attack, yaw-plane vs. sideslip) at a representative Mach, - skipping coefficients that are identically zero or flat. Works - uniformly across surface types because every generic, linear and - Barrowman surface now exposes these coefficients as callables over the - standard argument tuple. + # For each body-frame coefficient, the inputs worth sweeping, most relevant + # first: its own plane's incidence angle, the other angle, then the angular + # rate that damps its motion. A coefficient is swept against the first + # input on its list that it depends on. + _COEFFICIENT_SWEEP = { + "cN": ("alpha", "beta", "pitch_rate"), + "cY": ("beta", "alpha", "yaw_rate"), + "cA": ("alpha", "beta"), + "cm": ("alpha", "beta", "pitch_rate"), + "cn": ("beta", "alpha", "yaw_rate"), + "cl": ("roll_rate", "alpha", "beta"), + } + _AXIS_LABELS = { + "alpha": "Angle of attack (°)", + "beta": "Sideslip angle (°)", + "pitch_rate": "Reduced pitch rate q·L/(2V)", + "yaw_rate": "Reduced yaw rate r·L/(2V)", + "roll_rate": "Reduced roll rate p·L/(2V)", + "mach": "Mach number", + } + + @staticmethod + def _depends_on(coeff, index, mach): + """Find the inputs a coefficient changes with. + + Read from ``depends_on`` when the coefficient has it. Otherwise each + input is moved away from a reference state, and the coefficient depends + on it if its value changes. Parameters ---------- - mach : float, optional - Mach number at which to sample the coefficients. Default 0.3. + coeff : callable + The coefficient, taking the surface's full list of inputs. + index : dict + Position of each input name in that list. + mach : float + Mach number of the reference state. + + Returns + ------- + set of str + Names of the inputs the coefficient depends on. + """ + depends = getattr(coeff, "depends_on", None) + if depends is not None: + return set(depends) + nudges = { + "alpha": np.deg2rad(5), + "beta": np.deg2rad(5), + "mach": mach + 0.3, + "reynolds": 1e6, + "pitch_rate": 0.05, + "yaw_rate": 0.05, + "roll_rate": 0.05, + } + base = [0.0] * len(index) + base[index["mach"]] = mach + reference = coeff(*base) + found = set() + for name, value in nudges.items(): + if name not in index: + continue + args = list(base) + args[index[name]] = value + if not np.isclose(coeff(*args), reference): + found.add(name) + return found + + def coefficients( + self, + *, + mach=(0.1, 0.3, 0.6, 0.9), + angle_range_deg=15.0, + rate_range=0.1, + filename=None, + ): + """Plots the surface's body-frame aerodynamic coefficients. + + Each coefficient (``cN, cY, cA, cm, cn, cl``) that is not zero gets its + own plot, against the input it depends on most: the angle of attack for + the pitch-plane ones (``cN``, ``cA``, ``cm``), the sideslip angle for + the yaw-plane ones (``cY``, ``cn``) and the roll rate for the roll + moment ``cl``. A coefficient that does not use that input is plotted + against the next one it does use (the other angle, then an angular + rate), or against Mach if Mach is all it depends on. When a coefficient + changes with Mach, one curve is drawn for each Mach number in ``mach``. + Every input that is not plotted is held at zero. + + Parameters + ---------- + mach : float or sequence of float, optional + Mach number or numbers at which to draw each coefficient. Default + ``(0.1, 0.3, 0.6, 0.9)``. angle_range_deg : float, optional - Half-range of the incidence sweep, in degrees. Default 15. + The angles are plotted from ``-angle_range_deg`` to + ``+angle_range_deg``, in degrees. Default 15. + rate_range : float, optional + The angular rates are plotted from ``-rate_range`` to + ``+rate_range``. They are reduced rates, the rate times the + reference length divided by twice the airspeed (for example + ``p * L / (2 * V)`` for roll), so they have no units. Default 0.1. filename : str | None, optional - Path to save the figure; if None the figure is shown. + The path the plot should be saved to. By default None, in which case + the plot will be shown instead of saved. + + Returns + ------- + None """ surface = self.aero_surface independent_vars = getattr(surface, "independent_vars", None) @@ -63,40 +142,61 @@ def coefficients(self, *, mach=0.3, angle_range_deg=15.0, filename=None): index = {name: i for i, name in enumerate(independent_vars)} if "mach" not in index: return + machs = np.atleast_1d(np.asarray(mach, dtype=float)) n_args = len(independent_vars) - angles = np.linspace( - np.deg2rad(-angle_range_deg), np.deg2rad(angle_range_deg), 61 - ) entries = [] - for name, var in self._COEFFICIENT_SWEEP: + for name, candidates in self._COEFFICIENT_SWEEP.items(): coeff = getattr(surface, name, None) if coeff is None or getattr(coeff, "is_zero", False): continue - if var not in index: - continue - values = np.empty_like(angles) - for i, angle in enumerate(angles): - args = [0.0] * n_args - args[index["mach"]] = mach - args[index[var]] = angle - values[i] = coeff(*args) - if np.allclose(values, 0.0): + depends = self._depends_on(coeff, index, machs[0]) + var = next((v for v in candidates if v in depends and v in index), None) + if var is None: + var = "mach" if "mach" in depends else candidates[0] + if var == "mach": + x = np.linspace(0.0, machs.max(), 61) + curve_machs = [None] + else: + limit = ( + np.deg2rad(angle_range_deg) + if var in ("alpha", "beta") + else rate_range + ) + x = np.linspace(-limit, limit, 61) + curve_machs = machs if "mach" in depends else [None] + curves = [] + for curve_mach in curve_machs: + values = np.empty_like(x) + for i, value in enumerate(x): + args = [0.0] * n_args + args[index["mach"]] = machs[0] if curve_mach is None else curve_mach + args[index[var]] = value + values[i] = coeff(*args) + curves.append((curve_mach, values)) + if all(np.allclose(values, 0.0) for _, values in curves): continue - entries.append((name, var, values)) + entries.append((name, var, x, curves)) if not entries: return fig, axes = plt.subplots( - len(entries), 1, figsize=(7, 2.3 * len(entries)), squeeze=False + len(entries), 1, figsize=(7, 2.5 * len(entries)), squeeze=False ) - for ax, (name, var, values) in zip(axes[:, 0], entries): - ax.plot(np.rad2deg(angles), values) - ax.set_xlabel(f"{var.replace('_', ' ').title()} (°)") + for ax, (name, var, x, curves) in zip(axes[:, 0], entries): + x_plot = np.rad2deg(x) if var in ("alpha", "beta") else x + for curve_mach, values in curves: + label = None if len(curves) == 1 else f"Mach {curve_mach:g}" + ax.plot(x_plot, values, label=label) + if len(curves) == 1 and curves[0][0] is not None: + ax.set_title(f"{name} at Mach {curves[0][0]:g}", fontsize=9) + elif len(curves) > 1: + ax.legend(fontsize=8) + ax.set_xlabel(self._AXIS_LABELS[var]) ax.set_ylabel(name) ax.grid(True) - axes[0, 0].set_title(f"{surface.name} coefficients (Mach {mach})") + fig.suptitle(f"{surface.name} coefficients") plt.tight_layout() show_or_save_plot(filename) @@ -109,7 +209,7 @@ class _LinearGenericSurfacePlots(_GenericSurfacePlots): """Plots for a linear generic surface; same plots as the generic base.""" -class _BarrowmanSurfacePlots(_LinearGenericSurfacePlots): +class _BarrowmanSurfacePlots(_GenericSurfacePlots): """Plots shared by the geometry-defined (Barrowman) surfaces: adds the geometry drawing and the lift-coefficient surface plot.""" diff --git a/rocketpy/plots/flight_plots.py b/rocketpy/plots/flight_plots.py index c0d087ee4..8ccb1d4a1 100644 --- a/rocketpy/plots/flight_plots.py +++ b/rocketpy/plots/flight_plots.py @@ -1258,100 +1258,194 @@ def fluid_mechanics_data(self, *, filename=None): # pylint: disable=too-many-st plt.subplots_adjust(hspace=0.5) show_or_save_plot(filename) - def stability_margin_data(self, *, filename=None): - """Plots the rocket's stability margin over the flight, in calibers. - - The stability margin is one of the most important results of a - simulation: it is the distance from the center of mass to the center of - pressure that must stay positive (center of pressure behind the center - of mass) for the rocket to correct disturbances. A secondary axis reads - the same margin as a percentage of the rocket's overall length, the - convention often used in hobby rocketry. For a non-axisymmetric rocket - the pitch and yaw margins are drawn separately. + # Stability along the ascent. The panels below are shared by the single + # plots and by ``stability_summary``. - Parameters - ---------- - filename : str | None, optional - The path the plot should be saved to. By default None, in which case - the plot will be shown instead of saved. Supported file endings are: - eps, jpg, jpeg, pdf, pgf, png, ps, raw, rgba, svg, svgz, tif, tiff - and webp (these are the formats supported by matplotlib). - - Returns - ------- - None - """ - asymmetric = not self.flight.rocket.is_axisymmetric - - plt.figure(figsize=(9, 4.5)) - ax1 = plt.subplot(111) - ax1.plot( + def _ascent_window(self): + """Start and end, in seconds, of the free ascent: rail departure to + apogee, or to the first parachute event (or the end of the flight) when + there is no apogee, e.g. a flight cut short before it.""" + upper = ( + self.flight.apogee_time + if self.flight.apogee_time != 0 + else self.first_parachute_event_time + ) + return self.flight.out_of_rail_time, upper + + def _plot_angles(self, ax, t_lower, t_upper): + """Total angle of attack, angle of attack in the pitch plane and + sideslip angle against time, on ``ax``.""" + flight = self.flight + curves = ( + (flight.angle_of_attack, "-", "Total angle of attack"), + (flight.partial_angle_of_attack, "--", "Angle of attack (pitch plane)"), + (flight.angle_of_sideslip, ":", "Sideslip angle (yaw plane)"), + ) + tops = [] + for curve, style, label in curves: + ax.plot(curve[:, 0], curve[:, 1], style, label=label) + mask = (curve[:, 0] >= t_lower) & (curve[:, 0] <= t_upper) + values = np.abs(curve[mask, 1]) + if len(values): + # The 95th percentile keeps the runaway rise near apogee (where + # the speed vanishes) from setting the scale; the value at rail + # departure, usually the largest the margins are read at, must + # stay in view + tops.append(1.5 * float(np.percentile(values, 95))) + tops.append(1.15 * float(values[0])) + top = max(1.0, max(tops, default=1.0)) + ax.axhline(0, color="0.6", linewidth=0.8) + ax.set_xlim(t_lower, t_upper) + ax.set_ylim(-top, top) + ax.set_title("Angles of Attack") + ax.set_xlabel("Time (s)") + ax.set_ylabel("Angle (°)") + ax.legend() + ax.grid() + + def _plot_stability_margin(self, ax, t_upper): + """Stability margin in calibers against time on ``ax``, in the pitch and + yaw planes, with a secondary axis in percent of the rocket's length.""" + ax.plot( self.flight.stability_margin[:, 0], self.flight.stability_margin[:, 1], - label="Pitch" if asymmetric else "Stability margin", + label="Pitch", ) - if asymmetric: - ax1.plot( - self.flight.stability_margin_yaw[:, 0], - self.flight.stability_margin_yaw[:, 1], - label="Yaw", - ) - ax1.set_title("Stability Margin") - ax1.set_xlabel("Time (s)") - ax1.set_ylabel("Stability Margin (c)") - ax1.set_xlim(0, self.first_parachute_event_time) - ax1.legend() - ax1.grid() + ax.plot( + self.flight.stability_margin_yaw[:, 0], + self.flight.stability_margin_yaw[:, 1], + "--", + label="Yaw", + ) + ax.set_title("Stability Margin") + ax.set_xlabel("Time (s)") + ax.set_ylabel("Stability Margin (c)") + ax.set_xlim(0, t_upper) + ax.legend() + ax.grid() # Secondary y-axis reading the same margin as a percentage of the # rocket's overall length (see Rocket.length), the convention often used # in hobby rocketry. A margin in calibers and the same margin as a # fraction of body length differ only by the constant factor below, so # the second scale is a plain rescaling of the caliber axis. - # A rocket with no aerodynamic surfaces has no defined length, so the + # A rocket whose two ends are not known has no length (None), so the # percentage-of-length scale can't be drawn; skip it in that case. rocket = self.flight.rocket - rocket_length = rocket.length if rocket.aerodynamic_surfaces else 0 + rocket_length = rocket.length or 0 if rocket_length > 0: factor = 2 * rocket.radius / rocket_length * 100 - secondary_axis = ax1.secondary_yaxis( + secondary_axis = ax.secondary_yaxis( "right", functions=(lambda c: c * factor, lambda p: p / factor), ) secondary_axis.set_ylabel("Stability Margin (% of length)") - self._add_event_markers_dropline(ax1, labels={"Out Of Rail", "Burnout"}) + self._add_event_markers_dropline(ax, labels={"Out Of Rail", "Burnout"}) + def _plot_natural_frequency(self, ax, t_upper): + """Natural frequency of the attitude oscillation, in the pitch and yaw + planes, with the roll rate overlaid, in Hz, on ``ax``.""" + freq = self.flight.pitch_natural_frequency + ax.plot(freq[:, 0], freq[:, 1] / (2 * np.pi), label="Pitch natural freq.") + yaw_freq = self.flight.yaw_natural_frequency + ax.plot( + yaw_freq[:, 0], + yaw_freq[:, 1] / (2 * np.pi), + "--", + label="Yaw natural freq.", + ) + roll_rate = self.flight.w3 + ax.plot( + roll_rate[:, 0], + np.abs(roll_rate[:, 1]) / (2 * np.pi), + ":", + color="tab:red", + label="Roll rate", + ) + ax.set_title("Natural Frequency & Roll Rate") + ax.set_xlabel("Time (s)") + ax.set_ylabel("Frequency (Hz)") + ax.set_xlim(0, t_upper) + ax.legend() + ax.grid() + self._add_event_markers_dropline(ax, labels={"Out Of Rail", "Burnout"}) + + def _plot_damping_ratio(self, ax, t_upper): + """Damping ratio of the attitude oscillation, in the pitch and yaw + planes, on ``ax``.""" + ratio = self.flight.pitch_damping_ratio + ax.plot(ratio[:, 0], ratio[:, 1], label="Pitch") + yaw_ratio = self.flight.yaw_damping_ratio + ax.plot(yaw_ratio[:, 0], yaw_ratio[:, 1], "--", label="Yaw") + ax.set_title("Damping Ratio") + ax.set_xlabel("Time (s)") + ax.set_ylabel("Damping Ratio (ζ)") + ax.set_xlim(0, t_upper) + ax.legend() + ax.grid() + + def stability_margin_data(self, *, filename=None): + """Plots the rocket's stability margin over the ascent, in calibers. + + The stability margin is one of the most important results of a + simulation: it is the distance from the center of mass to the neutral + point that must stay positive (neutral point behind the center of mass) + for the rocket to correct disturbances. A secondary axis reads the same + margin as a percentage of the rocket's overall length, the convention + often used in hobby rocketry. The pitch and yaw margins are drawn + separately whenever they can differ: for a non-axisymmetric rocket, and + for an axisymmetric rocket with a surface nonlinear in the angle of + attack, where pitch is the plane of the wind and yaw the motion across + it. The time axis stops at apogee. + + Parameters + ---------- + filename : str | None, optional + The path the plot should be saved to. By default None, in which case + the plot will be shown instead of saved. Supported file endings are: + eps, jpg, jpeg, pdf, pgf, png, ps, raw, rgba, svg, svgz, tif, tiff + and webp (these are the formats supported by matplotlib). + + Returns + ------- + None + """ + plt.figure(figsize=(9, 4.5)) + self._plot_stability_margin(plt.subplot(111), self._ascent_window()[1]) show_or_save_plot(filename) def stability_and_control_data(self, *, filename=None): """Deprecated. Stability and the frequency response are now separate plots. - Use :meth:`stability_margin_data` for the stability margin, and - :meth:`dynamic_stability_data` for the natural frequency, damping ratio - and attitude frequency response. + Use :meth:`stability_margin_data` for the stability margin, + :meth:`dynamic_stability_data` for the natural frequency and damping + ratio, and :meth:`attitude_frequency_response_data` for the attitude + frequency response. Parameters ---------- filename : str | None, optional - Passed through to both replacement plots. + Passed through to the replacement plots. """ warnings.warn( "stability_and_control_data() is deprecated and will be removed in " - "v1.13. Stability is now its own plot: use stability_margin_data() " - "for the stability margin, and dynamic_stability_data() for the " - "natural frequency, damping ratio and attitude frequency response.", + "v1.16.0. Stability is now its own plot: use stability_margin_data() " + "for the stability margin, dynamic_stability_data() for the " + "natural frequency and damping ratio, and " + "attitude_frequency_response_data() for the frequency response.", DeprecationWarning, stacklevel=2, ) self.stability_margin_data(filename=filename) self.dynamic_stability_data(filename=filename) + self.attitude_frequency_response_data(filename=filename) - def dynamic_stability_data(self, *, filename=None): # pylint: disable=too-many-statements - """Plots the rocket's dynamic-stability quantities over the flight: the - pitch (and, for non-axisymmetric rockets, yaw) natural frequency and - damping ratio of the linearized attitude oscillation, together with the - attitude frequency response (the FFT of the simulated oscillation), which - independently verifies the predicted natural frequency. + def dynamic_stability_data(self, *, filename=None): + """Plots the rocket's dynamic-stability quantities over the ascent: the + natural frequency and damping ratio of the linearized attitude + oscillation, pitch and yaw when they can differ, with the roll rate + overlaid on the frequency so that roll-resonance crossings can be read + off directly. Parameters ---------- @@ -1365,62 +1459,34 @@ def dynamic_stability_data(self, *, filename=None): # pylint: disable=too-many- ------- None """ - asymmetric = not self.flight.rocket.is_axisymmetric - # Cap the time axis at apogee: the attitude oscillation is only - # meaningful during ascent. Fall back to the first parachute event (or - # flight end) when there is no apogee, e.g. a flight cut short before it. - upper = ( - self.flight.apogee_time - if self.flight.apogee_time != 0 - else self.first_parachute_event_time - ) + _, t_upper = self._ascent_window() + plt.figure(figsize=(9, 6)) + self._plot_natural_frequency(plt.subplot(211), t_upper) + self._plot_damping_ratio(plt.subplot(212), t_upper) + plt.subplots_adjust(hspace=0.5) + show_or_save_plot(filename) - plt.figure(figsize=(9, 9)) + def attitude_frequency_response_data(self, *, filename=None): + """Plots the attitude frequency response: the FFT spectrum of the + simulated attitude angle and body rates over the five seconds after + rail departure, each normalized by its peak. The peak should fall at + the natural frequency of :meth:`dynamic_stability_data`, giving an + independent check of the linearized prediction. - ax1 = plt.subplot(311) - freq = self.flight.pitch_natural_frequency - ax1.plot(freq[:, 0], freq[:, 1] / (2 * np.pi), label="Pitch natural freq.") - if asymmetric: - yaw_freq = self.flight.yaw_natural_frequency - ax1.plot( - yaw_freq[:, 0], - yaw_freq[:, 1] / (2 * np.pi), - "--", - label="Yaw natural freq.", - ) - roll_rate = self.flight.w3 - ax1.plot( - roll_rate[:, 0], - np.abs(roll_rate[:, 1]) / (2 * np.pi), - ":", - color="tab:red", - label="Roll rate", - ) - ax1.set_title("Natural Frequency & Roll Rate") - ax1.set_xlabel("Time (s)") - ax1.set_ylabel("Frequency (Hz)") - ax1.set_xlim(0, upper) - ax1.legend() - ax1.grid() - self._add_event_markers_dropline(ax1, labels={"Out Of Rail", "Burnout"}) - - ax2 = plt.subplot(312) - ratio = self.flight.pitch_damping_ratio - ax2.plot(ratio[:, 0], ratio[:, 1], label="Pitch") - if asymmetric: - yaw_ratio = self.flight.yaw_damping_ratio - ax2.plot(yaw_ratio[:, 0], yaw_ratio[:, 1], "--", label="Yaw") - ax2.set_title("Damping Ratio") - ax2.set_xlabel("Time (s)") - ax2.set_ylabel("Damping Ratio (ζ)") - ax2.set_xlim(0, upper) - ax2.legend() - ax2.grid() + Parameters + ---------- + filename : str | None, optional + The path the plot should be saved to. By default None, in which case + the plot will be shown instead of saved. Supported file endings are: + eps, jpg, jpeg, pdf, pgf, png, ps, raw, rgba, svg, svgz, tif, tiff + and webp (these are the formats supported by matplotlib). - # Frequency response: the FFT spectrum of the simulated attitude and body - # rates. Its peak should fall at the natural frequency plotted above, - # giving an independent check of the linearized prediction. - ax3 = plt.subplot(313) + Returns + ------- + None + """ + plt.figure(figsize=(9, 4.5)) + ax = plt.subplot(111) x_axis = np.arange(0, 5, 0.01) for response, label in ( (self.flight.attitude_frequency_response, "Attitude Angle"), @@ -1429,15 +1495,45 @@ def dynamic_stability_data(self, *, filename=None): # pylint: disable=too-many- (self.flight.omega3_frequency_response, r"$\omega_3$"), ): peak = response.max if response.max != 0 else 1 - ax3.plot(x_axis, response(x_axis) / peak, label=label) - ax3.set_title("Attitude Frequency Response") - ax3.set_xlabel("Frequency (Hz)") - ax3.set_ylabel("Amplitude Magnitude Normalized") - ax3.set_xlim(0, 5) - ax3.legend() - ax3.grid() + ax.plot(x_axis, response(x_axis) / peak, label=label) + ax.set_title("Attitude Frequency Response") + ax.set_xlabel("Frequency (Hz)") + ax.set_ylabel("Amplitude Magnitude Normalized") + ax.set_xlim(0, 5) + ax.legend() + ax.grid() + show_or_save_plot(filename) - plt.subplots_adjust(hspace=0.5) + def stability_summary(self, *, filename=None): + """Plots the stability of the ascent in one figure: the angles of + attack the margins are read at, the stability margin, the natural + frequency with the roll rate, and the damping ratio, pitch and yaw + wherever they can differ. + + This is the four panels of :meth:`angle_of_attack_data`, + :meth:`stability_margin_data` and :meth:`dynamic_stability_data` side by + side, so that a margin or frequency that moves with the angle of attack + can be read against the angle that moves it. + + Parameters + ---------- + filename : str | None, optional + The path the plot should be saved to. By default None, in which case + the plot will be shown instead of saved. Supported file endings are: + eps, jpg, jpeg, pdf, pgf, png, ps, raw, rgba, svg, svgz, tif, tiff + and webp (these are the formats supported by matplotlib). + + Returns + ------- + None + """ + t_lower, t_upper = self._ascent_window() + _, axes = plt.subplots(2, 2, figsize=(12, 7)) + self._plot_angles(axes[0, 0], t_lower, t_upper) + self._plot_stability_margin(axes[0, 1], t_upper) + self._plot_natural_frequency(axes[1, 0], t_upper) + self._plot_damping_ratio(axes[1, 1], t_upper) + plt.tight_layout() show_or_save_plot(filename) def pressure_rocket_altitude(self, *, filename=None): @@ -1771,7 +1867,9 @@ def drift_bearing_data(self, *, filename=None): show_or_save_plot(filename) def angle_of_attack_data(self, *, filename=None): - """Plots angle of attack, partial angle of attack, and angle of sideslip. + """Plots the angles of attack over the ascent on one panel: the total + angle of attack, between the rocket's axis and the air; the angle of + attack in the pitch plane; and the sideslip angle in the yaw plane. Parameters ---------- @@ -1785,73 +1883,9 @@ def angle_of_attack_data(self, *, filename=None): ------- None """ - t_lower = self.flight.out_of_rail_time - t_upper = ( - self.flight.apogee_time - if self.flight.apogee_time != 0 - else self.flight.t_final - ) - - def _ylim_in_range(arr): - mask = (arr[:, 0] >= t_lower) & (arr[:, 0] <= t_upper) - vals = arr[mask, 1] - if len(vals) == 0: - return 10.0 - # Use 95th percentile so the runaway rise near apogee (v→0) - # does not dominate the y-scale; multiply by 1.5 to keep headroom. - top = float(np.percentile(vals, 95)) * 1.5 - return max(top, 1.0) - - def _ylim_signed(arr): - # Symmetric y-limits for signed quantities (partial angle of attack, - # sideslip): these are arctan2-based and routinely go negative, so a - # 0 lower bound would clip half the signal. Scale by the 95th - # percentile of the magnitude to ignore the runaway rise near apogee. - mask = (arr[:, 0] >= t_lower) & (arr[:, 0] <= t_upper) - vals = arr[mask, 1] - if len(vals) == 0: - return (-10.0, 10.0) - top = float(np.percentile(np.abs(vals), 95)) * 1.5 - top = max(top, 1.0) - return (-top, top) - - plt.figure(figsize=(9, 9)) - - ax1 = plt.subplot(311) - ax1.plot(self.flight.angle_of_attack[:, 0], self.flight.angle_of_attack[:, 1]) - ax1.set_xlim(t_lower, t_upper) - ax1.set_ylim(0, _ylim_in_range(self.flight.angle_of_attack[:, :])) - ax1.set_title("Angle of Attack") - ax1.set_xlabel("Time (s)") - ax1.set_ylabel("Angle of Attack (°)") - ax1.grid() - - ax2 = plt.subplot(312) - ax2.plot( - self.flight.partial_angle_of_attack[:, 0], - self.flight.partial_angle_of_attack[:, 1], - ) - ax2.set_xlim(t_lower, t_upper) - ax2.set_ylim(*_ylim_signed(self.flight.partial_angle_of_attack[:, :])) - ax2.axhline(0, color="0.6", linewidth=0.8) - ax2.set_title("Partial Angle of Attack") - ax2.set_xlabel("Time (s)") - ax2.set_ylabel("Partial Angle of Attack (°)") - ax2.grid() - - ax3 = plt.subplot(313) - ax3.plot( - self.flight.angle_of_sideslip[:, 0], self.flight.angle_of_sideslip[:, 1] - ) - ax3.set_xlim(t_lower, t_upper) - ax3.set_ylim(*_ylim_signed(self.flight.angle_of_sideslip[:, :])) - ax3.axhline(0, color="0.6", linewidth=0.8) - ax3.set_title("Angle of Sideslip") - ax3.set_xlabel("Time (s)") - ax3.set_ylabel("Angle of Sideslip (°)") - ax3.grid() - - plt.subplots_adjust(hspace=0.5) + t_lower, t_upper = self._ascent_window() + plt.figure(figsize=(9, 4.5)) + self._plot_angles(plt.subplot(111), t_lower, t_upper) show_or_save_plot(filename) def all(self): # pylint: disable=too-many-statements @@ -1886,9 +1920,15 @@ def all(self): # pylint: disable=too-many-statements print("\n\nDynamic Stability Plots\n") self.dynamic_stability_data() - print("\n\nAngle of Attack Plots\n") + print("\n\nAttitude Frequency Response Plot\n") + self.attitude_frequency_response_data() + + print("\n\nAngle of Attack Plot\n") self.angle_of_attack_data() + print("\n\nStability Summary Plot\n") + self.stability_summary() + print("\n\nAngular Position Plots\n") self.flight_path_angle_data() diff --git a/rocketpy/plots/rocket_plots.py b/rocketpy/plots/rocket_plots.py index ed7c6d3bb..e77a2cfa2 100644 --- a/rocketpy/plots/rocket_plots.py +++ b/rocketpy/plots/rocket_plots.py @@ -3,7 +3,6 @@ import matplotlib.pyplot as plt import numpy as np -from rocketpy.mathutils.function import Function from rocketpy.mathutils.vector_matrix import Vector from rocketpy.motors import EmptyMotor, HybridMotor, LiquidMotor, SolidMotor from rocketpy.rocket.aero_surface import Fin, Fins, NoseCone, Tail @@ -65,9 +64,15 @@ def _caliber_to_length_percent(self): differ only by the constant factor ``2 * radius / length`` (times 100 for a percentage), so the length-percentage scale is a plain rescaling of the caliber scale and can be drawn as a secondary axis. See - :attr:`rocketpy.Rocket.length`. + :attr:`rocketpy.Rocket.length`. ``None`` when the rocket has no + length (its two ends are not known and no ``length`` was given), so + the secondary axis is skipped. """ - factor = 2 * self.rocket.radius / self.rocket.length * 100 + rocket = self.rocket + length = rocket.length + if not length: + return None + factor = 2 * rocket.radius / length * 100 return (lambda calibers: calibers * factor, lambda percent: percent / factor) def static_margin(self, *, filename=None): @@ -105,16 +110,15 @@ def _plot_static_margin(self, margin, title, filename): ax.set_title(title) ax.grid(True) - secondary_axis = ax.secondary_yaxis( - "right", functions=self._caliber_to_length_percent() - ) - secondary_axis.set_ylabel("Static Margin (% of length)") + if (functions := self._caliber_to_length_percent()) is not None: + secondary_axis = ax.secondary_yaxis("right", functions=functions) + secondary_axis.set_ylabel("Static Margin (% of length)") show_or_save_fig(fig, filename) def stability_margin(self, *, filename=None): """Plots the stability margin of the rocket as a function of Mach number - and time, at zero angle of attack (the design surface). + and time, at zero angle of attack. Parameters ---------- @@ -126,7 +130,7 @@ def stability_margin(self, *, filename=None): ------- None """ - self._design_stability_margin(self.rocket.stability_margin).plot_2d( + self.rocket.stability_margin.plot_2d( lower=0, upper=[2, self.rocket.motor.burn_out_time], # Mach 2 and burnout samples=[20, 20], @@ -174,7 +178,7 @@ def stability_margin_yaw(self, *, filename=None): ------- None """ - self._design_stability_margin(self.rocket.stability_margin_yaw).plot_2d( + self.rocket.stability_margin_yaw.plot_2d( lower=0, upper=[2, self.rocket.motor.burn_out_time], # Mach 2 and burnout samples=[20, 20], @@ -183,16 +187,6 @@ def stability_margin_yaw(self, *, filename=None): filename=filename, ) - @staticmethod - def _design_stability_margin(margin): - """The zero-incidence (Mach, time) slice of an angle-of-attack-aware - stability margin, as a 2-D Function for surface plotting.""" - return Function( - lambda mach, time: margin.get_value_opt(0.0, mach, time), - inputs=["Mach", "Time (s)"], - outputs=margin.__outputs__[0], - ) - # pylint: disable=too-many-statements def drag_curves(self, *, filename=None): """Plots power off and on drag curves of the rocket as a function of time. @@ -480,6 +474,8 @@ def _draw_fins(self, ax, surface, position, drawn_surfaces, vis_args, plane): def _draw_fin(self, ax, surface, position, drawn_surfaces, vis_args, plane): """Draws individual fins.""" + # The fin's own frame sits at its root leading edge, cant applied + position = self.rocket._surface_origin(surface, position) # Get shape vec xs = surface.shape_vec[0] diff --git a/rocketpy/prints/aero_surface_prints.py b/rocketpy/prints/aero_surface_prints.py index 12a2ba5c5..478005070 100644 --- a/rocketpy/prints/aero_surface_prints.py +++ b/rocketpy/prints/aero_surface_prints.py @@ -1,12 +1,6 @@ import numpy as np -# The print classes mirror the aerodynamic-surface class hierarchy: -# GenericSurface -> _GenericSurfacePrints (root) -# LinearGenericSurface -> _LinearGenericSurfacePrints -# _BarrowmanSurface -> _BarrowmanSurfacePrints (adds clalpha/CP lift) -# NoseCone / Tail / Fins/Fin -> the leaf print classes below -# ControllableGenericSurface / AirBrakes / RailButtons -> generic-rooted leaves # TODO: this file could be separated into different, smaller files. class _GenericSurfacePrints: """Base prints for a generic aerodynamic surface.""" @@ -15,12 +9,13 @@ def __init__(self, aero_surface): self.aero_surface = aero_surface def coefficients(self): - """Prints a summary of the surface's main aerodynamic coefficients. + """Prints the surface's aerodynamic coefficients. - For every non-zero coefficient (``cL, cQ, cD, cm, cn``) reports its - value at a reference condition (5° angle of attack and sideslip, Mach - 0.3) and, when available, the variables it depends on. Works across all - surface types that expose the uniform coefficient accessors. + Reports the six body-frame coefficients the surface holds (``cN``, + ``cY``, ``cA``, ``cm``, ``cn``, ``cl``) at a reference condition (5° + angle of attack and sideslip, Mach 0.3) with the variables each one + depends on, marking the ones that are zero. A surface given in the wind + frame also reports ``cL``, ``cQ`` and ``cD``. """ surface = self.aero_surface independent_vars = getattr(surface, "independent_vars", None) @@ -39,18 +34,19 @@ def coefficients(self): args[index["alpha"]] = np.deg2rad(5) if "beta" in index: args[index["beta"]] = np.deg2rad(5) - printed = False - for name in ("cL", "cQ", "cD", "cm", "cn"): + names = ["cN", "cY", "cA", "cm", "cn", "cl"] + if getattr(surface, "force_convention", "body") == "wind": + names += ["cL", "cQ", "cD"] + for name in names: coeff = getattr(surface, name, None) - if coeff is None or getattr(coeff, "is_zero", False): + if coeff is None: + continue + if getattr(coeff, "is_zero", False): + print(f" {name} = 0 (zero)") continue - value = coeff(*args) depends = getattr(coeff, "depends_on", None) suffix = f" [depends on {', '.join(depends)}]" if depends else "" - print(f" {name} = {value:.4f}{suffix}") - printed = True - if not printed: - print(" (all zero)") + print(f" {name} = {coeff(*args):.4f}{suffix}") print() def identity(self): @@ -89,7 +85,7 @@ class _LinearGenericSurfacePrints(_GenericSurfacePrints): base.""" -class _BarrowmanSurfacePrints(_LinearGenericSurfacePrints): +class _BarrowmanSurfacePrints(_GenericSurfacePrints): """Prints shared by the geometry-defined (Barrowman) surfaces: adds the center-of-pressure / lift-curve-slope report on top of the generic base.""" diff --git a/rocketpy/prints/flight_prints.py b/rocketpy/prints/flight_prints.py index ceb32a7d1..f4a2eaddd 100644 --- a/rocketpy/prints/flight_prints.py +++ b/rocketpy/prints/flight_prints.py @@ -624,7 +624,7 @@ def stability_margin(self): # a defined overall length, also as a percentage of that length (the # convention often used in hobby rocketry). See Rocket.length. rocket = self.flight.rocket - length = rocket.length if rocket.aerodynamic_surfaces else 0 + length = rocket.length or 0 to_percent = 2 * rocket.radius / length * 100 if length > 0 else None def _margin(value): @@ -653,23 +653,19 @@ def _margin(value): f"at {self.flight.min_stability_margin_time:.2f} s" ) - out_of_rail_time = self.flight.out_of_rail_time - # The margins above describe the pitch plane. For a non-axisymmetric - # rocket, also report the yaw-plane margin at rail departure. - if not self.flight.rocket.is_axisymmetric: - print( - "Out of Rail Stability Margin - yaw: " - f"{_margin(self.flight.stability_margin_yaw.get_value_opt(out_of_rail_time))}" - ) + # The margins above describe the pitch plane + print( + "Out of Rail Stability Margin - yaw: " + f"{_margin(self.flight.out_of_rail_stability_margin_yaw)}" + ) def dynamic_stability(self): """Prints the rocket's dynamic-stability quantities at the key instants of the ascent. For rail departure and motor burnout it reports the attitude - oscillation's natural frequency and damping ratio (pitch, and yaw as - well for a non-axisymmetric rocket), and it reports the roll rate at - burnout. Companion summary to ``Flight.plots.dynamic_stability_data``. + oscillation's natural frequency and damping ratio in the pitch and yaw + planes, and it reports the roll rate at burnout. Companion summary to ``Flight.plots.dynamic_stability_data``. Notes ----- @@ -682,27 +678,22 @@ def dynamic_stability(self): """ two_pi = 6.283185307179586 flight = self.flight - asymmetric = not flight.rocket.is_axisymmetric def report(label, time): natural_frequency = ( flight.pitch_natural_frequency.get_value_opt(time) / two_pi ) damping_ratio = flight.pitch_damping_ratio.get_value_opt(time) - plane = "Pitch " if asymmetric else "" print( - f"{label} (t = {time:.2f} s): {plane}natural frequency = " + f"{label} (t = {time:.2f} s): Pitch natural frequency = " f"{natural_frequency:.2f} Hz, damping ratio = {damping_ratio:.3f}" ) - if asymmetric: - yaw_frequency = ( - flight.yaw_natural_frequency.get_value_opt(time) / two_pi - ) - yaw_damping = flight.yaw_damping_ratio.get_value_opt(time) - print( - f" Yaw natural frequency = {yaw_frequency:.2f} Hz, " - f"damping ratio = {yaw_damping:.3f}" - ) + yaw_frequency = flight.yaw_natural_frequency.get_value_opt(time) / two_pi + yaw_damping = flight.yaw_damping_ratio.get_value_opt(time) + print( + f" Yaw natural frequency = {yaw_frequency:.2f} Hz, " + f"damping ratio = {yaw_damping:.3f}" + ) burn_out_time = flight.rocket.motor.burn_out_time diff --git a/rocketpy/prints/rocket_prints.py b/rocketpy/prints/rocket_prints.py index d9021b6e6..991e75952 100644 --- a/rocketpy/prints/rocket_prints.py +++ b/rocketpy/prints/rocket_prints.py @@ -1,6 +1,3 @@ -from rocketpy.rocket.aero_surface.generic_surface import GenericSurface - - class _RocketPrints: """Class that holds prints methods for Rocket class. @@ -103,37 +100,43 @@ def rocket_aerodynamics_quantities(self): """ print("\nAerodynamics Lift Coefficient Derivatives\n") for surface, _ in self.rocket.aerodynamic_surfaces: - if isinstance(surface, GenericSurface): + # The slopes at Mach 0 and zero angles, referenced to the rocket's + # area; the yaw one is signed like the pitch one (see + # Rocket.evaluate_center_of_pressure). + ref_factor = surface.reference_area / self.rocket.area + pitch = ref_factor * surface.cN_alpha(0, 0, 0, 0, 0, 0, 0) + yaw = -ref_factor * surface.cY_beta(0, 0, 0, 0, 0, 0, 0) + if pitch == 0 and yaw == 0: continue - name = surface.name - # ref_factor corrects lift for different reference areas - ref_factor = (surface.rocket_radius / self.rocket.radius) ** 2 - print( - f"{name} Lift Coefficient Derivative: " - f"{ref_factor * surface.clalpha(0):.3f}/rad" - ) + line = f"{surface.name} Lift Coefficient Derivative: {pitch:.3f}/rad" + if abs(yaw - pitch) > 1e-9 * max(1.0, abs(pitch)): + line += f" (pitch), {yaw:.3f}/rad (yaw)" + print(line) print("\nCenter of Pressure\n") for surface, position in self.rocket.aerodynamic_surfaces: name = surface.name - cpz = surface.cp[2] # relative to the user defined coordinate system + # Same point the rocket's center of pressure is built from (see + # Rocket.evaluate_center_of_pressure), at Mach 0 + cpz = surface.aerodynamic_center.get_value_opt(0) + position = self.rocket._surface_origin(surface, position) print( f"{name} Center of Pressure position: " - f"{position.z - self.rocket._csys * cpz:.3f} m" + f"{position.z + self.rocket._csys * cpz:.3f} m" ) print("\nStability\n") print( f"Center of Mass position (time=0): {self.rocket.center_of_mass(0):.3f} m" ) print( - f"Aerodynamic Center position (Mach=0): " + f"Center of Pressure position (Mach=0): " f"{self.rocket.aerodynamic_center(0):.3f} m" ) # The static margin is reported in calibers and, when the rocket has a # defined overall length, also as a percentage of that length (the # convention often used in hobby rocketry). See Rocket.length. burn_out_time = self.rocket.motor.burn_out_time - length = self.rocket.length if self.rocket.aerodynamic_surfaces else 0 + length = self.rocket.length or 0 to_percent = 2 * self.rocket.radius / length * 100 if length > 0 else None def _margin(value): @@ -154,7 +157,7 @@ def _margin(value): f"{_margin(self.rocket.static_margin(burn_out_time))}" ) print( - f"Rocket Center of Mass (time=0) - Aerodynamic Center (Mach=0): " + f"Rocket Center of Mass (time=0) - Center of Pressure (Mach=0): " f"{abs(self.rocket.center_of_mass(0) - self.rocket.aerodynamic_center(0)):.3f} m\n" ) @@ -164,7 +167,7 @@ def _margin(value): "PITCH plane. Yaw plane:\n" ) print( - f"Aerodynamic Center position - yaw (Mach=0): " + f"Center of Pressure position - yaw (Mach=0): " f"{self.rocket.aerodynamic_center_yaw(0):.3f} m" ) print( diff --git a/rocketpy/rocket/__init__.py b/rocketpy/rocket/__init__.py index 94ad5e8ee..61c03a03d 100644 --- a/rocketpy/rocket/__init__.py +++ b/rocketpy/rocket/__init__.py @@ -17,6 +17,7 @@ TrapezoidalFin, TrapezoidalFins, ) +from rocketpy.rocket.aero_surface.aero_coefficient import AeroCoefficient from rocketpy.rocket.components import Components from rocketpy.rocket.parachute import Parachute from rocketpy.rocket.point_mass_rocket import PointMassRocket diff --git a/rocketpy/rocket/_helpers.py b/rocketpy/rocket/_helpers.py new file mode 100644 index 000000000..46ee201cd --- /dev/null +++ b/rocketpy/rocket/_helpers.py @@ -0,0 +1,1434 @@ +"""Functions that support the :class:`rocketpy.Rocket` class. + +They are kept out of ``rocket.py`` so that the class stays simple. Most take +the rocket as their first argument: the whole-rocket sums, the linearity and +symmetry checks, the neutral point, and the rocket as one set of coefficients. +""" + +import cmath +import math +import re +import warnings + +import numpy as np + +from rocketpy.mathutils._regular_grid import _RegularGrid +from rocketpy.mathutils.function import Function +from rocketpy.mathutils.vector_matrix import Vector +from rocketpy.rocket.aero_surface._barrowman_surface import _BarrowmanSurface +from rocketpy.rocket.aero_surface._helpers import _as_function +from rocketpy.rocket.aero_surface.aero_coefficient import AeroCoefficient +from rocketpy.rocket.aero_surface.linear_generic_surface import LinearGenericSurface + +# The surfaces the stability analysis looks at + + +def stability_surfaces(rocket, phase=None): + """The surfaces of ``rocket`` the stability analysis sums: those active + during ``phase`` (``"power_on"`` or ``"power_off"``; the rocket's + ``stability_phase`` when ``None``), as ``(surface, position)`` pairs. + + A surface is left out only when its ``active_during`` names the other + phase. One with a custom activation window cannot be evaluated without + a flight, so it is kept. + """ + phase = rocket.stability_phase if phase is None else phase + if phase not in ("power_on", "power_off"): + raise ValueError( + f"stability_phase must be 'power_on' or 'power_off', got {phase!r}." + ) + other = "power_on" if phase == "power_off" else "power_off" + return [ + (surface, position) + for surface, position in rocket.aerodynamic_surfaces + if getattr(surface, "active_during", "always") != other + ] + + +# Forces and moments of the whole rocket + +# The atmosphere the whole-rocket sums are taken in: unit density and the +# vanishing-Reynolds limit +_UNIT_DENSITY = Function(1.0) +_NO_VISCOSITY = Function(1e30) + + +class _UniformValue: + """A quantity that is the same at every altitude, read like a Function.""" + + def __init__(self, value): + self.value = value + + def get_value_opt(self, *_): + return self.value + + +def summed_force_and_moment( + rocket, + alpha, + beta, + mach, + omega, + speed=1.0, + reference_z=0.0, + phase=None, + reynolds=None, +): + """Total body-frame force ``(R1, R2, R3)`` and moment ``(M1, M2, M3)``, + summed over the aerodynamic surfaces of ``rocket`` active during the motor + ``phase`` (``"power_on"`` or ``"power_off"``; the rocket's + ``stability_phase`` when ``None``), at a flow state and set of body rates. + + Mirrors the per-surface computation the flight integrator performs: each + surface is fed its own local stream velocity, which includes the + ``omega x cp`` lever-arm term, so the sum captures the pitch and yaw damping + the distributed surfaces produce through their fore-and-aft positions. + Evaluated at unit air density; the result scales out of any dimensionless + coefficient, and the chosen ``speed`` cancels from every coefficient built + from it. ``omega`` is the body angular rate in rad/s. The stream is given at, + the rates are about, and the moment is taken about the point ``reference_z`` + meters ahead of the center of dry mass along the body axis (the center of + dry mass itself by default). ``reynolds`` is the Reynolds number of the + rocket, based on its diameter and ``speed``; each surface then sees its own, + scaled by its own length. ``None`` is the vanishing-Reynolds limit. + """ + if reynolds is None or reynolds <= 0: + viscosity = _NO_VISCOSITY + else: + # Unit density: Re = speed * diameter / viscosity + viscosity = _UniformValue(speed * 2 * rocket.radius / reynolds) + stream_direction = Vector([-math.tan(beta), -math.tan(alpha), -1.0]) + stream_at_reference = stream_direction / abs(stream_direction) * speed + body_rates = Vector(list(omega)) + reference = Vector([0.0, 0.0, reference_z]) + speed_of_sound = speed / mach if mach > 0 else 1e30 + totals = np.zeros(6) + for surface, _ in stability_surfaces(rocket, phase): + cp = rocket.surfaces_cp_to_cdm[surface] - reference + comp_stream = stream_at_reference - (body_rates ^ cp) + comp_speed = abs(comp_stream) + forces = surface.compute_forces_and_moments( + comp_stream, + comp_speed, + comp_speed / speed_of_sound, + 1.0, + cp, + body_rates, + _UNIT_DENSITY, + viscosity, + 0.0, + ) + totals += np.array(forces) + return totals + + +# Linearity in the angle of attack + +# Angles and Mach numbers at which the coefficients are read to tell whether a +# rocket is linear in the angle of attack. The angles cover what a rocket sees +# while its stability matters; beyond them it is tumbling. +_PROBE_ANGLES = np.radians([-15.0, -10.0, -5.0, -2.0, 0.0, 2.0, 5.0, 10.0, 15.0]) +_PROBE_MACHS = (0.3, 0.9, 2.0) + + +def _is_linear_by_construction(surface): + """Whether the forces of ``surface`` are linear in the flow angles whatever + its data: the Barrowman surfaces, and a linear generic surface whose + derivatives do not themselves depend on the angles.""" + if isinstance(surface, _BarrowmanSurface): + return True + if isinstance(surface, LinearGenericSurface): + # pylint: disable-next=protected-access + names = surface._get_default_coefficients() + return not any( + {"alpha", "beta"} & set(getattr(surface, name).depends_on) for name in names + ) + return False + + +def _varies_with(coefficient, variable): + """Whether a coefficient may change with ``variable``. A plain Function + does not say what it depends on, so it is taken to.""" + if isinstance(coefficient, AeroCoefficient): + return not coefficient.is_zero and variable in coefficient.depends_on + return True + + +def uses_rate_coefficients(rocket): + """Tell whether a surface of ``rocket`` has a coefficient that depends on a + body rate (``pitch_rate``, ``yaw_rate`` or ``roll_rate``). The built-in + surfaces are not counted: their damping comes from their position, not from + a rate coefficient.""" + # pylint: disable=protected-access + rates = ("pitch_rate", "yaw_rate", "roll_rate") + for surface, _ in stability_surfaces(rocket): + if isinstance(surface, _BarrowmanSurface): + continue + for name in surface._get_default_coefficients(): + coefficient = getattr(surface, name) + # A linear surface gives its rate dependence as a derivative + # (cm_q, cl_p, ...); a generic one through what a coefficient reads + if isinstance(surface, LinearGenericSurface): + if name[-2:] in ("_p", "_q", "_r") and not coefficient.is_zero: + return True + elif any(_varies_with(coefficient, rate) for rate in rates): + return True + return False + + +def is_incidence_linear(rocket): + """Tell whether the neutral point of ``rocket`` stays where it is as the + angle of attack (or the sideslip angle) changes. + + It does for a rocket made of surfaces that are linear in the angle, such as + the nose cone, the fins and the tail. It does not when a surface gives a + force that is nonlinear in the angle and sits away from the rest of the lift, + for example a body-lift term or a table with a stall, given as a + :class:`rocketpy.GenericSurface`. + + Only the coefficients of the surfaces that can be nonlinear are read, at a + few angles up to 15 degrees and at Mach 0.3, 0.9 and 2, so a rocket made of + the built-in surfaces costs nothing. The neutral point between two + neighboring angles is ``sum(x_i * slope_i) / sum(slope_i)``, with + ``slope_i`` the change of the force of surface ``i`` between them; the + rocket is linear when that point is the same between every pair of angles. + + Parameters + ---------- + rocket : rocketpy.Rocket + The rocket to check. + + Returns + ------- + bool + ``True`` when the neutral point moves by less than a micrometer. + """ + # pylint: disable=protected-access + surfaces = [surface for surface, _ in stability_surfaces(rocket)] + nonlinear_candidates = [ + surface for surface in surfaces if not _is_linear_by_construction(surface) + ] + intervals = len(_PROBE_ANGLES) - 1 + + def slopes(coefficient, surface, angle_index, mach): + """Change of a coefficient per radian between neighboring angles.""" + if not _varies_with(coefficient, ("alpha", "beta")[angle_index]): + return np.zeros(intervals) + args = [0.0] * len(surface.independent_vars) + args[2] = mach + values = [] + for angle in _PROBE_ANGLES: + args[angle_index] = angle + values.append(coefficient(*args)) + return np.diff(values) / np.diff(_PROBE_ANGLES) + + planes = ( + ("cN", "cm", "cN_alpha", "cm_alpha", 0), + ("cY", "cn", "cY_beta", "cn_beta", 1), + ) + for force, moment, force_slope, moment_slope, angle_index in planes: + angle_name = ("alpha", "beta")[angle_index] + probed = [ + surface + for surface in nonlinear_candidates + if _varies_with(getattr(surface, force), angle_name) + or _varies_with(getattr(surface, moment), angle_name) + ] + if not probed: + continue + # An axial force that changes with the angle, acting off the axis, also + # moves the neutral point. It is rare, so it is not followed further. + for surface in probed: + point = surface.force_application_point + if (point[0] or point[1]) and _varies_with(surface.cA, angle_name): + return False + any_mach = any( + _varies_with(getattr(surface, name), "mach") + for surface in probed + for name in (force, moment) + ) + for mach in _PROBE_MACHS if any_mach else _PROBE_MACHS[:1]: + numerator = np.zeros(intervals) + denominator = np.zeros(intervals) + for surface, position in stability_surfaces(rocket): + if surface in probed: + forces = slopes(getattr(surface, force), surface, angle_index, mach) + moments = slopes( + getattr(surface, moment), surface, angle_index, mach + ) + else: + args = [0.0] * len(surface.independent_vars) + args[2] = mach + forces = getattr(surface, force_slope)(*args) + moments = getattr(surface, moment_slope)(*args) + weight = surface.reference_area / rocket.area + application = ( + surface._rotation_surface_to_body @ surface.force_application_point + )[2] + numerator += weight * ( + forces * (position.z + rocket._csys * application) + + rocket._csys * surface.reference_length * moments + ) + denominator += weight * forces + lifting = np.abs(denominator) > 1e-12 + if not lifting.any(): + continue + if not lifting.all(): + return False # the lift appears or vanishes with the angle + if np.ptp(numerator / denominator) > 1e-6: + return False + return True + + +# Axisymmetry + +# Turning an axisymmetric rocket about its axis changes nothing, so the linear +# map from the crossflow (or from the lateral rates) to the lateral force and +# moment is the same in every plane. Written with the body-frame signs of +# ``cN``, ``cY``, ``cm`` and ``cn``, that is one relation per pair below: the +# yaw plane mirrors the pitch plane ... +_SYMMETRIC_PAIRS = ( + ("cN_alpha", "cY_beta", -1.0), + ("cm_alpha", "cn_beta", -1.0), + ("cN_q", "cY_r", 1.0), + ("cm_q", "cn_r", 1.0), + # ... and any coupling between the planes is the same seen from either. + # Canted fins have such a coupling, through the axial part of their force, + # and are still axisymmetric; a lone fin off the body axes is not. + ("cN_beta", "cY_alpha", 1.0), + ("cm_beta", "cn_alpha", 1.0), + ("cN_r", "cY_q", -1.0), + ("cm_r", "cn_q", -1.0), +) +# Nothing pushes sideways at zero angle, and the roll rate, which has no +# direction across the axis, gives no lateral force or moment. The roll terms +# themselves are left out: canted fins roll the rocket without breaking the +# symmetry of the pitch and yaw planes. +_ZERO_WHEN_SYMMETRIC = ( + "cN_0", + "cY_0", + "cm_0", + "cn_0", + "cN_p", + "cY_p", + "cm_p", + "cn_p", +) +_SYMMETRY_MACHS = np.linspace(0.0, 3.0, 16) + + +def is_axisymmetric(rocket, machs=_SYMMETRY_MACHS, tolerance=1e-6): + """Tell whether ``rocket`` behaves the same in every plane through its + axis, as far as its linear coefficients go. + + The whole rocket's small-angle derivatives are read at ``machs`` (the + surfaces of the rocket's ``stability_phase``, its drag left out since it is + axial) and checked against what turning the rocket about its axis must + leave unchanged: the yaw plane mirrors the pitch plane (``cY_beta = + -cN_alpha``, ``cn_beta = -cm_alpha``, ``cY_r = cN_q``, ``cn_r = cm_q``), + any coupling between the planes is the same seen from either (``cY_alpha + = cN_beta``, ``cn_alpha = cm_beta``, ``cY_q = -cN_r``, ``cn_q = -cm_r``), + and nothing acts sideways at zero angle or from the roll rate. Each + relation must hold to ``tolerance`` times the largest lateral derivative + at that Mach. Roll terms are not looked at. + + An asymmetry that only appears at large angles is not seen, since the + derivatives are taken at zero angle. + """ + rocket.evaluate_surfaces_cp_to_cdm() + machs = np.asarray(machs, dtype=float) + values = _linear_lumping( + _surfaces_evaluator(rocket, rocket.stability_phase), + (len(machs),), + lambda index: (machs[index[0]], None), + rates=True, + ) + lateral = [name for name in values if name[:2] in ("cN", "cY", "cm", "cn")] + scale = np.max(np.abs(np.array([values[name] for name in lateral])), axis=0) + allowed = tolerance * scale + 1e-15 + for name in _ZERO_WHEN_SYMMETRIC: + if np.any(np.abs(values[name]) > allowed): + return False + for pitch, yaw, sign in _SYMMETRIC_PAIRS: + if np.any(np.abs(values[pitch] - sign * values[yaw]) > allowed): + return False + return True + + +# Neutral point and stability + + +def axial_force_slope(surface, cp, mach, plane="pitch", step=1e-4): + """How fast one surface's axial force changes with the angle of attack + (``plane="pitch"``) or the sideslip angle (``"yaw"``), at zero angles, unit + speed and unit density, in newtons per radian. + + Taken by central difference of the surface's own + ``compute_forces_and_moments``, as the flight computes it. A surface whose + axial force is even in the angle, or absent, gives exactly zero. + + Parameters + ---------- + surface : GenericSurface + The surface. + cp : Vector + Its force application point relative to the rocket's center of dry + mass, in the body frame. + mach : float + Free-stream Mach number. + plane : str, optional + ``"pitch"`` or ``"yaw"``. Default ``"pitch"``. + step : float, optional + Half-step, in radians, of the central difference. Default ``1e-4``. + + Returns + ------- + float + ``dR3/dalpha`` (pitch) or ``dR3/dbeta`` (yaw). + """ + no_rotation = Vector([0.0, 0.0, 0.0]) + + def axial_force(alpha, beta): + stream = Vector([-math.tan(beta), -math.tan(alpha), -1.0]) + stream = stream / abs(stream) + return surface.compute_forces_and_moments( + stream, 1.0, mach, 1.0, cp, no_rotation, _UNIT_DENSITY, _NO_VISCOSITY, 0.0 + )[2] + + if plane == "yaw": + return (axial_force(0.0, step) - axial_force(0.0, -step)) / (2 * step) + return (axial_force(step, 0.0) - axial_force(-step, 0.0)) / (2 * step) + + +def moment_slopes_left_out(rocket, surface, position, lift_slope, side_slope): + """Parts of one surface's pitch and yaw moment slopes that the rocket's + aerodynamic center leaves out. + + The aerodynamic center averages the surfaces' own centers, each weighted + by its force slope, so it reads a surface's whole moment as its force slope + times an arm along the rocket's axis. Three parts of the moment do not fit + that reading: + + - the moment of a surface with no force slope (a pure couple), whose + weight is zero; + - the axial force of a surface that changes with the angle, acting at a + sideways offset from the center of dry mass (a canted fin); + - the shift of an individual fin's position along the axis caused by its + cant angle. + + Each part is exactly zero for a surface without it, so adding them leaves + every other rocket's result unchanged. + + Parameters + ---------- + rocket : rocketpy.Rocket + The rocket the surface is on. Its ``surfaces_cp_to_cdm`` must be + current. + surface : GenericSurface + The surface. + position : Vector + Where the surface was added to the rocket, in the user frame. + lift_slope, side_slope : Function + The surface's pitch force slope ``cN_alpha`` and signed yaw force slope + ``-cY_beta``, as functions of Mach, without the reference-area factor. + + Returns + ------- + tuple + ``(pitch, yaw)``: each a :class:`Function` of Mach giving the missing + moment slope in meters times the rocket's force coefficient (the units + of the weighted sum behind the aerodynamic center), or ``None`` when the + surface has nothing missing in that plane. + """ + # pylint: disable=import-outside-toplevel,protected-access + from rocketpy.rocket.aero_surface.fins.fin import Fin + + def _is_uncanted_fin(surface): + # An uncanted fin pushes straight across the axis, with no axial force. + # Read on each call, as a controller may change the cant. + return isinstance(surface, Fin) and surface.cant_angle_rad == 0 + + csys = rocket._csys + ref_factor = surface.reference_area / rocket.area + force_scale = 0.5 * rocket.area # unit speed and density + pitch_terms, yaw_terms = [], [] + + if isinstance(surface, Fin): + # The cant moves the fin's origin along the axis; read on each call so + # a cant changed by a controller is followed + def shift(): + return rocket._surface_origin(surface, position).z - position.z + + pitch_terms.append(lambda mach: ref_factor * lift_slope(mach) * shift()) + yaw_terms.append(lambda mach: ref_factor * side_slope(mach) * shift()) + + if not isinstance(surface, _BarrowmanSurface): + # Barrowman surfaces carry their moment in the application point, so + # their moment slopes are zero + couple = csys * ref_factor * surface.reference_length + + def pitch_couple(mach): + args = (0.0, 0.0, mach, 0.0, 0.0, 0.0, 0.0) + if surface.cN_alpha.get_value_opt(*args) != 0: + return 0.0 + return couple * surface.cm_alpha.get_value_opt(*args) + + def yaw_couple(mach): + args = (0.0, 0.0, mach, 0.0, 0.0, 0.0, 0.0) + if surface.cY_beta.get_value_opt(*args) != 0: + return 0.0 + return -couple * surface.cn_beta.get_value_opt(*args) + + pitch_terms.append(pitch_couple) + yaw_terms.append(yaw_couple) + + # The axial force acts at the application point's sideways offset. The + # offset is read on each call, as the flight reads it. + offset = rocket.surfaces_cp_to_cdm[surface] + if isinstance(surface, Fin) or offset[1] != 0: + + def pitch_axial(mach): + if _is_uncanted_fin(surface): + return 0.0 + cp = rocket.surfaces_cp_to_cdm[surface] + return csys * cp[1] * axial_force_slope(surface, cp, mach) / force_scale + + pitch_terms.append(pitch_axial) + if isinstance(surface, Fin) or offset[0] != 0: + + def yaw_axial(mach): + if _is_uncanted_fin(surface): + return 0.0 + cp = rocket.surfaces_cp_to_cdm[surface] + slope = axial_force_slope(surface, cp, mach, "yaw") + return csys * cp[0] * slope / force_scale + + yaw_terms.append(yaw_axial) + + def combine(terms, label): + if not terms: + return None + return Function(lambda mach: sum(term(mach) for term in terms), "Mach", label) + + return ( + combine(pitch_terms, "Moment slope left out (m)"), + combine(yaw_terms, "Moment slope left out - Yaw (m)"), + ) + + +def neutral_point_and_slope(rocket, alpha, beta, mach, plane="pitch", step=1e-4): + """Local (tangent) neutral point and force-curve slope at a finite incidence. + + Generalizes the aerodynamic center to a non-zero angle of attack. The neutral + point is the point about which the aerodynamic moment does not change for a + *small* perturbation of the incidence angle around the given ``(alpha, beta)`` + state, i.e. the tangent of the moment-versus-force curve at that state. It is + obtained by central-differencing the rocket's summed body-frame force and + moment (about the center of dry mass) with respect to the plane's incidence + angle, then forming ``x_cdm + csys * L_ref * (dCm/da) / (dCN/da)``. + + For a rocket whose surfaces are all linear in incidence (the built-in + Barrowman surfaces) the result is independent of ``alpha``/``beta`` and equals + :attr:`rocketpy.Rocket.aerodynamic_center`. It moves with incidence only when + a surface's normal-force coefficient is nonlinear in the incidence angle (for + example a Galejs ``sin**2(alpha)`` body-lift term added as a + :class:`rocketpy.GenericSurface`). + + Parameters + ---------- + rocket : rocketpy.Rocket + The rocket to evaluate. + alpha, beta : float + Angle of attack and sideslip angle, in radians, defining the state the + neutral point is taken about. + mach : float + Free-stream Mach number. + plane : str, optional + ``"pitch"`` (perturb ``alpha``, use the normal force and pitch moment) or + ``"yaw"`` (perturb ``beta``, use the side force and yaw moment). Default + ``"pitch"``. + step : float, optional + Half-step, in radians, of the central difference. Default ``1e-4``. + + Returns + ------- + tuple of float + ``(neutral_point, slope)``: the neutral-point axial position in the + user-defined rocket frame, and the force-curve slope ``dCN/da`` (pitch) + or ``dCY/db`` (yaw) at the state. When the slope vanishes (no lift at all) + the neutral point falls back to the zero-incidence aerodynamic center. + """ + rocket.evaluate_surfaces_cp_to_cdm() + reference_length = 2 * rocket.radius + dynamic_pressure_area = 0.5 * rocket.area # unit speed, unit density + dynamic_pressure_area_length = dynamic_pressure_area * reference_length + + def coefficients(a, b): + r1, r2, _, m1, m2, _ = summed_force_and_moment( + rocket, a, b, mach, (0.0, 0.0, 0.0) + ) + if plane == "yaw": + return r1 / dynamic_pressure_area, m2 / dynamic_pressure_area_length + return -r2 / dynamic_pressure_area, m1 / dynamic_pressure_area_length + + if plane == "yaw": + force_high, moment_high = coefficients(alpha, beta + step) + force_low, moment_low = coefficients(alpha, beta - step) + else: + force_high, moment_high = coefficients(alpha + step, beta) + force_low, moment_low = coefficients(alpha - step, beta) + + force_slope = (force_high - force_low) / (2 * step) + moment_slope = (moment_high - moment_low) / (2 * step) + if force_slope == 0: + center = ( + rocket.aerodynamic_center_yaw + if plane == "yaw" + else rocket.aerodynamic_center + ) + return center.get_value_opt(mach), 0.0 + neutral_point = rocket.center_of_dry_mass_position + ( + rocket._csys * reference_length * moment_slope / force_slope + ) + return neutral_point, force_slope + + +def stability_margin_and_slope(rocket, alpha, beta, mach, time, plane="pitch"): + """Stability margin (in calibers) and local restoring-force slope of one + plane of ``rocket`` at a single flow state, the shared computation behind + ``Rocket.stability_margin`` and the flight dynamic-stability oscillator. + + The state is the full pair ``(alpha, beta)``. When a surface is + nonlinear in incidence the linearization of one plane depends on the + angle in the other: an axisymmetric rocket read in the plane of the + wind, at ``(theta, 0)``, gives the in-plane (tangent) values from the + pitch plane and the across-the-wind (secant, ``Cm/CN``) values from the + yaw plane, and they differ. + + For a rocket that is linear in incidence the neutral point is the + zero-incidence :attr:`rocketpy.Rocket.aerodynamic_center`, both angles are + ignored, and the fast analytic path is kept. Otherwise the neutral point + and slope are found at the state by :func:`neutral_point_and_slope`. + + Parameters + ---------- + rocket : rocketpy.Rocket + The rocket to evaluate. + alpha, beta : float + Angle of attack and sideslip angle, in radians, of the state. + mach : float + Free-stream Mach number. + time : float + Flight time, in seconds, at which the center of mass is taken. + plane : str, optional + ``"pitch"`` or ``"yaw"``. Default ``"pitch"``. + + Returns + ------- + tuple of float + ``(margin, slope)``: the stability margin in calibers and the local + slope of the restoring force, ``dCN/dalpha`` for pitch and + ``-dCY/dbeta`` for yaw. Both are positive for a surface that pushes + the rocket back, so the corrective moment ``q A slope margin d`` is + positive for a stable rocket in either plane. + """ + # pylint: disable=protected-access + rocket._refresh_margins() + if plane == "yaw": + center = rocket.aerodynamic_center_yaw + slope_curve = rocket.total_side_coeff_der + else: + center = rocket.aerodynamic_center + slope_curve = rocket.total_lift_coeff_der + + if rocket._is_incidence_linear: + neutral_point = center.get_value_opt(mach) + slope = slope_curve.get_value_opt(mach) + else: + neutral_point, slope = neutral_point_and_slope(rocket, alpha, beta, mach, plane) + if plane == "yaw": + # A restoring side force has a negative ``dCY/dbeta``. The + # slope is given the sign of the pitch plane, as + # ``total_side_coeff_der`` is, so that it is positive for a + # surface that pushes the rocket back. + slope = -slope + + margin = ( + rocket._csys + * (rocket.center_of_mass.get_value_opt(time) - neutral_point) + / (2 * rocket.radius) + ) + return margin, slope + + +def center_of_pressure_position(rocket, alpha, beta, mach, plane="pitch"): + """Point where the resultant aerodynamic force of one plane acts at a flow + state: the moment about the center of dry mass divided by the force, + ``x_cdm + csys * L_ref * Cm / CN`` for pitch (``Cn / CY`` for yaw). + + Unlike :func:`neutral_point_and_slope`, which weights each surface by how + fast its force grows with the angle, this weights each surface by its force + itself. The two agree for a rocket linear in the angle with no force at zero + angle. + + Parameters + ---------- + rocket : rocketpy.Rocket + The rocket to evaluate. + alpha, beta : float + Angle of attack and sideslip angle, in radians, of the flow state. + mach : float + Free-stream Mach number. + plane : str, optional + ``"pitch"`` (normal force and pitch moment) or ``"yaw"`` (side force + and yaw moment). Default ``"pitch"``. + + Returns + ------- + float + Axial position of the center of pressure in the user-defined rocket + frame, in meters. Where the plane's force vanishes (zero angle on a + symmetric rocket) the point is undefined and the zero-incidence + aerodynamic center, its limit for such a rocket, is returned. + """ + rocket.evaluate_surfaces_cp_to_cdm() + r1, r2, _, m1, m2, _ = summed_force_and_moment( + rocket, alpha, beta, mach, (0.0, 0.0, 0.0) + ) + force, moment = (r1, m2) if plane == "yaw" else (-r2, m1) + # Unit speed and density: the force coefficient is force / (0.5 * area) + if abs(force) <= _ZERO * 0.5 * rocket.area: + center = ( + rocket.aerodynamic_center_yaw + if plane == "yaw" + else rocket.aerodynamic_center + ) + return center.get_value_opt(mach) + return rocket.center_of_dry_mass_position + rocket._csys * moment / force + + +def damping_derivative( + rocket, alpha, beta, mach, plane="pitch", reference_z=0.0, step=1e-3 +): + """Aerodynamic damping of ``rocket`` in one plane at a flow state, per unit + air density and airspeed. + + It is ``-dM/domega``, the change of the summed aerodynamic moment for a small + body rate about the reference point, taken at unit density and speed by a + central difference of :func:`summed_force_and_moment` around zero rate. + Multiplied by ``rho * V`` it is the damping moment coefficient ``C2`` of the + flight (N m s/rad). It includes whatever the surfaces feel from a rate: the + lever arm of each surface with its force slope at ``(alpha, beta)``, and any + coefficient that depends on a rate. + + Parameters + ---------- + rocket : rocketpy.Rocket + The rocket to evaluate. + alpha, beta : float + Angle of attack and sideslip angle, in radians, of the state the damping + is taken about. + mach : float + Free-stream Mach number. + plane : str, optional + ``"pitch"`` (rate and moment about the body x axis) or ``"yaw"`` (about + the body y axis). Default ``"pitch"``. + reference_z : float, optional + Position of the point the rocket rotates about, in meters ahead of the + center of dry mass along the body axis. Default ``0.0``. + step : float, optional + Half-step of the central difference, in rad/s at unit speed. Default + ``1e-3``. + + Returns + ------- + float + ``-dM/domega`` at unit density and speed, in m**4 (positive when the + moment opposes the rate). + """ + rocket.evaluate_surfaces_cp_to_cdm() + axis = 1 if plane == "yaw" else 0 + + def moment(rate): + omega = [0.0, 0.0, 0.0] + omega[axis] = rate + return summed_force_and_moment( + rocket, alpha, beta, mach, omega, reference_z=reference_z + )[3 + axis] + + return -(moment(step) - moment(-step)) / (2 * step) + + +def aerodynamic_damping(rocket, alpha, beta, mach, time, plane="pitch"): + """Aerodynamic damping of ``rocket`` in the ``"pitch"`` or ``"yaw"`` plane, + per unit air density and airspeed, at a state ``(alpha, beta)`` (rad), Mach + and time. Times ``rho * V`` it is the aerodynamic part of the flight's + damping moment coefficient ``C2``. + + For a rocket linear in the angle and without rate coefficients it is + ``0.5 A sum((A_i / A) slope_i arm_i**2)`` over the surfaces of the + stability phase, with the zero-angle slopes and aerodynamic centers (a + surface with a negative slope, such as a boat tail, takes damping + away). Otherwise the summed moment is differenced with respect to the + body rate at the state, about the center of mass (see + :func:`damping_derivative`). + """ + # pylint: disable=protected-access + rocket._refresh_margins() + center_of_mass = rocket.center_of_mass.get_value_opt(time) + if rocket._is_incidence_linear and not rocket._uses_rate_coefficients: + damping = 0.0 + for surface, position in stability_surfaces(rocket): + if plane == "yaw": + # A restoring side force has a negative dCY/dbeta + slope = -surface.cY_beta.get_value_opt(0, 0, mach, 0, 0, 0, 0) + center = surface.aerodynamic_center_yaw + else: + slope = surface.cN_alpha.get_value_opt(0, 0, mach, 0, 0, 0, 0) + center = surface.aerodynamic_center + arm = ( + position.z + rocket._csys * center.get_value_opt(mach) - center_of_mass + ) + damping += surface.reference_area / rocket.area * slope * arm**2 + return 0.5 * rocket.area * damping + # Body-axis position of the center of mass ahead of the center of dry mass + reference_z = -rocket.com_to_cdm_function.get_value_opt(time) + return damping_derivative(rocket, alpha, beta, mach, plane, reference_z) + + +# The attitude oscillator: I_L theta'' + C2 theta' + C1 theta = 0 + + +def lateral_inertia_and_rate(rocket, inertia_about_cdm, time): + """Lateral moment of inertia of ``rocket`` about its center of mass at + ``time`` and its rate of change, from ``inertia_about_cdm`` (the rocket's + ``I_11`` or ``I_22``, a Function of time about the center of dry mass). + The rate is negative while propellant is consumed and enters the jet + damping.""" + offset = rocket.com_to_cdm_function + total_mass = rocket.total_mass.get_value_opt(time) + mass_rate = rocket.total_mass_flow_rate.get_value_opt(time) + cm_to_cdm = offset.get_value_opt(time) + cm_to_cdm_rate = offset.differentiate_complex_step(time) + # Parallel axis from the center of dry mass to the center of mass, and its + # time derivative + inertia = inertia_about_cdm.get_value_opt(time) - total_mass * cm_to_cdm**2 + rate = ( + inertia_about_cdm.differentiate_complex_step(time) + - mass_rate * cm_to_cdm**2 + - 2 * total_mass * cm_to_cdm * cm_to_cdm_rate + ) + return inertia, rate + + +def corrective_and_damping_moments( + rocket, + alpha, + beta, + mach, + time, + speed, + density, + dynamic_pressure, + inertia_rate, + plane="pitch", +): + """Corrective moment coefficient ``C1`` (N m/rad) and damping moment + coefficient ``C2`` (N m s/rad) of one plane of ``rocket`` at a flow state + ``(alpha, beta)`` (rad), Mach number, time, airspeed (m/s), air density + (kg/m^3) and dynamic pressure (Pa). ``inertia_rate`` is the rate of change + of the lateral inertia (see :func:`lateral_inertia_and_rate`).""" + margin, force_slope = stability_margin_and_slope( + rocket, alpha, beta, mach, time, plane + ) + damping_aero = ( + density * speed * aerodynamic_damping(rocket, alpha, beta, mach, time, plane) + ) + + # Corrective moment per radian: q A C_Nalpha (z_cm - z_np). + corrective = ( + dynamic_pressure * rocket.area * force_slope * margin * (2 * rocket.radius) + ) + + # Jet (propulsive) damping. The exhaust leaves the nozzle moving sideways + # with the rocket and carries angular momentum away, + # |mdot| (z_nozzle - z_cm)^2 per unit rate; the lateral inertia lost with + # the consumed propellant gives part of it back, dI/dt (negative). + # Together they are Thomson's mdot (l_n^2 - l_p^2). + center_of_mass = rocket.center_of_mass.get_value_opt(time) + damping_jet = ( + abs(rocket.motor.total_mass_flow_rate.get_value_opt(time)) + * (rocket.nozzle_position - center_of_mass) ** 2 + + inertia_rate + ) + return corrective, damping_aero + damping_jet + + +def disturbance_response( + corrective, damping, inertia, disturbance, duration=None, samples=500 +): + """Angle of a rocket after a sudden disturbance, as a Function of the time + since it: the solution of ``I_L theta'' + C2 theta' + C1 theta = 0`` that + starts at the angle ``disturbance`` with no rotation rate, with the three + coefficients held fixed. + + The solution is written from the two roots of ``I_L s^2 + C2 s + C1``, so + it also covers a rocket with no restoring moment (the angle then grows). + ``duration`` defaults to the time the response takes to settle: four times + the slowest decay time, kept between 3 and 15 oscillation periods. + """ + if inertia <= 0: + raise ValueError( + "The rocket has no lateral inertia (a point mass rocket), so it " + "has no attitude to disturb." + ) + discriminant = complex(damping**2 - 4 * inertia * corrective) + root_1 = (-damping + cmath.sqrt(discriminant)) / (2 * inertia) + root_2 = (-damping - cmath.sqrt(discriminant)) / (2 * inertia) + decay = max(root_1.real, root_2.real) # the slowest root; negative: settles + frequency = abs(root_1.imag) + + if duration is None: + if decay < 0: + duration = 4 / -decay + if frequency > 0: + period = 2 * math.pi / frequency + duration = min(max(duration, 3 * period), 15 * period) + elif decay > 0: + duration = math.log(10) / decay # until the angle grows tenfold + else: + duration = 10 * 2 * math.pi / frequency if frequency > 0 else 5.0 + + time = np.linspace(0, duration, samples) + if abs(root_1 - root_2) > 1e-12 * max(abs(root_1), abs(root_2), 1e-300): + angle = (root_2 * np.exp(root_1 * time) - root_1 * np.exp(root_2 * time)) / ( + root_2 - root_1 + ) + else: # a repeated root: critically damped + angle = np.exp(root_1 * time) * (1 - root_1 * time) + angle = disturbance * np.real(angle) + + if corrective > 0: + natural_frequency = math.sqrt(corrective / inertia) + damping_ratio = damping / (2 * math.sqrt(corrective * inertia)) + title = ( + f"Response to a {disturbance:g}° disturbance (natural frequency " + f"{natural_frequency:.2f} rad/s, damping ratio {damping_ratio:.3f})" + ) + else: + title = f"Response to a {disturbance:g}° disturbance (no restoring moment)" + return Function( + np.column_stack((time, angle)), + "Time after the disturbance (s)", + "Angle (°)", + interpolation="linear", + extrapolation="constant", + title=title, + ) + + +# The rocket as one set of coefficients (``to_coefficients`` / ``to_surface``) + +_LUMPED_COEFFICIENTS = ("cN", "cY", "cA", "cm", "cn", "cl") +# Derivative suffix -> the state variable it perturbs +_ANGLE_TERMS = {"alpha": "alpha", "beta": "beta"} +_RATE_TERMS = {"p": "roll", "q": "pitch", "r": "yaw"} +_RATE_NAMES = {"p": "roll_rate", "q": "pitch_rate", "r": "yaw_rate"} +_SLOPE_STEP = 1e-5 +_ZERO = 1e-12 + + +def _surfaces_evaluator(rocket, phase): + """A function giving the six body-frame coefficients ``(cN, cY, cA, cm, + cn, cl)`` of the surfaces active during ``phase`` at a state: Mach + number, flow angles, reduced rates and Reynolds number (of the rocket, + based on its diameter; ``None`` for the vanishing-Reynolds limit).""" + reference_length = 2 * rocket.radius + dynamic_pressure_area = 0.5 * rocket.area # unit speed, unit density + dynamic_pressure_area_length = dynamic_pressure_area * reference_length + rate_factor = 2.0 / reference_length # reduced rate -> rate at unit speed + + def coefficients_at( + mach, alpha=0.0, beta=0.0, pitch=0.0, yaw=0.0, roll=0.0, reynolds=None + ): + omega = (pitch * rate_factor, yaw * rate_factor, roll * rate_factor) + r1, r2, r3, m1, m2, m3 = summed_force_and_moment( + rocket, alpha, beta, mach, omega, phase=phase, reynolds=reynolds + ) + return np.array( + [ + -r2 / dynamic_pressure_area, + r1 / dynamic_pressure_area, + -r3 / dynamic_pressure_area, + m1 / dynamic_pressure_area_length, + m2 / dynamic_pressure_area_length, + m3 / dynamic_pressure_area_length, + ] + ) + + return coefficients_at + + +def _drag_evaluator(rocket, phase): + """Like :func:`_surfaces_evaluator`, for the rocket's own drag coefficient + of ``phase``, which is an axial force.""" + drag = getattr(rocket, f"{phase}_drag_7d").get_value_opt + + def coefficients_at( + mach, alpha=0.0, beta=0.0, pitch=0.0, yaw=0.0, roll=0.0, reynolds=None + ): + axial = drag(alpha, beta, mach, reynolds or 0.0, pitch, yaw, roll) + return np.array([0.0, 0.0, axial, 0.0, 0.0, 0.0]) + + return coefficients_at + + +def _slopes_at(evaluate, mach, reynolds, terms, **state): + """The slope of the six coefficients with each variable in ``terms`` + (suffix to state name) at ``state`` (the flow angles; the rates are zero), + as one array of six per suffix.""" + slopes = {} + for suffix, variable in terms.items(): + base = state.get(variable, 0.0) + high = evaluate( + mach, reynolds=reynolds, **{**state, variable: base + _SLOPE_STEP} + ) + low = evaluate( + mach, reynolds=reynolds, **{**state, variable: base - _SLOPE_STEP} + ) + slopes[suffix] = (high - low) / (2 * _SLOPE_STEP) + return slopes + + +# The lumping runs over a grid of conditions: the Mach numbers and, when asked +# for, the Reynolds numbers and the values of each control. ``shape`` is the +# shape of that grid and ``condition(index)`` moves the rocket's controls to +# the condition at ``index`` and returns its ``(mach, reynolds)``. + + +def _linear_lumping(evaluate, shape, condition, rates): + """The derivatives of the linear model on the grid of conditions: the + value at zero (``_0``), the angle slopes and, when ``rates``, the rate + slopes.""" + terms = {**_ANGLE_TERMS, **(_RATE_TERMS if rates else {})} + values = { + f"{coefficient}_{suffix}": np.empty(shape) + for coefficient in _LUMPED_COEFFICIENTS + for suffix in ("0", *terms) + } + for index in np.ndindex(*shape): + mach, reynolds = condition(index) + at_zero = evaluate(mach, reynolds=reynolds) + for coefficient, value in zip(_LUMPED_COEFFICIENTS, at_zero): + values[f"{coefficient}_0"][index] = value + for suffix, slopes in _slopes_at(evaluate, mach, reynolds, terms).items(): + for coefficient, slope in zip(_LUMPED_COEFFICIENTS, slopes): + values[f"{coefficient}_{suffix}"][index] = slope + return values + + +def _angle_axes(angles, axisymmetric): + """The angle axes of the table model and their names. An axisymmetric + rocket is swept in one plane, over the total angle of attack.""" + if axisymmetric: + return [np.unique(np.abs(angles))], ["alpha_total"] + return [angles, angles], ["alpha", "beta"] + + +def _table_lumping(evaluate, angle_axes, shape, condition, rates): + """The six coefficients on the grid of the angles and the conditions, read + at zero rates, keyed by name, plus the rate slopes when ``rates``: on the + grid of conditions at zero angle, or, for ``rates="at_each_angle"``, on + the whole grid.""" + angle_shape = tuple(len(axis) for axis in angle_axes) + at_each_angle = rates == "at_each_angle" + values = np.empty(angle_shape + tuple(shape) + (6,)) + rate_shape = values.shape if at_each_angle else tuple(shape) + (6,) + rate_values = {suffix: np.empty(rate_shape) for suffix in _RATE_TERMS} + for index in np.ndindex(*values.shape[:-1]): + mach, reynolds = condition(index[len(angle_shape) :]) + angles_at = (axis[i] for axis, i in zip(angle_axes, index)) + state = dict(zip(("alpha", "beta"), angles_at)) + values[index] = evaluate(mach, reynolds=reynolds, **state) + if at_each_angle: + slopes = _slopes_at(evaluate, mach, reynolds, _RATE_TERMS, **state) + for suffix, slope in slopes.items(): + rate_values[suffix][index] = slope + if rates and not at_each_angle: + for index in np.ndindex(*shape): + mach, reynolds = condition(index) + slopes = _slopes_at(evaluate, mach, reynolds, _RATE_TERMS) + for suffix, slope in slopes.items(): + rate_values[suffix][index] = slope + tables = { + coefficient: values[..., i] + for i, coefficient in enumerate(_LUMPED_COEFFICIENTS) + } + rate_tables = {} + if rates: + rate_tables = { + f"{coefficient}_{suffix}": rate_values[suffix][..., i] + for i, coefficient in enumerate(_LUMPED_COEFFICIENTS) + for suffix in _RATE_TERMS + } + return tables, rate_tables + + +def _mach_curve(machs, values, name): + """A Function of Mach through the tabulated ``values``: a constant for a + single Mach number, linear between two, Akima beyond.""" + if len(machs) == 1: + return Function(float(values[0]), "Mach", name) + return Function( + np.column_stack([machs, values]), + "Mach", + name, + interpolation="akima" if len(machs) > 2 else "linear", + extrapolation="constant", + ) + + +def _grid_table(axes, names, values, name): + """A Function interpolating linearly on the regular grid ``axes`` (one + array per variable, named after ``names``), holding its edge values + outside it.""" + return Function( + _RegularGrid.points_from_axes(axes, values), + list(names), + [name], + interpolation="linear", + extrapolation="constant", + ) + + +def _condition_curve(axes, names, values, name): + """A Function over the grid of conditions: a curve over Mach when Mach is + the only one, a table otherwise.""" + if len(axes) == 1: + return _mach_curve(axes[0], values, name) + return _grid_table(axes, names, values, name) + + +def _control_axes(rocket, controls): + """Match the ``controls`` asked for (name to values) with the controls of + the rocket's surfaces. + + Returns one ``(name, targets, values)`` per control axis of the lumped + coefficients, ``targets`` being the ``(surface, control)`` pairs it moves. + A control name used by one surface keeps its name. A name used by several + surfaces gives one axis per surface, named ``_``; asking + for the shared name sweeps each of them over the same values, with a + warning. + """ + owners = {} + for surface, _ in rocket.aerodynamic_surfaces: + for control in getattr(surface, "control_variables", ()): + owners.setdefault(control, []).append(surface) + + def slug(text): + return re.sub(r"\W+", "_", str(text)).strip("_").lower() + + # The name of each control once lumped + lumped = {} + for control, surfaces in owners.items(): + if len(surfaces) == 1: + lumped[control] = [(surfaces[0], control)] + continue + slugs = [slug(surface.name) for surface in surfaces] + for i, (surface, prefix) in enumerate(zip(surfaces, slugs)): + if slugs.count(prefix) > 1: + prefix = f"{prefix}_{i + 1}" + lumped[f"{prefix}_{control}"] = [(surface, control)] + + axes = [] + for name, values in controls.items(): + values = np.unique(np.asarray(values, dtype=float)) + if len(values) < 2: + raise ValueError( + f"Give at least two different values for the control '{name}'." + ) + if name in lumped: + axes.append((name, lumped[name], values)) + elif name in owners: + separate = [ + key + for key, targets in lumped.items() + if targets[0][1] == name and targets[0][0] in owners[name] + ] + warnings.warn( + f"The control '{name}' is used by {len(separate)} surfaces. Each " + "is kept as a separate control of the result, swept over the " + f"same values: {', '.join(separate)}. Give these names in " + "`controls` to choose the values of each.", + UserWarning, + stacklevel=4, + ) + axes.extend((key, lumped[key], values) for key in separate) + else: + raise ValueError( + f"The rocket has no control named '{name}'. Its controls are: " + f"{sorted(lumped) or 'none'}." + ) + return axes + + +def full_body_coefficients( + rocket, + machs=None, + force_convention="body", + model="linear", + angles=None, + rates=True, + reynolds=None, + controls=None, +): + """Compute the rocket's lumped coefficient set, split by motor phase. Backs + :meth:`rocketpy.Rocket.to_coefficients`; see that method for the full + description of the two models and their limitations. + """ + # pylint: disable=too-many-statements + if force_convention not in ("body", "wind"): + raise ValueError( + f"force_convention must be 'body' or 'wind', got {force_convention!r}." + ) + if model not in ("linear", "table"): + raise ValueError(f"model must be 'linear' or 'table', got {model!r}.") + if model == "table" and force_convention == "wind": + raise ValueError( + "The table model gives the body-frame coefficients only; use " + "force_convention='body'." + ) + if not (rates is True or rates is False or rates == "at_each_angle"): + raise ValueError( + f"rates must be True, False or 'at_each_angle', got {rates!r}." + ) + if rates == "at_each_angle" and model != "table": + raise ValueError("rates='at_each_angle' needs model='table'.") + if machs is None: + machs = np.arange(0.0, 3.01, 0.02 if model == "linear" else 0.05) + machs = np.asarray(machs, dtype=float) + if angles is None: + angles = np.radians(np.arange(-30.0, 31.0, 2.0)) + angles = np.asarray(angles, dtype=float) + + # The conditions the coefficients are read at, besides the angles + fixed_reynolds = None + condition_axes, condition_names = [machs], ["mach"] + if reynolds is not None: + reynolds = np.unique(np.atleast_1d(np.asarray(reynolds, dtype=float))) + if len(reynolds) == 1: + fixed_reynolds = float(reynolds[0]) + else: + condition_axes.append(reynolds) + condition_names.append("reynolds") + sweeps_reynolds = len(condition_axes) == 2 + control_axes = _control_axes(rocket, controls or {}) + for name, _, values in control_axes: + condition_axes.append(values) + condition_names.append(name) + if len(machs) < 2 and (model == "table" or len(condition_axes) > 1): + raise ValueError( + "The coefficients are interpolated over Mach, so `machs` needs at " + "least two values." + ) + shape = tuple(len(axis) for axis in condition_axes) + + def condition(index): + mach_index, *others = index + reynolds_at = fixed_reynolds + if sweeps_reynolds: + reynolds_at = reynolds[others.pop(0)] + for (_, targets, values), i in zip(control_axes, others): + for surface, control in targets: + surface.set_control(control, values[i]) + return machs[mach_index], reynolds_at + + # Make sure each surface's center-of-pressure offset is current. + rocket.evaluate_surfaces_cp_to_cdm() + # A deflected control or a rate at an angle breaks the single-plane sweep + axisymmetric = ( + model == "table" + and rocket.is_axisymmetric + and not control_axes + and rates != "at_each_angle" + ) + angle_axes, angle_names = _angle_axes(angles, axisymmetric) + + def lump(evaluate): + if model == "linear": + return _linear_lumping(evaluate, shape, condition, rates), {} + return _table_lumping(evaluate, angle_axes, shape, condition, rates) + + def package(values, rate_values): + if model == "linear": + if force_convention == "wind": + values = body_derivatives_to_wind(values) + return { + name: _condition_curve(condition_axes, condition_names, array, name) + for name, array in values.items() + if np.any(np.abs(array) > _ZERO) + } + axes, names = angle_axes + condition_axes, angle_names + condition_names + kept = ("cN", "cA", "cm", "cl") if axisymmetric else _LUMPED_COEFFICIENTS + packaged = { + name: _grid_table(axes, names, array, name) + for name, array in values.items() + if name in kept + } + for name, array in rate_values.items(): + if not np.any(np.abs(array) > _ZERO): + continue + if rates == "at_each_angle": + packaged[name] = _grid_table(axes, names, array, name) + else: + packaged[name] = _condition_curve( + condition_axes, condition_names, array, name + ) + return packaged + + # The sweep moves the controls of the rocket's surfaces; put them back + moved = { + (surface, control): surface.control_state[control] + for _, targets, _ in control_axes + for surface, control in targets + } + # The rocket's own drag coefficient differs by phase; the surfaces do so + # only when one is gated to a phase, so their sweep is shared otherwise + by_surfaces = {} + result = {} + try: + for phase in ("power_off", "power_on"): + surfaces = tuple( + surface for surface, _ in stability_surfaces(rocket, phase) + ) + if surfaces not in by_surfaces: + by_surfaces[surfaces] = lump(_surfaces_evaluator(rocket, phase)) + drag = lump(_drag_evaluator(rocket, phase)) + values, rate_values = ( + {name: array + of_drag[name] for name, array in of_surfaces.items()} + for of_surfaces, of_drag in zip(by_surfaces[surfaces], drag) + ) + result[phase] = package(values, rate_values) + finally: + for (surface, control), value in moved.items(): + surface.control_state[control] = value + return result + + +_FLOW_INPUTS = ( + "alpha", + "beta", + "alpha_total", + "phi", + "mach", + "reynolds", + *_RATE_NAMES.values(), +) + + +def _input_names(function): + """The names of the inputs of a lumped coefficient, in order.""" + return [str(name).lower() for name in function.__inputs__] + + +def lumped_control_names(coefficients): + """The control inputs of a coefficient set of the table model, in order.""" + return [ + name for name in _input_names(coefficients["cN"]) if name not in _FLOW_INPUTS + ] + + +def lumped_surface_coefficients(coefficients): + """The coefficient input of the surface that carries one phase of + :func:`full_body_coefficients` with ``model="table"``: each of the six + coefficients is its table plus its rate terms times the reduced rates. + Every coefficient takes the inputs of its table and of its rate terms + (angles, Mach and, when present, Reynolds number and controls), then the + reduced rates. + + An axisymmetric set (tables over the total angle of attack) is split + between the pitch and yaw planes with the roll angle of the wind, as the + generic surface does against the total angle: ``cN = table * sin(phi)``, + ``cY = -table * cos(phi)``, and likewise ``cm``/``cn``. + """ + axisymmetric = "cY" not in coefficients + # Along-the-crossflow coefficients of an axisymmetric set read the total + # angle and the roll angle of the wind + source_of = {"cY": "cN", "cn": "cm"} if axisymmetric else {} + direction = {} + if axisymmetric: + direction = { + "cN": math.sin, + "cm": math.sin, + "cY": lambda phi: -math.cos(phi), + "cn": lambda phi: -math.cos(phi), + } + + def build(coefficient): + table = coefficients[source_of.get(coefficient, coefficient)] + turn = direction.get(coefficient) + terms = [ + (_RATE_NAMES[suffix], coefficients[f"{coefficient}_{suffix}"]) + for suffix in _RATE_TERMS + if f"{coefficient}_{suffix}" in coefficients + ] + if turn is None and not terms: + return table + table_names = _input_names(table) + names = list(table_names) + if turn is not None: + names.insert(1, "phi") + for _, term in terms: + names.extend(name for name in _input_names(term) if name not in names) + names.extend(rate for rate, _ in terms) + read_table = table.get_value_opt + table_at = [names.index(name) for name in table_names] + phi_at = names.index("phi") if turn is not None else None + terms_at = [ + ( + term.get_value_opt, + [names.index(name) for name in _input_names(term)], + names.index(rate), + ) + for rate, term in terms + ] + + def value(*args): + total = read_table(*[args[i] for i in table_at]) + if turn is not None: + total *= turn(args[phi_at]) + for read_term, term_at, rate_at in terms_at: + total += read_term(*[args[i] for i in term_at]) * args[rate_at] + return total + + return _as_function(value, names, coefficient) + + return {coefficient: build(coefficient) for coefficient in _LUMPED_COEFFICIENTS} + + +def body_derivatives_to_wind(body): + """Express a body-frame derivative set (``cN_*``/``cY_*``/``cA_*``) in the + wind-frame names (``cL_*``/``cQ_*``/``cD_*``); the moment derivatives are + frame-shared. Four cross terms fold the zero-angle forces in at incidence, + ``cL_alpha = cN_alpha - cA_0``, ``cQ_beta = cY_beta + cA_0``, + ``cD_alpha = cA_alpha + cN_0`` and ``cD_beta = cA_beta - cY_0`` -- the + linear inverse of the wind-to-body rotation :class:`LinearGenericSurface` + applies to a wind-frame input, so feeding the result back with + ``force_convention="wind"`` recovers the same body-frame surface. Operates on + the tabulated derivative values (arrays over the Mach grid). + """ + rename = {"cN": "cL", "cY": "cQ", "cA": "cD"} + wind = {} + for key, value in body.items(): + prefix, sep, suffix = key.partition("_") + wind[f"{rename.get(prefix, prefix)}{sep}{suffix}"] = value + cross_terms = ( + ("cL_alpha", "cN_alpha", "cA_0", -1.0), + ("cQ_beta", "cY_beta", "cA_0", 1.0), + ("cD_alpha", "cA_alpha", "cN_0", 1.0), + ("cD_beta", "cA_beta", "cY_0", -1.0), + ) + for name, first, second, sign in cross_terms: + if first in body or second in body: + wind[name] = body.get(first, 0.0) + sign * body.get(second, 0.0) + return wind diff --git a/rocketpy/rocket/aero_surface/_barrowman_surface.py b/rocketpy/rocket/aero_surface/_barrowman_surface.py index dc161b9e6..a461f5ee1 100644 --- a/rocketpy/rocket/aero_surface/_barrowman_surface.py +++ b/rocketpy/rocket/aero_surface/_barrowman_surface.py @@ -1,39 +1,30 @@ +import math + import numpy as np from rocketpy.mathutils.vector_matrix import Matrix, Vector from rocketpy.rocket.aero_surface.aero_coefficient import AeroCoefficient -from rocketpy.rocket.aero_surface.linear_generic_surface import LinearGenericSurface - - -class _BarrowmanSurface(LinearGenericSurface): - """Intermediate base for Barrowman-defined aerodynamic surfaces - such as nose cones, tails/transitions and fin sets. - - These surfaces expose a lift-curve slope ``clalpha`` (a ``Function`` of - Mach), a geometric center of pressure ``cpz`` and, for fins, a pair of roll - forcing/damping coefficients. - - The in-flight normal force and its moment are computed with the classic - Barrowman method (see :meth:`compute_forces_and_moments`): the normal force - uses the true total angle of attack and acts at the geometric center of - pressure, and its moment about the center of dry mass is the geometric - transport (``cp ^ force``). This reproduces the formulation used in - RocketPy's flight-test validation. The resultant force is therefore reported - at the geometric center of pressure (:attr:`force_application_point`), which - the surface-local frame maps to the body frame through - :meth:`_default_surface_rotation`. - - The class also derives the linear normal-force slopes ``cN_alpha`` (pitch - plane) and ``cY_beta`` (yaw plane), which feed the stability and - center-of-pressure diagnostics; the geometric cp is carried by the force - application point, so the moment slopes ``cm_alpha`` / ``cn_beta`` are zero. - Fin roll uses the coefficient model: ``cl_0`` (cant forcing) and ``cl_p`` - (roll damping). - - Subclasses must compute ``self.clalpha`` (Function of Mach) and the geometric - center of pressure before calling ``super().__init__`` (which passes the - geometric cp through ``center_of_pressure``), and, for fins, set - ``self.roll_parameters = [clf_delta, cld_omega, cant_angle_rad]``. +from rocketpy.rocket.aero_surface.generic_surface import GenericSurface + + +class _BarrowmanSurface(GenericSurface): + """Base class for the surfaces modeled with the Barrowman method: nose + cones, tails/transitions and fins. + + The normal force is ``clalpha(Mach)`` times the total angle of attack and + acts at the geometric center of pressure ``cpz``. Fins add a roll moment + from their cant angle and their roll damping. + + The coefficients ``cN``, ``cY`` and ``cl`` describe that same force, so + what is read or plotted is what flies. + :meth:`compute_forces_and_moments` computes it in a faster way, and a unit + test keeps the two in agreement. The slopes ``cN_alpha`` and ``cY_beta`` + are used for the stability margin and the center of pressure. + + Subclasses must set ``self.clalpha`` (a Function of Mach) and the geometric + center of pressure before calling ``super().__init__``. Fins must also + provide ``roll_parameters``, ``[clf_delta, cld_omega, cant_angle_rad]``, + with the two coefficients taken at zero cant. """ # Geometry-defined Barrowman surfaces are axisymmetric by construction @@ -41,6 +32,21 @@ class _BarrowmanSurface(LinearGenericSurface): # pitch and yaw planes. The individual ``Fin`` overrides this back to False. is_axisymmetric = True + def _geometry_changed(self): + """Rebuild the coefficients after a change to the geometry, and count + the change so the rockets using this surface update too.""" + if not hasattr(self, "cN_alpha"): + return # Still being built: the constructor builds the coefficients + self.evaluate_coefficients() + self._evaluate_stability_derivatives() + self._version += 1 + + def _evaluate_stability_derivatives(self): + """The slopes are set from the geometry (see + :meth:`evaluate_coefficients`), so there is nothing to differentiate: + only the center-of-pressure accessors are built.""" + self._set_stability_accessors() + @staticmethod def _beta(mach): """Prandtl-Glauert compressibility factor used to correct subsonic @@ -80,16 +86,14 @@ def _default_surface_rotation(self): return Matrix([[-1, 0, 0], [0, 1, 0], [0, 0, -1]]) def evaluate_coefficients(self): - """Populate the coefficient slopes used by the stability diagnostics - from the surface geometry. Called by ``GenericSurface.__init__`` and - again whenever the geometry changes. + """Populate the coefficients from the surface geometry. Called by + ``GenericSurface.__init__`` and again whenever the geometry changes. - Sets the normal-force slopes ``cN_alpha`` (pitch) and ``cY_beta`` (yaw) - and the fin roll coefficients when present. The geometric center of + Sets the normal-force slopes ``cN_alpha`` (pitch) and ``cY_beta`` (yaw), + the force coefficients ``cN`` and ``cY`` and, for fins, the roll + coefficients ``cl_0``, ``cl_p`` and ``cl``. The geometric center of pressure is carried by the force application point (not the moment - coefficients), so ``cm_alpha`` / ``cn_beta`` are zero. The in-flight - force and moment are computed geometrically in - :meth:`compute_forces_and_moments`. + coefficients), so ``cm_alpha`` / ``cn_beta`` are zero. """ clalpha = self.clalpha # normal-force-curve slope, a Function of Mach @@ -102,19 +106,46 @@ def evaluate_coefficients(self): ) # The center of pressure is carried by the force application point, so - # the moment slopes add no further offset (the diagnostic recovers the - # geometric cp from the application point alone). + # the moment slopes add no further offset self.cm_alpha = self._mach_coefficient(lambda mach: 0.0, "cm_alpha") self.cn_beta = self._mach_coefficient(lambda mach: 0.0, "cn_beta") - # Fin roll forcing (cant) and damping, when present. + # The classic normal force (see compute_forces_and_moments) written as + # coefficients: ``clalpha * total angle of attack`` in the plane of the + # wind, split between the pitch and yaw planes along the crossflow. + total_normal = self._as_coefficient( + lambda alpha_total, mach: clalpha.get_value_opt(mach) * alpha_total, + "cN", + ) + c_normal, c_side = self._split_along_crossflow(total_normal, ("cN", "cY")) + self.cN = self._as_coefficient(c_normal, "cN") + self.cY = self._as_coefficient(c_side, "cY") + + # Fin roll forcing (cant) and damping, when present. The cant angle is + # read when the coefficient is evaluated, so a controller may change it + # without the coefficients being rebuilt. roll_parameters = getattr(self, "roll_parameters", None) if roll_parameters is not None: - clf_delta, cld_omega, cant_angle_rad = roll_parameters + clf_delta, cld_omega, _ = roll_parameters self.cl_0 = self._mach_coefficient( - lambda mach: clf_delta.get_value_opt(mach) * cant_angle_rad, "cl_0" + lambda mach: clf_delta.get_value_opt(mach) * self._roll_cant_angle_rad, + "cl_0", + ) + self.cl_p = self._mach_coefficient( + lambda mach: ( + cld_omega.get_value_opt(mach) * math.cos(self.cant_angle_rad) + ), + "cl_p", + ) + self.cl = self._as_coefficient( + lambda mach, roll_rate: ( + clf_delta.get_value_opt(mach) * self._roll_cant_angle_rad + + cld_omega.get_value_opt(mach) + * math.cos(self.cant_angle_rad) + * roll_rate + ), + "cl", ) - self.cl_p = self._mach_coefficient(cld_omega.get_value_opt, "cl_p") def compute_forces_and_moments( self, @@ -132,10 +163,10 @@ def compute_forces_and_moments( The normal force uses the true total angle of attack between the flow and the body axis, ``attack_angle = arccos(-v_z / |v|)``, giving ``0.5 * rho * V**2 * A_ref * clalpha(Mach) * attack_angle``. It is - applied perpendicular to the body axis (along the transverse flow) at the - geometric center of pressure, and its moment about the rocket's center of - dry mass is the geometric transport ``cp ^ force``. Fin sets add their - roll moment on top. + applied perpendicular to the body axis (along the transverse flow) at + the geometric center of pressure, and its moment about the rocket's + center of dry mass is the geometric transport ``cp ^ force``. + Fin sets add their roll moment on top. Parameters ---------- @@ -195,6 +226,8 @@ def _roll_moment(self, stream_speed, mach, rho, omega): reduced-rate damping. Returns 0 for surfaces without fins, whose roll coefficients are identically zero. """ + if self.cl.is_zero: + return 0.0 reduced_roll_rate = ( omega[2] * self.reference_length / (2 * stream_speed) if stream_speed > 0 @@ -202,7 +235,7 @@ def _roll_moment(self, stream_speed, mach, rho, omega): ) # The Barrowman roll coefficients depend only on Mach and the roll rate. args = (0.0, 0.0, mach, 0.0, 0.0, 0.0, reduced_roll_rate) - cl = self.clf.get_value_opt(*args) + self.cld.get_value_opt(*args) + cl = self.cl.get_value_opt(*args) return ( 0.5 * rho diff --git a/rocketpy/rocket/aero_surface/_helpers.py b/rocketpy/rocket/aero_surface/_helpers.py new file mode 100644 index 000000000..e4f8ec356 --- /dev/null +++ b/rocketpy/rocket/aero_surface/_helpers.py @@ -0,0 +1,314 @@ +"""Functions that support the aerodynamic surface classes. + +They build the :class:`Function` objects the surfaces store, describe the +direction of the flow from the angle of attack and the sideslip angle (see +:func:`_wind_axes`), and convert coefficients between the wind frame, the body +frame and a surface's own turned frame. +""" + +import inspect +import math + +import numpy as np + +from rocketpy.mathutils import Function + +# Below this size the crossflow is rounding noise, and its direction means nothing +_NO_CROSSFLOW = 1e-12 + + +def _as_function(func, variables, name): + """Wrap a callable that takes ``*args`` as a :class:`Function`. + + ``Function`` counts a callable's parameters to know how many inputs it has, + so ``func`` is given a signature with one parameter per variable. The + signature is set on ``func`` itself. + + Parameters + ---------- + func : callable + Callable taking one value per variable of ``variables``. + variables : sequence of str + Names of the inputs, in the order ``func`` takes them. + name : str + Name of the output. + + Returns + ------- + Function + ``func`` as a Function of ``variables``. + """ + func.__signature__ = inspect.Signature( + inspect.Parameter(var, inspect.Parameter.POSITIONAL_OR_KEYWORD) + for var in variables + ) + return Function(func, list(variables), [name]) + + +def _computed_together(compute, variables, names, reuse=True): + """Build the Functions of several coefficients that are computed together. + + The flight asks for all the coefficients of a surface at the same state one + after the other, so by default ``compute`` runs once per state and its + result is kept for the other coefficients. Array inputs are evaluated one + element at a time. + + Parameters + ---------- + compute : callable + Takes the tuple of input values and returns one value per name. + variables : sequence of str + Names of the inputs, in the order ``compute`` reads them. + names : sequence of str + Names of the coefficients, in the order ``compute`` returns them. + reuse : bool, optional + Whether to keep the last result. Only safe when ``compute`` depends on + its inputs alone. Default True. + + Returns + ------- + tuple of Function + One Function of ``variables`` per name. + """ + last = [None, None] + + def values(args): + try: + if reuse and args == last[0]: + return last[1] + result = compute(args) + except (TypeError, ValueError): + if not any(np.ndim(arg) for arg in args): + raise + return _element_by_element(values, args, len(names)) + last[0], last[1] = args, result + return result + + return tuple( + _as_function(lambda *args, i=i: values(args)[i], variables, name) + for i, name in enumerate(names) + ) + + +def _element_by_element(values, args, count): + """Evaluate ``values`` at each point of array inputs, one array per output.""" + arrays = np.broadcast_arrays(*(np.asarray(arg, dtype=float) for arg in args)) + result = np.empty((count, *arrays[0].shape)) + for point in np.ndindex(arrays[0].shape): + result[(slice(None), *point)] = values( + tuple(float(array[point]) for array in arrays) + ) + return result + + +def _wind_axes(alpha, beta): + """Compute the unit vectors of the wind frame, written in the body frame. + + Drag acts along ``-u``, lift along ``(0, -u_z, u_y) / h`` (perpendicular to + ``u``, in the body y-z plane) and the side force along + ``(h, -u_x * u_y / h, -u_x * u_z / h)``, which completes the right-handed + set. With ``beta = 0`` this is the plain angle-of-attack rotation. + + The two partial angles are not the angles of a rotation sequence, so the + direction is rebuilt from them instead of chaining an ``alpha`` and a + ``beta`` rotation, which would only be exact with one of them at zero. With + the flow exactly sideways both angles are 90 degrees and no longer say how + the crossflow splits between x and y; it then comes out split evenly. + + Parameters + ---------- + alpha : float + Angle of attack, ``arctan2(v_y, v_z)``, in radians, where ``v`` is the + rocket's velocity relative to the air in the body frame. + beta : float + Sideslip angle, ``arctan2(v_x, v_z)``, in radians. + + Returns + ------- + tuple of float + ``(u_x, u_y, u_z, h)``: the direction ``u`` of the rocket's velocity + relative to the air, and ``h = hypot(u_y, u_z)``. + """ + sin_alpha, cos_alpha = math.sin(alpha), math.cos(alpha) + sin_beta, cos_beta = math.sin(beta), math.cos(beta) + # Parallel to v scaled by v_z; the sign of cos(alpha) restores tail-first flow + u_x, u_y, u_z = sin_beta * cos_alpha, sin_alpha * cos_beta, cos_alpha * cos_beta + # Never zero: the cosine of a float is never exactly zero + norm = math.copysign(math.sqrt(u_x**2 + u_y**2 + u_z**2), cos_alpha) + u_x, u_y, u_z = u_x / norm, u_y / norm, u_z / norm + return u_x, u_y, u_z, math.hypot(u_y, u_z) + + +def total_angle_and_roll(alpha, beta): + """Compute the total angle of attack and the roll angle of the wind. + + Parameters + ---------- + alpha : float or array + Angle of attack, in radians. + beta : float or array + Sideslip angle, in radians. + + Returns + ------- + alpha_total : float or array + Angle between the rocket's axis and its velocity relative to the air, in + radians: 0 flying straight into the air, ``pi / 2`` sideways and ``pi`` + tail first. + phi : float or array + Direction around the body the crossflow comes from, in radians: + ``pi / 2`` for a pure angle of attack (``alpha > 0``) and 0 for a pure + sideslip (``beta > 0``). It is 0 when there is no crossflow. + """ + try: + u_x, u_y, u_z, _ = _wind_axes(alpha, beta) + except TypeError: + if not (np.ndim(alpha) or np.ndim(beta)): + raise + return np.vectorize(total_angle_and_roll)(alpha, beta) + crossflow = math.hypot(u_x, u_y) + roll = math.atan2(u_y, u_x) if crossflow > _NO_CROSSFLOW else 0.0 + return math.atan2(crossflow, u_z), roll + + +def _wind_to_body_coefficients(c_lift, c_drag, c_side, variables): + """Rotate wind-frame force coefficients into the body frame. + + See :func:`_wind_axes` for the directions of lift, drag and side force. + + Parameters + ---------- + c_lift, c_drag, c_side : AeroCoefficient + Lift, drag and side-force coefficients. + variables : sequence of str + Inputs of the returned Functions: the angle of attack and the sideslip + angle first, then any other variable the three coefficients depend on. + + Returns + ------- + tuple of Function + Body-frame normal, side and axial force coefficients ``(cN, cY, cA)``, + as Functions of ``variables``. + """ + lift, drag, side = (c.evaluator(variables) for c in (c_lift, c_drag, c_side)) + + def compute(args): + u_x, u_y, u_z, h = _wind_axes(args[0], args[1]) + c_l, c_d, c_s = lift(*args), drag(*args), side(*args) + return ( + (c_s * u_x * u_y + c_l * u_z) / h + c_d * u_y, + c_s * h - c_d * u_x, + (c_s * u_x * u_z - c_l * u_y) / h + c_d * u_z, + ) + + return _computed_together(compute, variables, ("cN", "cY", "cA")) + + +def _body_to_wind_coefficients(c_normal, c_side, c_axial, variables): + """Rotate body-frame force coefficients into the wind frame. + + Inverse of :func:`_wind_to_body_coefficients`. + + Parameters + ---------- + c_normal, c_side, c_axial : AeroCoefficient + Body-frame normal, side and axial force coefficients. + variables : sequence of str + Every variable of the surface, in the order its coefficients are called + with (the angle of attack and the sideslip angle first). + + Returns + ------- + tuple of Function + Wind-frame lift, drag and side-force coefficients ``(cL, cD, cQ)``, as + Functions of ``variables``. + """ + normal, side, axial = (c.get_value_opt for c in (c_normal, c_side, c_axial)) + + def compute(args): + u_x, u_y, u_z, h = _wind_axes(args[0], args[1]) + c_n, c_y, c_a = normal(*args), side(*args), axial(*args) + return ( + (c_n * u_z - c_a * u_y) / h, + -c_y * u_x + c_n * u_y + c_a * u_z, + c_y * h + (c_n * u_y + c_a * u_z) * u_x / h, + ) + + # Not kept: a fin's coefficients also read its cant angle, which a + # controller may change between two calls with the same inputs + return _computed_together(compute, variables, ("cL", "cD", "cQ"), reuse=False) + + +def _wind_plane_lift_to_body_coefficients(lift, drag, variables): + """Convert a lift given against the total angle of attack, and the drag + that goes with it, into the body-frame ``cN``, ``cY`` and ``cA``. + + Such a lift acts in the plane that holds the rocket's axis and the wind, + perpendicular to the wind. With the total angle ``a`` it gives the normal + force in that plane and the axial force:: + + cN_plane = cL * cos(a) + cD * sin(a) + cA = cD * cos(a) - cL * sin(a) + + and the normal force is split between the pitch and yaw planes as in + :func:`_total_angle_to_body_coefficients`. + + Parameters + ---------- + lift, drag : AeroCoefficient + Lift and drag coefficients. + variables : sequence of str + Inputs of the returned Functions: the angle of attack and the sideslip + angle first, then any other variable the two depend on. + + Returns + ------- + tuple of Function + ``cN``, ``cY`` and ``cA``, as Functions of ``variables``. + """ + read_lift, read_drag = lift.evaluator(variables), drag.evaluator(variables) + + def compute(args): + total, phi = total_angle_and_roll(args[0], args[1]) + c_l, c_d = read_lift(*args), read_drag(*args) + sin, cos = math.sin(total), math.cos(total) + normal = c_l * cos + c_d * sin + return normal * math.sin(phi), -normal * math.cos(phi), c_d * cos - c_l * sin + + return _computed_together(compute, variables, ("cN", "cY", "cA")) + + +def _total_angle_to_body_coefficients(coefficient, variables, names): + """Split a total-angle coefficient between the pitch and yaw planes. + + The coefficient acts in the plane that holds the rocket's axis and the wind, + and is split along the crossflow, whose direction is the roll angle of the + wind ``phi`` (see :func:`total_angle_and_roll`):: + + cN = coefficient * sin(phi) (or cm, for a moment) + cY = -coefficient * cos(phi) (or cn) + + Parameters + ---------- + coefficient : AeroCoefficient + Normal force or pitch moment coefficient, against the total angle of + attack. + variables : sequence of str + Inputs of the returned Functions: the angle of attack and the sideslip + angle first, then any other variable the coefficient depends on. + names : tuple of str + Names of the two parts, such as ``("cN", "cY")``. + + Returns + ------- + tuple of Function + The pitch-plane and yaw-plane parts, as Functions of ``variables``. + """ + evaluate = coefficient.evaluator(variables) + + def compute(args): + _, phi = total_angle_and_roll(args[0], args[1]) + value = evaluate(*args) + return value * math.sin(phi), -value * math.cos(phi) + + return _computed_together(compute, variables, names) diff --git a/rocketpy/rocket/aero_surface/aero_coefficient.py b/rocketpy/rocket/aero_surface/aero_coefficient.py index 09ebe5445..6ff3af412 100644 --- a/rocketpy/rocket/aero_surface/aero_coefficient.py +++ b/rocketpy/rocket/aero_surface/aero_coefficient.py @@ -1,8 +1,18 @@ import copy import csv import inspect +import math +import re +import warnings + +import numpy as np from rocketpy.mathutils import Function +from rocketpy.mathutils._regular_grid import _RegularGrid +from rocketpy.rocket.aero_surface._helpers import ( + _as_function, + total_angle_and_roll, +) # Single source of truth for the seven base coefficient independent variables. BASE_INDEPENDENT_VARS = [ @@ -16,6 +26,54 @@ ] +# The total angle of attack (between the air and the rocket's axis, from 0 to pi) +# and the roll angle of the wind (the direction around the body the crossflow +# comes from). A source may name them as inputs. +DERIVED_ANGLES = ("alpha_total", "phi") + +# Names under which an angle can be given in degrees. The simulation always +# works in radians; a coefficient whose source uses one of these names converts +# the angle before reading the source. +ANGLES_IN_DEGREES = { + "alpha_deg": "alpha", + "beta_deg": "beta", + "alpha_total_deg": "alpha_total", + "phi_deg": "phi", +} + +# Names a source may use for its inputs on top of the variables themselves +SOURCE_ONLY_NAMES = (*DERIVED_ANGLES, *ANGLES_IN_DEGREES) + + +# Error for a one-input source whose variable cannot be told; filled with the +# name of the coefficient +_UNNAMED_INPUT_MESSAGE = ( + "Cannot tell which variable the single input of {name} is. Name it " + "by giving the coefficient as a pair, for example " + '(table, ["mach"]), by labelling the input of a Function after the ' + 'variable (for example "mach" or "Mach Number"), by adding a header ' + "to the CSV file, or by naming the argument of a function after it " + "(for example lambda mach: ...)." +) + + +def _required_arguments(func): + """Names of the arguments ``func`` must be given by position: the ones with + no default value. Optional arguments (``k=2.0``, keyword-only ones, + ``*args``, ``**kwargs``) are for the function's own use and are left out.""" + try: + parameters = inspect.signature(func).parameters.values() + except (TypeError, ValueError): # pragma: no cover - builtins + return [] + return [ + parameter.name + for parameter in parameters + if parameter.default is parameter.empty + and parameter.kind + in (parameter.POSITIONAL_ONLY, parameter.POSITIONAL_OR_KEYWORD) + ] + + def build_independent_vars(control_variables=()): """Build the ordered independent-variable list of a coefficient/surface. @@ -31,6 +89,22 @@ class AeroCoefficient: """A single aerodynamic coefficient (such as lift or drag), stored using only the variables it actually depends on.""" + # The names a Function accepts for its tables + _INTERPOLATIONS = ( + "linear", + "polynomial", + "akima", + "spline", + "shepard", + "rbf", + "nearest", + "slinear", + "cubic", + "quintic", + "pchip", + ) + _EXTRAPOLATIONS = ("constant", "natural", "zero") + def __init__( self, source, @@ -44,62 +118,80 @@ def __init__( """Build a coefficient from a value, a data table, or a function. A plain number is stored as a constant. Anything else is stored as a - :class:`Function` of only the variables it depends on, so a coefficient - that varies with Mach alone stays a simple 1-D curve instead of being - spread across all seven variables. When the coefficient is evaluated, - the variables it does not use are simply ignored. + :class:`Function` of only the variables it depends on. Most of the time you only pass ``source`` and leave ``depends_on`` as ``None``, so the variables are worked out automatically. This is the same input a :class:`GenericSurface` accepts. Pass ``depends_on`` yourself only when the source and the order of its inputs are already - known (used internally by the Barrowman surfaces and when loading a - saved rocket). + known. Parameters ---------- - source : int, float, str, list, tuple, callable, Function, or AeroCoefficient - The coefficient value, given in one of these forms: - - - **number**: a constant coefficient that never changes. - - **function or lambda**: a coefficient computed from its inputs. - Name the arguments after the variables they use (e.g. - ``lambda alpha, mach: ...``), or give one argument per variable, - or a single argument together with ``single_var``. - - **Function**: a :class:`Function` you already built, used as is. - If ``extrapolation`` is given it is applied to a copy, so the - Function you passed in is left unchanged. - - **list or tuple of data points**: a table of values, read the - same way as the same data in a CSV file. The variables it depends - on are worked out from the table, using ``single_var`` for a - one-input table. - - **str**: the path to a data file. A ``.csv`` file has one column - per variable (named in the header) and the coefficient value in - the last column; a headerless two-column file is a table of - ``single_var`` versus the value. - - **AeroCoefficient**: an existing coefficient, reused as is. This - lets one coefficient be shared by several surfaces and lets a - rocket be saved and loaded. + source : int, float, str, list, tuple, array, callable, Function, or AeroCoefficient + The coefficient value. It can depend on these variables, named the + same way in every form below: + + - ``"alpha"``, ``"beta"``: angle of attack and sideslip angle, in + radians, or ``"alpha_deg"``, ``"beta_deg"`` in degrees. + - ``"alpha_total"``, ``"phi"``: total angle of attack (between the + rocket's axis and the air) and roll angle of the wind (the + direction the crossflow comes from), in radians, or + ``"alpha_total_deg"``, ``"phi_deg"`` in degrees. + - ``"mach"``: Mach number. + - ``"reynolds"``: Reynolds number. + - ``"pitch_rate"``, ``"yaw_rate"``, ``"roll_rate"``: angular rates in + reduced form, such as ``q * L / (2 * V)`` for the pitch rate. + - the names in ``control_variables``. + + It can be given as: + + - **number**: a constant. + - **function or lambda**: arguments named after the variables, e.g. + ``lambda alpha, mach: ...``, or seven arguments in the order + ``alpha, beta, mach, reynolds, pitch_rate, yaw_rate, roll_rate``. + Arguments with a default value are not counted, so + ``def cN(alpha, mach, k=2.0)`` depends on ``alpha`` and ``mach``. + ``functools.partial`` also works. + - **list, tuple or numpy array**: a table with one column per + variable and the value in the last column. Name its columns with + the ``(source, variables)`` pair below, unless it has one input + (see ``single_var``) or all seven, in the order above. + - **str**: path to a ``.csv`` file with one column per variable, + named in the header, and the value in the last column. A + headerless two-column file is ``single_var`` against the value. + - **Function**: used as is; its input labels name the variables. If + ``extrapolation`` is given, it is applied to a copy. + - **AeroCoefficient**: reused as is. + - **(axes, values)**: values on a regular grid. ``axes`` maps each + variable to its values, and ``values`` has one dimension per + variable, in the same order: ``({"alpha": alphas, "mach": machs}, + values)``, with ``values[i, j]`` at ``alphas[i]`` and ``machs[j]``. + - **(source, variables)**: any of the above with the names of its + inputs, in order, e.g. ``(points, ["alpha", "mach"])``. Use it + when the source does not name its variables. + + A source with one input must name its variable, in one of the ways + above or with ``single_var``: a table is never assumed to be against + the angle of attack. depends_on : sequence of str, optional - The variables this coefficient actually uses, chosen from the - surface's variables: the seven base ones ``"alpha"``, ``"beta"``, + The names of the source's inputs, in the order the source takes + them (a function's arguments, a CSV's columns). The names are + ``"alpha"``, ``"beta"``, ``"alpha_deg"``, ``"beta_deg"``, + ``"alpha_total"``, ``"phi"``, ``"alpha_total_deg"``, ``"phi_deg"``, ``"mach"``, ``"reynolds"``, ``"pitch_rate"``, ``"yaw_rate"``, - ``"roll_rate"``, plus any names in - ``control_variables``. List them in the same order as the source's - own inputs (a function's arguments, a CSV's columns). For example, - ``()`` for a constant, ``("mach",)`` for a Mach-only curve, or the - whole list for something that uses every variable. A name that is - not one of the surface's variables raises a ``ValueError``. Leave it - as ``None`` (the default) to have it worked out from ``source``. + ``"roll_rate"`` and the names in ``control_variables``. For example + ``()`` for a constant or ``("alpha_total_deg", "mach")``. Any other + name raises a ``ValueError``. Leave it as ``None`` (the default) to + have it worked out from ``source``. control_variables : sequence of str, optional Names of extra variables, such as control-surface deflections set by a controller. They are added after the seven base variables, in the order given. Empty for ordinary surfaces. Default ``()``. name : str, optional A readable name for the coefficient (e.g. ``"cL_alpha"`` or - ``"Drag Coefficient with Power Off"``). It appears in error messages, - so a clear name makes problems easier to spot. Default - ``"coefficient"``. + ``"Drag Coefficient with Power Off"``). It appears in error messages + and plots. Default ``"coefficient"``. extrapolation : str, optional What the coefficient does outside the range of its data table: ``"constant"`` holds the value at the nearest edge (the safe default @@ -120,31 +212,60 @@ def __init__( Which variable a one-input table or function maps to. Used only when working out the variables of a single-input source: a headerless two-column CSV, a one-input :class:`Function`, or a one-argument - function. ``None`` (the default) guesses it from the input's label - and otherwise falls back to the first variable. Ignored when - ``depends_on`` is given. Default ``None``. + function. A source that names its own variable (an argument or an + input label such as ``alpha`` or ``"Alpha (deg)"``) keeps that + name; ``single_var`` only fills in for a source that does not. + ``None`` (the default) reads it from the input's label + and raises a ``ValueError`` when the label names no variable. + Ignored when ``depends_on`` is given. Default ``None``. """ self.name = name + for what, option, allowed in ( + ("interpolation", interpolation, self._INTERPOLATIONS), + ("extrapolation", extrapolation, self._EXTRAPOLATIONS), + ): + if option is not None and option not in allowed: + raise ValueError( + f"Unknown {what} {option!r} for {name}; the options are " + f"{', '.join(allowed)}." + ) self.extrapolation = extrapolation self.interpolation = interpolation self.control_variables = tuple(control_variables) - # ``control_variables`` completes the full ordered variable list: every - # coefficient's argument order and each variable's position. This is a - # surface-wide property, distinct from ``depends_on`` (the subset a - # single coefficient reads), and it is passed in rather than derived - # from ``depends_on``: inferring ``depends_on`` already needs this list. + # Every variable, in the order the coefficient is called with self.independent_vars = tuple(build_independent_vars(control_variables)) - # Infer the stored source and its dependencies from the raw input when - # ``depends_on`` is not given. if depends_on is None: - source, depends_on = self._resolve_input(source, single_var) + source, depends_on = self._resolve_pair(source) or self._resolve_input( + source, single_var + ) extrapolation = self.extrapolation interpolation = self.interpolation - # ``depends_on`` is kept in the given order because it matches the - # positional argument order of the stored source (callable parameters, - # CSV columns, …). ``_indices`` then maps the full argument tuple - # to the source's own argument order. - self.depends_on = tuple(depends_on) + # The source's inputs as given (possibly in degrees or total angles), + # kept in its own order to save and rebuild the coefficient + self._source_variables = tuple(depends_on) + bases = [ANGLES_IN_DEGREES.get(var, var) for var in self._source_variables] + # The source's inputs with the degree names read as the angle they + # stand for: ``alpha_deg`` as ``alpha``, ``alpha_total_deg`` as + # ``alpha_total`` + self.source_angles = tuple(bases) + self.in_degrees = tuple( + ANGLES_IN_DEGREES[var] + for var in self._source_variables + if var in ANGLES_IN_DEGREES + ) + # The derived angles come from alpha and beta; each variable is kept once + self.depends_on = tuple( + dict.fromkeys( + var + for base in bases + for var in (("alpha", "beta") if base in DERIVED_ANGLES else (base,)) + ) + ) + if len(set(bases)) != len(bases): + raise ValueError( + f"{name} names the same variable more than once: " + f"{list(self._source_variables)}." + ) unknown = [var for var in self.depends_on if var not in self.independent_vars] if unknown: raise ValueError( @@ -158,27 +279,29 @@ def __init__( self.is_zero = False self._constant = None if isinstance(source, Function): - # Only override interpolation/extrapolation when explicitly asked, - # and always on a copy (the Function may be shared elsewhere). + # Changed only when asked, and on a copy: the Function may be shared if interpolation is not None or extrapolation is not None: source = copy.deepcopy(source) - # Interpolation names like "akima"/"spline" are 1-D concepts; a - # multi-dimensional Function (e.g. a regular grid) keeps its own - # interpolation, whose method is fixed when the grid is built, so - # a 1-D name here would wrongly fall back to "shepard". - if interpolation is not None and source.__dom_dim__ == 1: + # Scattered points would read "akima" or "spline" as "shepard" + if interpolation is not None and ( + source.__dom_dim__ == 1 or source.is_regular_grid + ): source.set_interpolation(interpolation) if extrapolation is not None: source.set_extrapolation(extrapolation) self.function = source elif callable(source): - self.function = Function( - source, - list(self.depends_on) or ["x"], - [name], - interpolation=interpolation or "linear", - extrapolation=extrapolation or "constant", - ) + if len(inspect.signature(source).parameters) != len(self._source_variables): + # Hide the optional arguments, which Function would count as inputs + self.function = _as_function( + lambda *args, function=source: function(*args), + self._source_variables, + name, + ) + else: + self.function = Function( + source, list(self._source_variables) or ["x"], [name] + ) else: # Scalar constant. self._constant = float(source) @@ -186,87 +309,233 @@ def __init__( self.function = Function(self._constant) self._evaluate = self.function.get_value_opt + if self._source_variables != self.depends_on: + self._evaluate = self._converting_evaluator() + self._check_angle_axes() + if callable(source) and not isinstance(source, Function): + # Call it once, so a function that cannot be called or does not + # return a number fails here rather than during the flight + try: + float(self._evaluate(*[0.0] * len(self.depends_on))) + except Exception as exc: + raise ValueError( + f"The function given for {self.name} could not be evaluated " + f"with every variable at zero: {exc!r}. It must accept the " + f"variables {list(self.depends_on)} as positional arguments " + "and return a number." + ) from exc - def _resolve_input(self, source, single_var): - """Infer ``(stored source, depends_on)`` from a raw coefficient input. + def _converting_evaluator(self): + """Evaluator for a source whose inputs are not the variables as they + are: angles in degrees, the total angle of attack or the roll angle of + the wind. It takes the values of ``depends_on`` and builds the source's + own inputs from them.""" + position = {var: i for i, var in enumerate(self.depends_on)} + source_evaluate = self.function.get_value_opt + + def reader(variable): + base = ANGLES_IN_DEGREES.get(variable, variable) + scale = 180 / math.pi if variable in ANGLES_IN_DEGREES else 1.0 + if base in DERIVED_ANGLES: + i_alpha, i_beta = position["alpha"], position["beta"] + which = DERIVED_ANGLES.index(base) + return lambda args: ( + total_angle_and_roll(args[i_alpha], args[i_beta])[which] * scale + ) + index = position[base] + return lambda args: args[index] * scale + + readers = [reader(variable) for variable in self._source_variables] + return lambda *args: source_evaluate(*[read(args) for read in readers]) + + def _check_angle_axes(self): + """Warn about two mistakes a table's angle of attack or sideslip axis + can show. + + - Values no angle in radians can take: both angles stay within ``-pi`` + to ``pi``, so a larger value almost surely means the table is in + degrees. + - A table that starts at zero angle, where the coefficient is zero, and + has no negative angles. Such a coefficient changes sign with the angle + (a normal force or a pitch moment), but outside its range a table + holds its edge value, so it would stay at zero for every negative + angle. Data tabulated against the total angle of attack looks like + this. + """ + domain = getattr(self.function, "_domain", None) + image = getattr(self.function, "_image", None) + if self._constant is not None or domain is None or image is None: + return + if len(np.unique(domain, axis=0)) != len(domain): + raise ValueError( + f"The table of {self.name} lists the same input values more " + "than once. Each point must appear a single time." + ) + for column, variable in enumerate(self._source_variables): + angle = ANGLES_IN_DEGREES.get(variable, variable) + if angle not in ("alpha", "beta", *DERIVED_ANGLES): + continue + if column >= domain.shape[1]: + continue + axis = domain[:, column] + if variable == angle and float(np.max(np.abs(axis))) > 3.2: + warnings.warn( + f"The {variable} values of {self.name} go up to " + f"{float(np.max(np.abs(axis))):g}, which looks like degrees, " + "but they are read as radians. If the table is in degrees, " + f'name the variable "{variable}_deg" (in the CSV header, the ' + "Function input or the list of variables given with the " + "table).", + UserWarning, + stacklevel=4, + ) + at_zero = np.abs(np.ravel(image)[axis == 0]) + if ( + angle in ("alpha", "beta") + and np.min(axis) == 0 + and at_zero.size + and np.all(at_zero < 1e-12) + and np.any(np.abs(image) > 1e-12) + ): + warnings.warn( + f"The table of {self.name} starts at {angle} = 0, where it is " + "zero, and has no negative angles. Outside its range a table " + f"holds its edge value, so {self.name} would be zero for every " + f"negative {angle}. RocketPy's {angle} is measured in one " + "plane and takes both signs: mirror the table to negative " + "angles (with the sign of the coefficient flipped). If the " + "data is against the total angle of attack, name the " + f'variable "alpha_total" instead of "{angle}".', + UserWarning, + stacklevel=4, + ) - Parameters - ---------- - source : int, float, str, list, tuple, callable, Function or AeroCoefficient - Raw coefficient input: a scalar, a CSV file path (or any other path - read by :class:`Function`), a list/tuple of data points, a callable, - a pre-built :class:`Function`, or an existing ``AeroCoefficient``. - single_var : str or None - Name of the independent variable a one-dimensional input depends on. - When ``None``, it is inferred from the source (see - :meth:`_infer_single_var` / :meth:`_infer_callable_depends_on`). + def _as_table(self, source): + """Turn a file path, a list of data points or an array into a + :class:`Function`; anything else is returned unchanged. Points that + cover a regular grid over two or more variables are interpolated on it + by the ``Function``, which is more accurate than treating them as + scattered.""" + if not isinstance(source, (str, list, tuple, np.ndarray)): + return source + try: + return Function( + source if isinstance(source, str) else np.asarray(source).tolist(), + interpolation=self.interpolation or "linear", + extrapolation=self.extrapolation or "constant", + ) + except (TypeError, ValueError) as exc: + raise TypeError( + f"Invalid input for {self.name}: could not be read as a table " + "of data points." + ) from exc - Returns - ------- - tuple - ``(stored_source, depends_on)`` where ``stored_source`` is the scalar - or :class:`Function` kept internally and ``depends_on`` is the tuple - of independent-variable names it depends on. - """ + def _resolve_pair(self, source): + """Handle a source given as a pair. ``(table, ["alpha", "mach"])`` names + the variables of a table or function, and + ``({"alpha": alphas, "mach": machs}, values)`` gives values on a regular + grid, with ``values[i, j]`` the value at ``alphas[i]`` and ``machs[j]``. + Returns ``(stored source, depends_on)``, or ``None`` when ``source`` is + not such a pair.""" + if not (isinstance(source, tuple) and len(source) == 2): + return None + source, variables = source + if isinstance(source, dict) and all(isinstance(key, str) for key in source): + shape = tuple(len(axis) for axis in source.values()) + if np.shape(variables) != shape: + raise ValueError( + f"The values of {self.name} have shape {np.shape(variables)} " + f"but its axes {list(source)} have {shape} points: values " + "must have one dimension per axis, in the order of the axes " + "(a square grid given the other way round cannot be told " + "apart, so check the order)." + ) + source, variables = ( + _RegularGrid.points_from_axes(list(source.values()), variables), + list(source), + ) + elif not ( + isinstance(variables, (list, tuple)) + and all(isinstance(variable, str) for variable in variables) + ): + return None + if len(set(variables)) != len(variables): + raise ValueError( + f"{self.name} names the same variable more than once: " + f"{list(variables)}." + ) + source = self._as_table(source) + if isinstance(source, Function): + number_of_inputs = source.__dom_dim__ + elif callable(source): + number_of_inputs = len(_required_arguments(source)) + elif variables: + raise ValueError( + f"{self.name} is a constant ({source}) but the variable names " + f"{list(variables)} were given: a constant depends on nothing. " + "Give the number alone, or a table or function of those variables." + ) + else: + number_of_inputs = 0 + if number_of_inputs != len(variables): + raise ValueError( + f"{self.name} has {number_of_inputs} input(s) but " + f"{len(variables)} variable name(s) were given: {list(variables)}." + ) + return source, tuple(variables) + + def _resolve_input(self, source, single_var): + """Work out ``(stored source, depends_on)`` from a raw input: a number, + a file path, a list of points, a function, a :class:`Function` or another + ``AeroCoefficient``. ``single_var`` names the variable of a one-input + source; when ``None`` it is read from the source's own labels.""" name = self.name - independent_vars = self.independent_vars - n_vars = len(independent_vars) + n_vars = len(self.independent_vars) + # Every name a source may give its inputs + independent_vars = [*self.independent_vars, *SOURCE_ONLY_NAMES] if isinstance(source, AeroCoefficient): - # An already-built coefficient passed straight through, re-keyed to - # this surface's variable order. Adopt its extrapolation when none - # was requested. + # Reused as is, keeping its extrapolation unless another was asked if self.extrapolation is None: self.extrapolation = source.extrapolation value = ( source._constant if source._constant is not None else source.function ) - return value, source.depends_on + return value, source._source_variables - if isinstance(source, str): - if source.lower().endswith(".csv"): - return self._load_csv( - source, - name, - independent_vars, - extrapolation=self.extrapolation or "constant", - interpolation=self.interpolation or "linear", - single_var=single_var, - ) - # Any other path is read by Function - source = Function( + if isinstance(source, str) and source.lower().endswith(".csv"): + return self._load_csv( source, - interpolation=self.interpolation or "linear", + name, + independent_vars, extrapolation=self.extrapolation or "constant", + interpolation=self.interpolation or "linear", + single_var=single_var, ) - - # A list/tuple of data points is parsed by Function and handled below - if isinstance(source, (list, tuple)): - try: - source = Function( - list(source), - interpolation=self.interpolation or "linear", - extrapolation=self.extrapolation or "constant", - ) - except (TypeError, ValueError) as exc: - raise TypeError( - f"Invalid list/tuple input for {name}: could not be parsed " - "into a Function of the independent variables." - ) from exc + # Any other path, or a list of data points, is read as a table + source = self._as_table(source) if isinstance(source, Function): dom_dim = source.__dom_dim__ + # Inputs named exactly after the variables say what it depends on + inputs = [str(label) for label in source.__inputs__] + if set(inputs) <= set(independent_vars) and len(set(inputs)) == dom_dim: + return source, inputs if dom_dim == n_vars: - return source, list(independent_vars) + return source, list(self.independent_vars) if dom_dim == 1: - # A 1-D Function depends on ``single_var`` when given, else on - # the first independent variable unless its input name matches. - return source, [ - single_var or self._infer_single_var(source, independent_vars) - ] + # No default: guessing could read a Mach curve against alpha + variable = ( + self._infer_single_var(source, independent_vars) or single_var + ) + if variable is None: + raise ValueError(_UNNAMED_INPUT_MESSAGE.format(name=name)) + return source, [variable] raise ValueError( f"{name} Function must have {n_vars} input arguments " - f"({', '.join(independent_vars)}) or be one-dimensional." + f"({', '.join(self.independent_vars)}) or be one-dimensional. To use " + f"a table with {dom_dim} inputs, name them by giving the " + 'coefficient as a pair, for example (table, ["alpha", "mach"]).' ) if callable(source): @@ -292,132 +561,87 @@ def _load_csv( extrapolation="constant", interpolation="linear", single_var=None, - ): # pylint: disable=too-many-statements - """Load a coefficient CSV at minimal dimension. - - Expects header-based CSV data whose columns (except the last) are - independent variables among ``independent_vars``; the last column is the - coefficient value. The coefficient is stored over only the columns that - are present, in their header order. A headerless two-column file is - treated as a one-dimensional table over ``single_var``. - - Parameters - ---------- - file_path : str - Path to the CSV file. - name : str - Coefficient name, used for error messages and the Function output. - independent_vars : sequence of str - The owning surface's ordered independent variables, used to validate - the CSV header columns. - extrapolation : str, optional - Extrapolation method for the loaded ``Function``. Defaults to - ``"constant"`` (holds the edge value past the tabulated range). - interpolation : str, optional - Interpolation method for the loaded ``Function``. Defaults to - ``"linear"``. For 1-D and non-grid tables it is used directly; a - strict Cartesian grid uses ``"regular_grid"`` with the method mapped - from this value (see :meth:`Function.from_regular_grid_csv`). - single_var : str, optional - Independent variable a headerless two-column table depends on. - Defaults to the first independent variable. - - Returns - ------- - tuple - ``(function, depends_on)`` where ``function`` is a low-dimensional - ``Function`` over the present columns and ``depends_on`` lists those - columns. Consumed by :meth:`_resolve_input`. - """ - independent_vars = list(independent_vars) - + ): + """Read a coefficient from a CSV file. The last column is the value of + the coefficient; the header names the variable of each of the other + columns, among ``independent_vars``. A two-column file with no header + is a table over ``single_var``, which must then be given. Returns + ``(function, depends_on)``, with ``depends_on`` the columns in the order + of the file.""" try: with open(file_path, mode="r") as file: - reader = csv.reader(file) - header = next(reader) - except (FileNotFoundError, IOError) as e: + header = [ + column.strip().strip("\"'") + for column in next(csv.reader(file, skipinitialspace=True)) + ] + except OSError as e: raise ValueError(f"Error reading {name} CSV file: {e}") from e except StopIteration as e: raise ValueError(f"Invalid or empty CSV file for {name}.") from e - if not header: raise ValueError(f"Invalid or empty CSV file for {name}.") - header = [column.strip() for column in header] - - # Headerless two-column (x, coefficient) table: a 1-D table over - # ``single_var`` (e.g. a Mach-only drag curve given as ``mach, cd``). - def _is_numeric(value): + def is_number(text): try: - float(value) + float(text) return True - except (TypeError, ValueError): + except ValueError: return False - if len(header) == 2 and all(_is_numeric(cell) for cell in header): - csv_func = Function( - file_path, - interpolation=interpolation, - extrapolation=extrapolation, - ) - return csv_func, [single_var or independent_vars[0]] - - present_columns = [col for col in independent_vars if col in header] - - invalid_columns = [col for col in header[:-1] if col not in independent_vars] - if invalid_columns: - raise ValueError( - f"Invalid independent variable(s) in {name} CSV: " - f"{invalid_columns}. Valid options are: {independent_vars}." - ) - - if header[-1] in independent_vars: - raise ValueError( - f"Last column in {name} CSV must be the coefficient" - " value, not an independent variable." - ) - - if not present_columns: - raise ValueError(f"No independent variables found in {name} CSV.") - - ordered_present_columns = [ - col for col in header[:-1] if col in independent_vars - ] + if len(header) == 2 and all(is_number(cell) for cell in header): + # No header: a table of one variable, which has to be told + if single_var is None: + raise ValueError(_UNNAMED_INPUT_MESSAGE.format(name=name)) + variables = [single_var] + else: + variables = header[:-1] + invalid_columns = [col for col in variables if col not in independent_vars] + if invalid_columns: + raise ValueError( + f"Invalid independent variable(s) in {name} CSV: " + f"{invalid_columns}. Valid options are: {list(independent_vars)}." + ) + if header[-1] in independent_vars: + raise ValueError( + f"Last column in {name} CSV must be the coefficient" + " value, not an independent variable." + ) + if not variables: + raise ValueError(f"No independent variables found in {name} CSV.") + if len(set(variables)) != len(variables): + raise ValueError( + f"{name} names the same variable more than once: {variables}." + ) - csv_func = Function.from_regular_grid_csv( - file_path, - ordered_present_columns, - name, - extrapolation=extrapolation, - interpolation=interpolation, + # A table covering a regular grid is interpolated on it by Function + function = Function( + file_path, interpolation=interpolation, extrapolation=extrapolation ) - if csv_func is None: - csv_func = Function( - file_path, - interpolation=interpolation, - extrapolation=extrapolation, - ) - - return csv_func, ordered_present_columns + return function, variables @staticmethod def _infer_single_var(function, independent_vars): - """Best-effort name of the variable a 1-D Function depends on.""" + """The variable a one-input Function is labelled after, or ``None``. + + The label matches a variable when it contains the variable's name as a + whole word, ignoring case and reading spaces as underscores: ``"mach"``, + ``"Mach Number"`` and ``"Pitch Rate"`` match, ``"Alphabet"`` does not. + An angle whose label also says ``deg`` or ``°`` (``"Alpha (deg)"``) is + read in degrees. + """ try: - label = function.__inputs__[0] + label = str(function.__inputs__[0]).lower() except (AttributeError, IndexError, TypeError): - return independent_vars[0] - label_lower = str(label).lower() - # Exact match first; then substring, longest variable name first, so a - # label containing a longer variable name binds to it rather than to a - # shorter variable that happens to be a substring of it. - for var in independent_vars: - if var == label_lower: - return var + return None + # Longest name first, so "alpha_total" is not read as "alpha" for var in sorted(independent_vars, key=len, reverse=True): - if var in label_lower: + pattern = rf"(? Cd 0 rule applies only when the air brakes + # add to (rather than override) the rocket drag. + def drag_coefficient_function(mach, deployment_level): if deployment_level == 0 and not self.override_rocket_drag: return 0.0 return self.drag_coefficient.get_value_opt(deployment_level, mach) @@ -135,7 +131,7 @@ def drag_coefficient_function( super().__init__( reference_area=reference_area, reference_length=2 * (reference_area / np.pi) ** 0.5, - coefficients={"cD": drag_coefficient_function}, + coefficients={"cA": drag_coefficient_function}, center_of_pressure=(0, 0, 0), name=name, controls=("deployment_level",), diff --git a/rocketpy/rocket/aero_surface/controllable_generic_surface.py b/rocketpy/rocket/aero_surface/controllable_generic_surface.py index f312d95c6..f76588435 100644 --- a/rocketpy/rocket/aero_surface/controllable_generic_surface.py +++ b/rocketpy/rocket/aero_surface/controllable_generic_surface.py @@ -1,18 +1,22 @@ from rocketpy.rocket.aero_surface.generic_surface import GenericSurface -from rocketpy.tools import from_hex_decode, to_hex_encode class ControllableGenericSurface(GenericSurface): - """A generic aerodynamic surface whose coefficients also depend on one or + """An aerodynamic surface whose coefficients also depend on one or more control inputs (canards, grid fins, elevons, air-brake deployment, and so on) set by a controller while the rocket flies. On top of the seven standard variables of :class:`GenericSurface` (``alpha``, ``beta``, ``mach``, ``reynolds``, ``pitch_rate``, ``yaw_rate``, ``roll_rate``), each coefficient takes one extra input per control, in the - order listed in ``controls``. A controller updates the current control - values every simulation step (see ``Rocket.add_controllable_surface``), and - they are passed to the coefficients automatically. + order listed in ``controls``. The current control values are set with + :meth:`set_control` and passed to the coefficients automatically. During a + flight a controller does that: build a ``rocketpy.control._Controller`` + with this surface as its ``controlled_objects`` and a function that calls + ``set_control`` from the flight state, and bind it with + ``rocket._add_controllers(controller)`` (the path + :meth:`rocketpy.Rocket.add_air_brakes` uses); the flight then runs the + controller at its sampling rate. Attributes ---------- @@ -20,6 +24,81 @@ class ControllableGenericSurface(GenericSurface): Names of the controls, in the order the coefficients expect them. ControllableGenericSurface.control_state : dict Current value of each control (starts at 0). + ControllableGenericSurface.cN : AeroCoefficient + Normal force coefficient, the force in the pitch plane. + ControllableGenericSurface.cY : AeroCoefficient + Side force coefficient, the force in the yaw plane. + ControllableGenericSurface.cA : AeroCoefficient + Axial force coefficient, the force along the rocket's axis. + ControllableGenericSurface.cm : AeroCoefficient + Pitching moment coefficient, about ``center_of_pressure``. + ControllableGenericSurface.cn : AeroCoefficient + Yawing moment coefficient, about ``center_of_pressure``. + ControllableGenericSurface.cl : AeroCoefficient + Roll moment coefficient. + ControllableGenericSurface.cL : Function + Lift coefficient, the force perpendicular to the airflow. + ControllableGenericSurface.cD : Function + Drag coefficient, the force along the airflow. + ControllableGenericSurface.cQ : Function + Crosswind coefficient, the side force relative to the airflow. + ControllableGenericSurface.cN_alpha : AeroCoefficient + Slope of ``cN`` with the angle of attack, as a function of Mach. + Has units of 1/rad. + ControllableGenericSurface.cY_beta : AeroCoefficient + Slope of ``cY`` with the sideslip angle, as a function of Mach. + Has units of 1/rad. + ControllableGenericSurface.cm_alpha : AeroCoefficient + Slope of ``cm`` with the angle of attack, as a function of Mach. + Has units of 1/rad. + ControllableGenericSurface.cn_beta : AeroCoefficient + Slope of ``cn`` with the sideslip angle, as a function of Mach. + Has units of 1/rad. + ControllableGenericSurface.aerodynamic_center : Function + Pitch-plane aerodynamic center of the surface along the rocket's axis, + in meters from the surface's position (positive toward the nose), as a + function of Mach. It is the point where the change of the normal force + acts when the angle of attack changes a little from zero. It is the + same point as ``center_of_pressure`` when no moment coefficient is + given. + ControllableGenericSurface.aerodynamic_center_yaw : Function + Yaw-plane aerodynamic center of the surface along the rocket's axis, in + meters from the surface's position (positive toward the nose), as a + function of Mach: the same for the side force and the sideslip angle. + ControllableGenericSurface.reference_area : float + Reference area, in square meters. + ControllableGenericSurface.reference_length : float + Reference length, in meters. + ControllableGenericSurface.reynolds_length : float + Length scale of the Reynolds number, in meters. + ControllableGenericSurface.name : str + Name of the surface. + ControllableGenericSurface.center_of_pressure : tuple + Point where the forces are applied, ``(x, y, z)`` in meters, as given. + ControllableGenericSurface.cp : tuple + The same point as ``(cpx, cpy, cpz)``. ``cpz`` is 0 when the center of + pressure varies with Mach, since the force is then applied at ``z = 0``. + ControllableGenericSurface.cpx : float + x coordinate of ``cp``, in meters. + ControllableGenericSurface.cpy : float + y coordinate of ``cp``, in meters. + ControllableGenericSurface.cpz : float + z coordinate of ``cp``, in meters. + ControllableGenericSurface.active_during : str or callable + When the surface produces force, as given. + ControllableGenericSurface.is_active : callable + ``is_active(t, flight)``: whether the surface produces force at time + ``t`` of the flight. + ControllableGenericSurface.force_convention : str + Frame the force coefficients were given in: ``"body"`` or ``"wind"``. + ControllableGenericSurface.independent_vars : list of str + The variables every coefficient is called with, in order. + ControllableGenericSurface.prints : _GenericSurfacePrints + The prints of the surface. Use help(ControllableGenericSurface.prints) to + know more. + ControllableGenericSurface.plots : _GenericSurfacePlots + The plots of the surface. Use help(ControllableGenericSurface.plots) to + know more. """ # TODO: deflection-dependent static-margin diagnostics. @@ -28,10 +107,11 @@ class ControllableGenericSurface(GenericSurface): # functions live every step (see ``_coefficient_arguments``), and the surface # never physically moves, so its force-application point / ``cp_to_cdm`` cache # cannot go stale (unlike an individual fin's cant angle, which IS a physical - # reconfiguration and is refreshed via ``Rocket.refresh_controlled_components``). + # reconfiguration and counts up the fin's ``_version`` so the rocket + # refreshes it). # - # The gap is diagnostic-only. The derived ``center_of_pressure_z`` / - # ``aerodynamic_center`` come from ``cm_alpha = d(cm)/d(alpha)`` evaluated ONCE + # The gap is diagnostic-only. The surface's ``aerodynamic_center`` and the + # rocket's come from ``cm_alpha = d(cm)/d(alpha)`` evaluated ONCE # (in ``_set_stability_accessors``) with the control variables frozen at their # value at construction (0). So if ``cm`` couples alpha and a control axis # (e.g. an ``alpha * deflection`` term), the reported ``static_margin`` is @@ -45,8 +125,8 @@ class ControllableGenericSurface(GenericSurface): # derived cp accessors are built about a chosen reference deflection rather # than always 0; # - re-deriving the cp accessors when the deflection changes -- reuse the - # fin mechanism: bump ``_geometry_version`` in ``set_control`` and have - # ``Rocket.refresh_controlled_components`` re-run the derived-cp step; + # fin mechanism: bump ``_version`` in ``set_control`` so the rocket's + # stamp-based refresh re-runs the derived-cp step; # - dedicated stability plots/prints that sweep the static margin (and cp) # OVER the control-deflection range, since a single scalar margin is the # wrong abstraction for a controllable surface. @@ -63,34 +143,64 @@ def __init__( extrapolation=None, interpolation=None, active_during="always", + force_convention=None, ): - """Create a controllable generic aerodynamic surface. + """Create an aerodynamic surface whose coefficients also depend on + controls, such as a canard deflection. Parameters ---------- reference_area : int, float - Reference area of the surface, in squared meters. + Reference area of the surface, in square meters. Commonly the + rocket's cross-sectional area. reference_length : int, float - Reference length of the surface, in meters. + Reference length of the surface, in meters. Commonly the rocket's + diameter. Used to non-dimensionalize the moment coefficients and the + reduced rotation rates, and (unless ``reynolds_length`` is given) as + the length scale of the Reynolds number. coefficients : dict - The six force and moment coefficients (``cL``, ``cQ``, ``cD``, - ``cm``, ``cn``, ``cl``), by name. Each one can be a constant, a - function, or a path to a data file, and depends on the seven base - variables **plus** the controls listed in ``controls`` (in that - order). Any you leave out are set to 0. + The force and moment coefficients, by name. Any you leave out are 0: + the body-frame forces ``cN`` (normal), ``cY`` (side) and ``cA`` + (axial), or the wind-frame ones ``cL`` (lift), ``cQ`` (side) and + ``cD`` (drag), and the moments ``cm`` (pitch), ``cn`` (yaw) and + ``cl`` (roll), taken about ``center_of_pressure``. + + Each coefficient can depend on ``alpha`` and ``beta`` (radians, or + ``alpha_deg`` and ``beta_deg`` in degrees), ``alpha_total`` and + ``phi`` (or ``alpha_total_deg`` and ``phi_deg``; a ``cN``, ``cL`` or + ``cm`` against ``alpha_total`` alone is split between the pitch and + yaw planes for you, see :class:`GenericSurface`), ``mach``, ``reynolds``, the reduced rates + ``pitch_rate``, ``yaw_rate`` and ``roll_rate`` (such as + ``q * L_ref / (2 * V)``), and each name in ``controls``, for example + ``lambda alpha, mach, deflection: ...``. It can be given as a + number, a function whose arguments are named after the variables it + uses, the path to a CSV file whose header names the variables + (controls included), a list or numpy array of data points as a pair + with the names of its variables, a :class:`Function`, or a pair + ``(axes, values)`` for values on a regular grid. center_of_pressure : tuple, list, optional - Application point of the aerodynamic forces and moments in the local - surface frame. Default ``(0, 0, 0)``. + Point where the aerodynamic forces are applied and about which the + moment coefficients are taken, as ``(x, y, z)`` in meters. It is + measured from the position the surface is added to the rocket at: + ``z`` runs along the rocket's centerline and is positive toward the + nose, whichever coordinate system orientation the rocket uses. The + ``z`` component may instead vary with Mach, as a function of Mach, a + one-input :class:`Function` or a two-column table + ``[[mach, z], ...]``; the moments are then carried to that point by + the moment coefficients. Default ``(0, 0, 0)``. name : str, optional Name of the surface. Default ``"Controllable Generic Surface"``. controls : iterable of str, optional - Names of the controls, such as a canard deflection angle. Default - ``("deflection",)``. Each name becomes an extra input to every - coefficient and a key in :attr:`control_state`. + Names of the controls, such as a canard deflection angle. Each name + becomes an extra input to every coefficient, after the seven flow + variables and in this order, and a key in :attr:`control_state`. The + values start at 0 and are set with :meth:`set_control`. Default + ``("deflection",)``. reynolds_length : int, float, optional Length scale, in meters, of the Reynolds number passed to the - coefficients. See :class:`GenericSurface`. ``None`` (the default) - uses ``reference_length`` (the diameter). + coefficients. Set it to the length your Reynolds-dependent data was + tabulated against (for example the rocket's body length). ``None`` + (the default) uses ``reference_length`` (the diameter). extrapolation : str or dict, optional What tabulated coefficients do outside their data range: ``"constant"`` holds the nearest edge value, ``"natural"`` keeps @@ -99,22 +209,43 @@ def __init__( default) uses ``"constant"`` for tables built here and leaves a pre-built :class:`Function` unchanged. interpolation : str or dict, optional - How tabulated coefficients read values between points (for example - ``"linear"``, ``"akima"`` or ``"spline"`` for a 1-D table; see - :class:`rocketpy.GenericSurface` for the full list by table type). - Give one string for all coefficients or a dict keyed by coefficient - name. ``None`` (the default) uses ``"linear"`` for tables built here - and leaves a pre-built :class:`Function` unchanged. + How tabulated coefficients read values between points: a 1-D table + accepts ``"linear"``, ``"akima"``, ``"spline"`` and ``"polynomial"``; + a multi-dimensional scattered table accepts ``"linear"``, + ``"shepard"`` and ``"rbf"``; and a table on a regular grid accepts + ``"linear"``, ``"nearest"``, ``"slinear"``, ``"cubic"``, + ``"quintic"`` and ``"pchip"``. Give one string for all coefficients + or a dict keyed by coefficient name. ``None`` (the default) uses + ``"linear"`` for tables built here and leaves a pre-built + :class:`Function` unchanged. active_during : str or callable, optional When this surface produces force during a simulation: ``"always"`` (default), ``"power_on"`` (only while the motor burns, e.g. jet vanes), ``"power_off"`` (only after burnout), or a function - ``active_during(t, flight)``. See :class:`GenericSurface` for details. + ``active_during(t, flight)`` returning ``True`` when the surface is + active at time ``t`` (in seconds). + force_convention : str, optional + The frame the force coefficients are given in: ``"body"`` for + ``cN``/``cY``/``cA`` or ``"wind"`` for ``cL``/``cQ``/``cD``. ``None`` + (the default) works it out from the names. + + Raises + ------ + TypeError + If ``coefficients`` is not a dict, or ``center_of_pressure`` is not + an ``(x, y, z)`` triple. + ValueError + If a coefficient name, a variable name, ``force_convention`` or + ``active_during`` is not one of the accepted values, or if + wind-frame and body-frame force coefficients are mixed without a + ``force_convention``. + + See Also + -------- + :ref:`genericsurfaces` """ - # These must be set before ``super().__init__`` so coefficient - # processing (arity, CSV validation) and the derived-cp accessors see - # the extended variable list (via the ``independent_vars`` property, - # which appends ``control_variables``) and the current control values. + # Set before the base constructor, which reads them to build the + # coefficients self.control_variables = list(controls) self.control_state = {name: 0.0 for name in self.control_variables} @@ -127,33 +258,19 @@ def __init__( reynolds_length=reynolds_length, extrapolation=extrapolation, interpolation=interpolation, + force_convention=force_convention, active_during=active_during, ) # ``self.prints``/``self.plots`` are the generic ones wired by the base. - def _coefficient_arguments( - self, - alpha, - beta, - mach, - reynolds, - pitch_rate, - yaw_rate, - roll_rate, - ): - """Append the current control-variable values (in - ``self.control_variables`` order) to the standard inputs.""" - base = super()._coefficient_arguments( - alpha, - beta, - mach, - reynolds, - pitch_rate, - yaw_rate, - roll_rate, - ) - controls = tuple(self.control_state[name] for name in self.control_variables) - return base + controls + @classmethod + def _input_variable_names(cls, **kwargs): + controls = kwargs.get("controls", ("deflection",)) + return [*super()._input_variable_names(), *controls] + + def _coefficient_arguments(self, *state): + """Add the current value of each control, in ``control_variables`` order.""" + return (*state, *(self.control_state[name] for name in self.control_variables)) def _clamp_control(self, name, value): # pylint: disable=unused-argument """Hook to constrain a control value before it is stored. The base class @@ -182,55 +299,13 @@ def get_control(self, name): """Return the current value of a control variable.""" return self.control_state[name] - def to_dict( # pylint: disable=unused-argument - self, include_outputs=False, **kwargs - ): - # A preset ``active_during`` is stored as is; a custom (t, flight) -> bool - # function is pickled to text when allowed, otherwise dropped to "always" - # (a function cannot be restored without pickling). - active_during = self.active_during - if callable(active_during): - active_during = ( - to_hex_encode(active_during) - if kwargs.get("allow_pickle", True) - else "always" - ) - return { - "reference_area": self.reference_area, - "reference_length": self.reference_length, - "coefficients": { - "cN": self.cN, - "cY": self.cY, - "cA": self.cA, - "cm": self.cm, - "cn": self.cn, - "cl": self.cl, - }, - "center_of_pressure": self.center_of_pressure, - "name": self.name, - "controls": self.control_variables, - "reynolds_length": self.reynolds_length, - "active_during": active_during, - } + def to_dict(self, include_outputs=False, **kwargs): + data = super().to_dict(include_outputs=include_outputs, **kwargs) + data["controls"] = list(self.control_variables) + return data @classmethod - def from_dict(cls, data): - # A preset ``active_during`` is used as is; anything else is unpickled - # back into the original function (falling back to "always" if it cannot - # be restored). - active_during = data.get("active_during", "always") - if active_during not in ("always", "power_on", "power_off"): - try: - active_during = from_hex_decode(active_during) - except (TypeError, ValueError): - active_during = "always" - return cls( - reference_area=data["reference_area"], - reference_length=data["reference_length"], - coefficients=data["coefficients"], - center_of_pressure=data.get("center_of_pressure", (0, 0, 0)), - name=data.get("name", "Controllable Generic Surface"), - controls=data.get("controls", ("deflection",)), - reynolds_length=data.get("reynolds_length"), - active_during=active_during, - ) + def _arguments_from_dict(cls, data): + arguments = super()._arguments_from_dict(data) + arguments["controls"] = data.get("controls", ("deflection",)) + return arguments diff --git a/rocketpy/rocket/aero_surface/fins/_base_fin.py b/rocketpy/rocket/aero_surface/fins/_base_fin.py index 0f2fa7864..3839e0ad9 100644 --- a/rocketpy/rocket/aero_surface/fins/_base_fin.py +++ b/rocketpy/rocket/aero_surface/fins/_base_fin.py @@ -6,21 +6,14 @@ from rocketpy.mathutils.function import Function from .._barrowman_surface import _BarrowmanSurface -from ..linear_generic_surface import LinearGenericSurface +from ..generic_surface import GenericSurface -# TODO: review note: airfoil handling can now be fully implemented. That is -# instead of getting just the clalpha from the airfoil, we can get and use the -# full lift curve. We need to check if simulation does not break with this -# change. If we use a full curve for airfoils, then we will have fin stall -# (abrupt cN drop), but the other surfaces do not have this drop, they simply -# will have their generated normal forces grow linearly with AoA, this can lead -# to wrong behaviour at high AoA. class _BaseFin(_BarrowmanSurface): """ Base class for fins, shared by both Fin and Fins classes. Inherits from :class:`_BarrowmanSurface`, translating the fin geometry into - the linear generic-surface coefficient model. + generic-surface coefficients. Handles shared initialization logic and common properties. """ @@ -58,36 +51,28 @@ def __init__( self.reference_area = np.pi * rocket_radius**2 self.reference_length = self.rocket_diameter - # The linear generic-surface machinery is initialized lazily by - # ``_finalize_barrowman`` once the concrete subclass has set up its - # geometry strategy and the first ``_update_geometry_chain`` has - # produced ``clalpha``, ``cpz`` and ``roll_parameters``. - self._barrowman_initialized = False - def _update_reference_quantities(self): """Update quantities that depend on rocket radius.""" self.reference_area = np.pi * self._rocket_radius**2 self.reference_length = self.rocket_diameter - def _update_geometry_chain(self): - """Update geometry-dependent quantities in dependency order, then - (re)build the generic-surface coefficients from the new geometry.""" + def _evaluate_geometry(self): + """Compute the fin's geometric and aerodynamic parameters. + + Each step uses the results of the one before it, so the order matters. + """ self.evaluate_geometrical_parameters() self.evaluate_center_of_pressure() self.evaluate_lift_coefficient() self.evaluate_roll_parameters() - if self._barrowman_initialized: - # Geometry changed after construction: refresh the coefficients. - self.evaluate_coefficients() - self.compute_all_coefficients() - self._evaluate_stability_derivatives() - else: - self._finalize_barrowman() - def _finalize_barrowman(self): - """Initialize the linear generic-surface machinery from the geometry - computed by the first ``_update_geometry_chain`` call.""" - LinearGenericSurface.__init__( + def _build_surface(self): + """Compute the fin's parameters and set it up as an aerodynamic surface. + + Called once, at the end of the constructor of each fin class. + """ + self._evaluate_geometry() + GenericSurface.__init__( self, reference_area=self.reference_area, reference_length=self.reference_length, @@ -95,7 +80,61 @@ def _finalize_barrowman(self): center_of_pressure=(self.cpx, self.cpy, self.cpz), name=self.name, ) - self._barrowman_initialized = True + + def _update_geometry_chain(self): + """Recompute the fin after one of its dimensions changes. + + Called by the geometry setters once the fin is built. It also tells the + rockets holding the fin that it changed. + """ + self._evaluate_geometry() + self._geometry_changed() + + def _cant_changed(self): + """Update the fin after its cant angle changes. + + The coefficients read the cant angle each time they are evaluated, so + nothing is recomputed here: only the rockets holding the fin are told + that it changed. A controller may set the cant angle at every step of + the simulation, so this must stay cheap. ``Fin`` overrides it to also + turn its frame with the new cant angle. + """ + self._version += 1 + + @property + def _roll_cant_angle_rad(self): + """Cant angle, in radians, with the sign used by the roll coefficients + ``cl_0`` and ``cl``. + + ``Fins`` overrides it to flip the sign. + + Returns + ------- + float + Cant angle in radians. + """ + return self.cant_angle_rad + + @property + def roll_parameters(self): + """Roll forcing and roll damping coefficients of the fin, and its cant + angle. + + The roll moment coefficient of a fin set is + ``cl = -clf_delta * cant_angle + cld_omega * cos(cant_angle) * roll_rate``, + where ``roll_rate`` is the reduced roll rate ``p * L / (2 * V)``. A + positive cant angle therefore gives a negative roll moment. A single fin + has no forcing term: its roll moment comes from the force acting at its + center of pressure. + + Returns + ------- + list + ``[clf_delta, cld_omega, cant_angle_rad]``: the roll forcing and + roll damping coefficients, both Functions of Mach for the fin at + zero cant angle, and the cant angle in radians. + """ + return [self._clf_delta, self._cld_omega, self.cant_angle_rad] @property def rocket_radius(self): @@ -253,7 +292,7 @@ def cant_angle_rad(self, value): Cant angle in radians. """ self._cant_angle_rad = value - self._update_geometry_chain() + self._cant_changed() @property def airfoil(self): @@ -328,7 +367,7 @@ def evaluate_single_fin_lift_coefficient(self): ) # Normal-force coefficient derivative for a single fin - def lift_source(mach): + def force_source(mach): return ( clalpha2D(mach) * planform_correlation_parameter(mach) @@ -341,7 +380,7 @@ def lift_source(mach): ) self.clalpha_single_fin = Function( - lift_source, + force_source, "Mach", "Normal-force coefficient derivative for a single fin", ) @@ -377,3 +416,43 @@ def evaluate_shape(self): @abstractmethod def draw(self): """Draw or render the fin.""" + + def to_dict(self, include_outputs=False, **kwargs): + discretize = kwargs.get("discretize", False) + airfoil = None + if self.airfoil: + # The curve is saved rather than the file it came from, so the saved + # rocket loads anywhere. A table is kept as is: resampling it would + # change the slope the lift is computed from. + airfoil_cl = self.airfoil_cl + if discretize and callable(airfoil_cl.source): + limit = np.pi / 6 if self.airfoil[1] == "radians" else 30 + airfoil_cl = airfoil_cl.set_discrete( + -limit, limit, 50, mutate_self=False + ) + airfoil = (airfoil_cl, self.airfoil[1]) + data = { + "root_chord": self.root_chord, + "span": self.span, + "rocket_radius": self.rocket_radius, + "cant_angle": self.cant_angle, + "airfoil": airfoil, + "name": self.name, + } + if include_outputs: + clalpha = self.clalpha + if discretize: + clalpha = clalpha.set_discrete(0, 4, 50, mutate_self=False) + data.update( + { + "cp": self.cp, + "clalpha": clalpha, + "roll_parameters": self.roll_parameters, + "rocket_diameter": self.rocket_diameter, + "diameter": self.rocket_diameter, + "d": self.rocket_diameter, + "reference_area": self.reference_area, + "ref_area": self.reference_area, + } + ) + return data diff --git a/rocketpy/rocket/aero_surface/fins/_geometry.py b/rocketpy/rocket/aero_surface/fins/_geometry.py index 71b213909..6915a460f 100644 --- a/rocketpy/rocket/aero_surface/fins/_geometry.py +++ b/rocketpy/rocket/aero_surface/fins/_geometry.py @@ -40,12 +40,12 @@ def __init__( if sweep_length is not None and sweep_angle is not None: raise ValueError("Cannot use sweep_length and sweep_angle together") - if sweep_angle is not None: - sweep_length = np.tan(np.radians(sweep_angle)) * owner.span - elif sweep_length is None: + if sweep_angle is None and sweep_length is None: sweep_length = owner.root_chord - tip_chord self._tip_chord = tip_chord + # The sweep is kept as it was given, an angle or a length; the other is + # worked out from it self._sweep_length = sweep_length self._sweep_angle = sweep_angle @@ -59,11 +59,14 @@ def tip_chord(self, value): @property def sweep_length(self): + # A sweep given as an angle follows the span + if self._sweep_angle is not None: + return np.tan(np.radians(self._sweep_angle)) * self.owner.span return self._sweep_length @sweep_length.setter def sweep_length(self, value): - self._sweep_length = value + self._sweep_length, self._sweep_angle = value, None @property def sweep_angle(self): @@ -71,8 +74,7 @@ def sweep_angle(self): @sweep_angle.setter def sweep_angle(self, value): - self._sweep_angle = value - self._sweep_length = np.tan(np.radians(value)) * self.owner.span + self._sweep_angle, self._sweep_length = value, None def evaluate_geometrical_parameters(self): """Calculate trapezoidal fin geometric parameters.""" diff --git a/rocketpy/rocket/aero_surface/fins/elliptical_fin.py b/rocketpy/rocket/aero_surface/fins/elliptical_fin.py index 1db9dc75d..ae1f7af7a 100644 --- a/rocketpy/rocket/aero_surface/fins/elliptical_fin.py +++ b/rocketpy/rocket/aero_surface/fins/elliptical_fin.py @@ -76,11 +76,42 @@ class EllipticalFin(Fin): EllipticalFin.cpz : float Fin set local center of pressure z coordinate. Has units of length and is given in meters. - EllipticalFin.cl : Function - Roll-moment coefficient, inherited from the generic-surface model - (a function of the flow variables). Zero for a nose cone or tail; for a - fin set it carries the cant forcing and roll damping. The lift-curve - slope is ``clalpha``. + EllipticalFin.cN : AeroCoefficient + Normal force coefficient, the force in the pitch plane. + EllipticalFin.cY : AeroCoefficient + Side force coefficient, the force in the yaw plane. + EllipticalFin.cA : AeroCoefficient + Axial force coefficient, the force along the rocket's axis. + EllipticalFin.cm : AeroCoefficient + Pitching moment coefficient. + EllipticalFin.cn : AeroCoefficient + Yawing moment coefficient. + EllipticalFin.cl : AeroCoefficient + Roll moment coefficient, from the roll damping only. A canted fin + rolls the rocket through its side force. + EllipticalFin.cL : Function + Lift coefficient, the force perpendicular to the airflow. + EllipticalFin.cD : Function + Drag coefficient, the force along the airflow. + EllipticalFin.cQ : Function + Crosswind coefficient, the side force relative to the airflow. + EllipticalFin.cN_alpha : AeroCoefficient + Slope of ``cN`` with the angle of attack, as a function of Mach. + Has units of 1/rad. + EllipticalFin.cY_beta : AeroCoefficient + Slope of ``cY`` with the sideslip angle, as a function of Mach. + Has units of 1/rad. + EllipticalFin.cm_alpha : AeroCoefficient + Slope of ``cm`` with the angle of attack, as a function of Mach. + Has units of 1/rad. + EllipticalFin.cn_beta : AeroCoefficient + Slope of ``cn`` with the sideslip angle, as a function of Mach. + Has units of 1/rad. + EllipticalFin.cl_0 : AeroCoefficient + Roll moment coefficient at zero roll rate, as a function of Mach. + EllipticalFin.cl_p : AeroCoefficient + Slope of ``cl`` with the reduced roll rate (roll damping), as a + function of Mach. EllipticalFin.clalpha : float Normal-force coefficient slope. Has units of 1/rad. """ @@ -112,6 +143,8 @@ def __init__( cant_angle : int, float, optional Fins cant angle with respect to the rocket centerline. Must be given in degrees. + A positive cant angle gives a negative roll moment about the + rocket's axis (see :ref:`individual_fins`). sweep_length : int, float, optional Fins sweep length in meters. By sweep length, understand the axial distance between the fin root leading edge and the fin tip leading @@ -159,7 +192,7 @@ def __init__( ) self.geometry = _EllipticalGeometry(self) - self._update_geometry_chain() + self._build_surface() self.evaluate_shape() self.prints = _EllipticalFinPrints(self) @@ -171,10 +204,7 @@ def evaluate_center_of_pressure(self): tuple.""" # Barrowman elliptical-fin center of pressure location. cpz = 0.288 * self.root_chord - self.cpx = 0 - self.cpy = self.Yma - self.cpz = cpz - self.cp = (self.cpx, self.cpy, self.cpz) + self._set_center_of_pressure((0, self.Yma, cpz)) def to_dict(self, include_outputs=False, **kwargs): data = super().to_dict(include_outputs=include_outputs, **kwargs) diff --git a/rocketpy/rocket/aero_surface/fins/elliptical_fins.py b/rocketpy/rocket/aero_surface/fins/elliptical_fins.py index 74aea986a..31f4cbf3c 100644 --- a/rocketpy/rocket/aero_surface/fins/elliptical_fins.py +++ b/rocketpy/rocket/aero_surface/fins/elliptical_fins.py @@ -79,11 +79,41 @@ class EllipticalFins(Fins): EllipticalFins.cpz : float Fin set local center of pressure z coordinate. Has units of length and is given in meters. - EllipticalFins.cl : Function - Roll-moment coefficient, inherited from the generic-surface model - (a function of the flow variables). Zero for a nose cone or tail; for a - fin set it carries the cant forcing and roll damping. The lift-curve - slope is ``clalpha``. + EllipticalFins.cN : AeroCoefficient + Normal force coefficient, the force in the pitch plane. + EllipticalFins.cY : AeroCoefficient + Side force coefficient, the force in the yaw plane. + EllipticalFins.cA : AeroCoefficient + Axial force coefficient, the force along the rocket's axis. + EllipticalFins.cm : AeroCoefficient + Pitching moment coefficient. + EllipticalFins.cn : AeroCoefficient + Yawing moment coefficient. + EllipticalFins.cl : AeroCoefficient + Roll moment coefficient, from the cant angle and the roll damping. + EllipticalFins.cL : Function + Lift coefficient, the force perpendicular to the airflow. + EllipticalFins.cD : Function + Drag coefficient, the force along the airflow. + EllipticalFins.cQ : Function + Crosswind coefficient, the side force relative to the airflow. + EllipticalFins.cN_alpha : AeroCoefficient + Slope of ``cN`` with the angle of attack, as a function of Mach. + Has units of 1/rad. + EllipticalFins.cY_beta : AeroCoefficient + Slope of ``cY`` with the sideslip angle, as a function of Mach. + Has units of 1/rad. + EllipticalFins.cm_alpha : AeroCoefficient + Slope of ``cm`` with the angle of attack, as a function of Mach. + Has units of 1/rad. + EllipticalFins.cn_beta : AeroCoefficient + Slope of ``cn`` with the sideslip angle, as a function of Mach. + Has units of 1/rad. + EllipticalFins.cl_0 : AeroCoefficient + Roll moment coefficient at zero roll rate, as a function of Mach. + EllipticalFins.cl_p : AeroCoefficient + Slope of ``cl`` with the reduced roll rate (roll damping), as a + function of Mach. EllipticalFins.clalpha : float Normal-force coefficient slope. Has units of 1/rad. """ @@ -113,6 +143,8 @@ def __init__( cant_angle : int, float, optional Fins cant angle with respect to the rocket centerline. Must be given in degrees. + A positive cant angle gives a negative roll moment about the + rocket's axis (see :ref:`individual_fins`). sweep_length : int, float, optional Fins sweep length in meters. By sweep length, understand the axial distance between the fin root leading edge and the fin tip leading @@ -160,7 +192,7 @@ def __init__( ) self.geometry = _EllipticalGeometry(self) - self._update_geometry_chain() + self._build_surface() self.evaluate_shape() self.prints = _EllipticalFinsPrints(self) @@ -177,10 +209,7 @@ def evaluate_center_of_pressure(self): """ # Barrowman elliptical-fin center of pressure location. cpz = 0.288 * self.root_chord - self.cpx = 0 - self.cpy = 0 - self.cpz = cpz - self.cp = (self.cpx, self.cpy, self.cpz) + self._set_center_of_pressure((0, 0, cpz)) def to_dict(self, **kwargs): data = super().to_dict(**kwargs) diff --git a/rocketpy/rocket/aero_surface/fins/fin.py b/rocketpy/rocket/aero_surface/fins/fin.py index f5d71e932..61910efdf 100644 --- a/rocketpy/rocket/aero_surface/fins/fin.py +++ b/rocketpy/rocket/aero_surface/fins/fin.py @@ -2,8 +2,8 @@ import numpy as np -from rocketpy.mathutils.function import Function from rocketpy.mathutils.vector_matrix import Matrix, Vector +from rocketpy.rocket.aero_surface._helpers import _wind_axes from rocketpy.rocket.aero_surface.fins._base_fin import _BaseFin @@ -80,16 +80,48 @@ class Fin(_BaseFin): Fin.cpz : float Fin set local center of pressure z coordinate. Has units of length and is given in meters. - Fin.cl : Function - Roll-moment coefficient, inherited from the generic-surface model - (a function of the flow variables). Zero for a nose cone or tail; for a - fin set it carries the cant forcing and roll damping. The lift-curve - slope is ``clalpha``. + Fin.cN : AeroCoefficient + Normal force coefficient, the force in the pitch plane. + Fin.cY : AeroCoefficient + Side force coefficient, the force in the yaw plane. + Fin.cA : AeroCoefficient + Axial force coefficient, the force along the rocket's axis. + Fin.cm : AeroCoefficient + Pitching moment coefficient. + Fin.cn : AeroCoefficient + Yawing moment coefficient. + Fin.cl : AeroCoefficient + Roll moment coefficient, from the roll damping only. A canted fin + rolls the rocket through its side force. + Fin.cL : Function + Lift coefficient, the force perpendicular to the airflow. + Fin.cD : Function + Drag coefficient, the force along the airflow. + Fin.cQ : Function + Crosswind coefficient, the side force relative to the airflow. + Fin.cN_alpha : AeroCoefficient + Slope of ``cN`` with the angle of attack, as a function of Mach. + Has units of 1/rad. + Fin.cY_beta : AeroCoefficient + Slope of ``cY`` with the sideslip angle, as a function of Mach. + Has units of 1/rad. + Fin.cm_alpha : AeroCoefficient + Slope of ``cm`` with the angle of attack, as a function of Mach. + Has units of 1/rad. + Fin.cn_beta : AeroCoefficient + Slope of ``cn`` with the sideslip angle, as a function of Mach. + Has units of 1/rad. + Fin.cl_0 : AeroCoefficient + Roll moment coefficient at zero roll rate, as a function of Mach. + Fin.cl_p : AeroCoefficient + Slope of ``cl`` with the reduced roll rate (roll damping), as a + function of Mach. Fin.clalpha : float Normal-force coefficient slope. Has units of 1/rad. Fin.roll_parameters : list - List containing the roll moment lift coefficient, the roll moment - damping coefficient and the cant angle in radians. + List containing the roll moment forcing coefficient, the roll moment + damping coefficient (both for the fin at zero cant) and the cant angle + in radians. """ # A single fin contributes unequally to the pitch and yaw planes, so it is @@ -124,6 +156,8 @@ def __init__( cant_angle : int, float, optional Fin cant angle with respect to the rocket centerline. Must be given in degrees. + A positive cant angle gives a negative roll moment about the + rocket's axis (see :ref:`individual_fins`). airfoil : tuple, optional Default is null, in which case fins will be treated as flat plates. Otherwise, if tuple, fins will be considered as airfoils. The @@ -154,33 +188,22 @@ def __init__( self._angular_position = angular_position self._angular_position_rad = math.radians(angular_position) - def _update_geometry_chain(self): - """Run the base geometry/coefficient chain, then (re)build the body to - fin rotation matrices.""" - super()._update_geometry_chain() + def _evaluate_geometry(self): + """Compute the fin's parameters and its rotation relative to the rocket + body.""" + super()._evaluate_geometry() self.evaluate_rotation_matrix() - @property - def cant_angle(self): - return self._cant_angle - - @cant_angle.setter - def cant_angle(self, value): - self._cant_angle = value - self.cant_angle_rad = math.radians(value) - - @property - def cant_angle_rad(self): - return self._cant_angle_rad - - @cant_angle_rad.setter - def cant_angle_rad(self, value): - self._cant_angle_rad = value - self.evaluate_geometrical_parameters() - self.evaluate_center_of_pressure() - self.evaluate_lift_coefficient() - self.evaluate_roll_parameters() + def _cant_changed(self): + """Update the fin after its cant angle changes, turning its frame with + the new cant angle.""" self.evaluate_rotation_matrix() + super()._cant_changed() + + def _default_surface_rotation(self): + """A fin's frame is turned by its angular position and its cant angle, + see :meth:`evaluate_rotation_matrix`.""" + return self._rotation_fin_to_body @property def angular_position(self): @@ -198,7 +221,7 @@ def angular_position_rad(self): @angular_position_rad.setter def angular_position_rad(self, value): self._angular_position_rad = value - self.evaluate_rotation_matrix() + self._update_geometry_chain() def evaluate_lift_coefficient(self): """Calculates and returns the fin set's lift coefficient. @@ -239,11 +262,12 @@ def evaluate_roll_parameters(self): clf_delta.set_title( "Roll moment forcing coefficient derivative vs. Mach number" ) + # Damping of the fin at zero cant; the cos(cant) factor is applied + # when the moment is evaluated, so a new cant angle needs no rebuild cld_omega = -( 2 * self.roll_damping_interference_factor * self.clalpha_single_fin - * np.cos(self.cant_angle_rad) * self.roll_geometrical_constant / (self.reference_area * self.reference_length**2) ) # Function of mach number @@ -252,7 +276,7 @@ def evaluate_roll_parameters(self): cld_omega.set_title( "Roll moment damping coefficient derivative vs. Mach number" ) - self.roll_parameters = [clf_delta, cld_omega, self.cant_angle_rad] + self._clf_delta, self._cld_omega = clf_delta, cld_omega return self.roll_parameters def evaluate_rotation_matrix(self): @@ -335,21 +359,69 @@ def force_application_point(self): return Vector([self.cpx, self.cpy, self.cpz]) def evaluate_coefficients(self): - """Evaluate the fin's normal-force slope coefficients. + """Evaluate the fin's coefficients from its geometry. Sets ``cN_alpha`` (pitch plane) and ``cY_beta`` (yaw plane) from the fin's normal-force slope projected onto each plane by its angular - position. Moment coefficients are left at zero since the moment is - transported geometrically in :meth:`compute_forces_and_moments`. + position and reduced by the cosine of its cant angle, and the force + coefficients ``cN``, ``cY`` and ``cA`` as the force computed in + :meth:`compute_forces_and_moments` written as functions + of the angle of attack, the sideslip angle and Mach. Moment coefficients + are left at zero since the moment is transported geometrically, except + for the roll damping ``cl_p``. The roll moment a canted fin produces + comes from its force acting at its center of pressure, so there is no + ``cl_0``. """ + super().evaluate_coefficients() clalpha = self.clalpha - sin_sq = math.sin(self.angular_position_rad) ** 2 - cos_sq = math.cos(self.angular_position_rad) ** 2 + # The cant tilts the fin's force: only cos(cant) of it acts across the + # rocket's axis, the rest acts along it. The cant angle is read when + # the coefficient is evaluated, so a controller may change it freely. + pitch_share = math.sin(self.angular_position_rad) ** 2 + yaw_share = math.cos(self.angular_position_rad) ** 2 self.cN_alpha = self._mach_coefficient( - lambda mach: clalpha.get_value_opt(mach) * sin_sq + lambda mach: ( + clalpha.get_value_opt(mach) + * pitch_share + * math.cos(self.cant_angle_rad) + ), + "cN_alpha", ) self.cY_beta = self._mach_coefficient( - lambda mach: -clalpha.get_value_opt(mach) * cos_sq + lambda mach: ( + -clalpha.get_value_opt(mach) * yaw_share * math.cos(self.cant_angle_rad) + ), + "cY_beta", + ) + + def force(alpha, beta, mach): + # Same steps as compute_forces_and_moments, from the flow direction + u_x, u_y, u_z, _ = _wind_axes(alpha, beta) + stream_f = self._rotation_body_to_fin @ Vector([-u_x, -u_y, -u_z]) + attack_angle = math.atan2(stream_f[0], stream_f[2]) + return self._rotation_fin_to_body @ Vector( + [clalpha.get_value_opt(mach) * attack_angle, 0, 0] + ) + + self.cY = self._as_coefficient( + lambda alpha, beta, mach: force(alpha, beta, mach)[0], "cY" + ) + self.cN = self._as_coefficient( + lambda alpha, beta, mach: -force(alpha, beta, mach)[1], "cN" + ) + self.cA = self._as_coefficient( + lambda alpha, beta, mach: -force(alpha, beta, mach)[2], "cA" + ) + + cld_omega = self._cld_omega + self.cl_0 = self._mach_coefficient(lambda mach: 0.0, "cl_0") + self.cl = self._as_coefficient( + lambda mach, roll_rate: ( + cld_omega.get_value_opt(mach) + * math.cos(self.cant_angle_rad) + * roll_rate + ), + "cl", ) def compute_forces_and_moments( @@ -386,8 +458,9 @@ def compute_forces_and_moments( Returns ------- tuple of float - The aerodynamic forces (lift, side_force, drag) and moments - (pitch, yaw, roll) in the body frame. + The aerodynamic force components ``(R1, R2, R3)`` along the body + x, y and z axes and the moments ``(M1, M2, M3)`` about them, taken + about the rocket's center of dry mass. """ R1, R2, R3, M1, M2, M3 = 0, 0, 0, 0, 0, 0 @@ -412,12 +485,12 @@ def compute_forces_and_moments( M3 *= self.roll_forcing_interference_factor / self.lift_interference_factor # Roll damping - _, cld_omega, _ = self.roll_parameters M3_damping = ( (1 / 2 * rho * stream_speed) * self.reference_area * (self.reference_length) ** 2 - * cld_omega.get_value_opt(stream_mach) + * self._cld_omega.get_value_opt(stream_mach) + * math.cos(self.cant_angle_rad) * omega[2] # omega3 / 2 ) @@ -462,30 +535,9 @@ def _compute_leading_edge_position(self, position, _csys): position += p return position - def to_dict(self, include_outputs=False): - data = { - "angular_position": self.angular_position, - "root_chord": self.root_chord, - "span": self.span, - "rocket_radius": self.rocket_radius, - "cant_angle": self.cant_angle, - "airfoil": self.airfoil, - "name": self.name, - } - - if include_outputs: - data.update( - { - "cp": self.cp, - "clalpha": self.clalpha, - "roll_parameters": self.roll_parameters, - "rocket_diameter": self.rocket_diameter, - "diameter": self.rocket_diameter, - "d": self.rocket_diameter, - "reference_area": self.reference_area, - "ref_area": self.reference_area, - } - ) + def to_dict(self, include_outputs=False, **kwargs): + data = super().to_dict(include_outputs=include_outputs, **kwargs) + data["angular_position"] = self.angular_position return data def draw(self, *, filename=None): diff --git a/rocketpy/rocket/aero_surface/fins/fins.py b/rocketpy/rocket/aero_surface/fins/fins.py index 781857be3..a75db1ae4 100644 --- a/rocketpy/rocket/aero_surface/fins/fins.py +++ b/rocketpy/rocket/aero_surface/fins/fins.py @@ -1,6 +1,5 @@ import numpy as np -from rocketpy.mathutils.function import Function from rocketpy.rocket.aero_surface.fins._base_fin import _BaseFin @@ -79,16 +78,47 @@ class Fins(_BaseFin): Fins.cpz : float Fin set local center of pressure z coordinate. Has units of length and is given in meters. - Fins.cl : Function - Roll-moment coefficient, inherited from the generic-surface model - (a function of the flow variables). Zero for a nose cone or tail; for a - fin set it carries the cant forcing and roll damping. The lift-curve - slope is ``clalpha``. + Fins.cN : AeroCoefficient + Normal force coefficient, the force in the pitch plane. + Fins.cY : AeroCoefficient + Side force coefficient, the force in the yaw plane. + Fins.cA : AeroCoefficient + Axial force coefficient, the force along the rocket's axis. + Fins.cm : AeroCoefficient + Pitching moment coefficient. + Fins.cn : AeroCoefficient + Yawing moment coefficient. + Fins.cl : AeroCoefficient + Roll moment coefficient, from the cant angle and the roll damping. + Fins.cL : Function + Lift coefficient, the force perpendicular to the airflow. + Fins.cD : Function + Drag coefficient, the force along the airflow. + Fins.cQ : Function + Crosswind coefficient, the side force relative to the airflow. + Fins.cN_alpha : AeroCoefficient + Slope of ``cN`` with the angle of attack, as a function of Mach. + Has units of 1/rad. + Fins.cY_beta : AeroCoefficient + Slope of ``cY`` with the sideslip angle, as a function of Mach. + Has units of 1/rad. + Fins.cm_alpha : AeroCoefficient + Slope of ``cm`` with the angle of attack, as a function of Mach. + Has units of 1/rad. + Fins.cn_beta : AeroCoefficient + Slope of ``cn`` with the sideslip angle, as a function of Mach. + Has units of 1/rad. + Fins.cl_0 : AeroCoefficient + Roll moment coefficient at zero roll rate, as a function of Mach. + Fins.cl_p : AeroCoefficient + Slope of ``cl`` with the reduced roll rate (roll damping), as a + function of Mach. Fins.clalpha : float Normal-force coefficient slope. Has units of 1/rad. Fins.roll_parameters : list - List containing the roll moment lift coefficient, the roll moment - damping coefficient and the cant angle in radians. + List containing the roll moment forcing coefficient, the roll moment + damping coefficient (both for the fins at zero cant) and the cant angle + in radians. """ def __init__( @@ -116,6 +146,8 @@ def __init__( cant_angle : int, float, optional Fins cant angle with respect to the rocket centerline. Must be given in degrees. + A positive cant angle gives a negative roll moment about the + rocket's axis (see :ref:`individual_fins`). airfoil : tuple, optional Default is null, in which case fins will be treated as flat plates. Otherwise, if tuple, fins will be considered as airfoils. The @@ -139,7 +171,7 @@ def __init__( root_chord=root_chord, span=span, airfoil=airfoil, - cant_angle=-cant_angle, + cant_angle=cant_angle, ) # Store values @@ -149,13 +181,28 @@ def __init__( def n(self): return self._n + @property + def _roll_cant_angle_rad(self): + """Cant angle, in radians, with the sign used by the roll coefficients + ``cl_0`` and ``cl``. + + The roll forcing of a fin set uses the opposite sign convention to the + individual fins, whose roll moment comes from the force at their center + of pressure, so the cant angle is flipped here. The angle the user gave + is kept unchanged as ``cant_angle``, so it reads back, saves and loads + as given. + + Returns + ------- + float + Cant angle in radians, with its sign flipped. + """ + return -self.cant_angle_rad + @n.setter def n(self, value): self._n = value - self.evaluate_geometrical_parameters() - self.evaluate_center_of_pressure() - self.evaluate_lift_coefficient() - self.evaluate_roll_parameters() + self._update_geometry_chain() def evaluate_lift_coefficient(self): """Calculates and returns the fin set's lift coefficient. @@ -192,6 +239,8 @@ def evaluate_roll_parameters(self): roll moment damping coefficient and the cant angle in radians """ + # Scaled by n, not fin_num_correction(n): every canted fin adds the same + # roll moment, while their normal forces partly cancel in pitch and yaw clf_delta = ( self.roll_forcing_interference_factor * self.n @@ -199,24 +248,18 @@ def evaluate_roll_parameters(self): * self.clalpha_single_fin / self.reference_length ) # Function of mach number - # NOTE: roll forcing scales with the full fin count ``n`` -- every - # identically-canted fin contributes the same roll moment, with no - # cancellation. This differs from the normal-force slope, which uses the - # ``fin_num_correction(n)`` (~n/2) multiple-fin factor because fins at - # different roll angles partially cancel in pitch/yaw. Using - # ``fin_num_correction(n)`` here previously halved the roll forcing (and - # the roll rate) of a fin set relative to the equivalent individual fins. clf_delta.set_inputs("Mach") clf_delta.set_outputs("Roll moment forcing coefficient derivative") clf_delta.set_title( "Roll moment forcing coefficient derivative vs. Mach number" ) + # Damping of the fins at zero cant; the cos(cant) factor is applied + # when the moment is evaluated, so a new cant angle needs no rebuild cld_omega = -( 2 * self.roll_damping_interference_factor * self.n * self.clalpha_single_fin - * np.cos(self.cant_angle_rad) * self.roll_geometrical_constant / (self.reference_area * self.reference_length**2) ) # Function of mach number @@ -225,7 +268,7 @@ def evaluate_roll_parameters(self): cld_omega.set_title( "Roll moment damping coefficient derivative vs. Mach number" ) - self.roll_parameters = [clf_delta, cld_omega, self.cant_angle_rad] + self._clf_delta, self._cld_omega = clf_delta, cld_omega return self.roll_parameters @staticmethod @@ -252,47 +295,9 @@ def fin_num_correction(n): else: return n / 2 - def to_dict(self, **kwargs): - if self.airfoil: - if kwargs.get("discretize", False): - lower = -np.pi / 6 if self.airfoil[1] == "radians" else -30 - upper = np.pi / 6 if self.airfoil[1] == "radians" else 30 - airfoil = ( - self.airfoil_cl.set_discrete(lower, upper, 50, mutate_self=False), - self.airfoil[1], - ) - else: - airfoil = (self.airfoil_cl, self.airfoil[1]) if self.airfoil else None - else: - airfoil = None - data = { - "n": self.n, - "root_chord": self.root_chord, - "span": self.span, - "rocket_radius": self.rocket_radius, - "cant_angle": self.cant_angle, - "airfoil": airfoil, - "name": self.name, - } - - if kwargs.get("include_outputs", False): - clalpha = self.clalpha - if kwargs.get("discretize", False): - clalpha = clalpha.set_discrete(0, 4, 50) - - data.update( - { - "cp": self.cp, - "clalpha": clalpha, - "roll_parameters": self.roll_parameters, - "rocket_diameter": self.rocket_diameter, - "diameter": self.rocket_diameter, - "d": self.rocket_diameter, - "reference_area": self.reference_area, - "ref_area": self.reference_area, - } - ) - + def to_dict(self, include_outputs=False, **kwargs): + data = super().to_dict(include_outputs=include_outputs, **kwargs) + data["n"] = self.n return data def draw(self, *, filename=None): diff --git a/rocketpy/rocket/aero_surface/fins/free_form_fin.py b/rocketpy/rocket/aero_surface/fins/free_form_fin.py index eedb4b76f..03b580c91 100644 --- a/rocketpy/rocket/aero_surface/fins/free_form_fin.py +++ b/rocketpy/rocket/aero_surface/fins/free_form_fin.py @@ -71,11 +71,42 @@ class FreeFormFin(Fin): FreeFormFin.cpz : float Fin set local center of pressure z coordinate. Has units of length and is given in meters. - FreeFormFin.cl : Function - Roll-moment coefficient, inherited from the generic-surface model - (a function of the flow variables). Zero for a nose cone or tail; for a - fin set it carries the cant forcing and roll damping. The lift-curve - slope is ``clalpha``. + FreeFormFin.cN : AeroCoefficient + Normal force coefficient, the force in the pitch plane. + FreeFormFin.cY : AeroCoefficient + Side force coefficient, the force in the yaw plane. + FreeFormFin.cA : AeroCoefficient + Axial force coefficient, the force along the rocket's axis. + FreeFormFin.cm : AeroCoefficient + Pitching moment coefficient. + FreeFormFin.cn : AeroCoefficient + Yawing moment coefficient. + FreeFormFin.cl : AeroCoefficient + Roll moment coefficient, from the roll damping only. A canted fin + rolls the rocket through its side force. + FreeFormFin.cL : Function + Lift coefficient, the force perpendicular to the airflow. + FreeFormFin.cD : Function + Drag coefficient, the force along the airflow. + FreeFormFin.cQ : Function + Crosswind coefficient, the side force relative to the airflow. + FreeFormFin.cN_alpha : AeroCoefficient + Slope of ``cN`` with the angle of attack, as a function of Mach. + Has units of 1/rad. + FreeFormFin.cY_beta : AeroCoefficient + Slope of ``cY`` with the sideslip angle, as a function of Mach. + Has units of 1/rad. + FreeFormFin.cm_alpha : AeroCoefficient + Slope of ``cm`` with the angle of attack, as a function of Mach. + Has units of 1/rad. + FreeFormFin.cn_beta : AeroCoefficient + Slope of ``cn`` with the sideslip angle, as a function of Mach. + Has units of 1/rad. + FreeFormFin.cl_0 : AeroCoefficient + Roll moment coefficient at zero roll rate, as a function of Mach. + FreeFormFin.cl_p : AeroCoefficient + Slope of ``cl`` with the reduced roll rate (roll damping), as a + function of Mach. FreeFormFin.clalpha : float Normal-force coefficient slope. Has units of 1/rad. FreeFormFin.mac_length : float @@ -113,6 +144,8 @@ def __init__( cant_angle : int, float, optional Fins cant angle with respect to the rocket centerline. Must be given in degrees. + A positive cant angle gives a negative roll moment about the + rocket's axis (see :ref:`individual_fins`). airfoil : tuple, optional Default is null, in which case fins will be treated as flat plates. Otherwise, if tuple, fins will be considered as airfoils. The @@ -147,7 +180,7 @@ def __init__( ) self.geometry = _FreeFormGeometry(self, shape_points) - self._update_geometry_chain() + self._build_surface() self.evaluate_shape() self.prints = _FreeFormFinPrints(self) @@ -164,10 +197,7 @@ def evaluate_center_of_pressure(self): """ # Center of pressure position in local coordinates cpz = self.mac_lead + 0.25 * self.mac_length - self.cpx = 0 - self.cpy = self.Yma - self.cpz = cpz - self.cp = (self.cpx, self.cpy, self.cpz) + self._set_center_of_pressure((0, self.Yma, cpz)) @property def shape_points(self): diff --git a/rocketpy/rocket/aero_surface/fins/free_form_fins.py b/rocketpy/rocket/aero_surface/fins/free_form_fins.py index d6186e9eb..549d0ad80 100644 --- a/rocketpy/rocket/aero_surface/fins/free_form_fins.py +++ b/rocketpy/rocket/aero_surface/fins/free_form_fins.py @@ -72,11 +72,41 @@ class FreeFormFins(Fins): FreeFormFins.cpz : float Fin set local center of pressure z coordinate. Has units of length and is given in meters. - FreeFormFins.cl : Function - Roll-moment coefficient, inherited from the generic-surface model - (a function of the flow variables). Zero for a nose cone or tail; for a - fin set it carries the cant forcing and roll damping. The lift-curve - slope is ``clalpha``. + FreeFormFins.cN : AeroCoefficient + Normal force coefficient, the force in the pitch plane. + FreeFormFins.cY : AeroCoefficient + Side force coefficient, the force in the yaw plane. + FreeFormFins.cA : AeroCoefficient + Axial force coefficient, the force along the rocket's axis. + FreeFormFins.cm : AeroCoefficient + Pitching moment coefficient. + FreeFormFins.cn : AeroCoefficient + Yawing moment coefficient. + FreeFormFins.cl : AeroCoefficient + Roll moment coefficient, from the cant angle and the roll damping. + FreeFormFins.cL : Function + Lift coefficient, the force perpendicular to the airflow. + FreeFormFins.cD : Function + Drag coefficient, the force along the airflow. + FreeFormFins.cQ : Function + Crosswind coefficient, the side force relative to the airflow. + FreeFormFins.cN_alpha : AeroCoefficient + Slope of ``cN`` with the angle of attack, as a function of Mach. + Has units of 1/rad. + FreeFormFins.cY_beta : AeroCoefficient + Slope of ``cY`` with the sideslip angle, as a function of Mach. + Has units of 1/rad. + FreeFormFins.cm_alpha : AeroCoefficient + Slope of ``cm`` with the angle of attack, as a function of Mach. + Has units of 1/rad. + FreeFormFins.cn_beta : AeroCoefficient + Slope of ``cn`` with the sideslip angle, as a function of Mach. + Has units of 1/rad. + FreeFormFins.cl_0 : AeroCoefficient + Roll moment coefficient at zero roll rate, as a function of Mach. + FreeFormFins.cl_p : AeroCoefficient + Slope of ``cl`` with the reduced roll rate (roll damping), as a + function of Mach. FreeFormFins.clalpha : float Normal-force coefficient slope. Has units of 1/rad. FreeFormFins.mac_length : float @@ -112,6 +142,8 @@ def __init__( cant_angle : int, float, optional Fins cant angle with respect to the rocket centerline. Must be given in degrees. + A positive cant angle gives a negative roll moment about the + rocket's axis (see :ref:`individual_fins`). airfoil : tuple, optional Default is null, in which case fins will be treated as flat plates. Otherwise, if tuple, fins will be considered as airfoils. The @@ -146,7 +178,7 @@ def __init__( ) self.geometry = _FreeFormGeometry(self, shape_points) - self._update_geometry_chain() + self._build_surface() self.evaluate_shape() self.prints = _FreeFormFinsPrints(self) @@ -163,10 +195,7 @@ def evaluate_center_of_pressure(self): """ # Center of pressure position in local coordinates cpz = self.mac_lead + 0.25 * self.mac_length - self.cpx = 0 - self.cpy = 0 - self.cpz = cpz - self.cp = (self.cpx, self.cpy, self.cpz) + self._set_center_of_pressure((0, 0, cpz)) @property def shape_points(self): diff --git a/rocketpy/rocket/aero_surface/fins/trapezoidal_fin.py b/rocketpy/rocket/aero_surface/fins/trapezoidal_fin.py index 49e594ec1..ea966f1cd 100644 --- a/rocketpy/rocket/aero_surface/fins/trapezoidal_fin.py +++ b/rocketpy/rocket/aero_surface/fins/trapezoidal_fin.py @@ -49,9 +49,12 @@ class TrapezoidalFin(Fin): Fins sweep length in meters. By sweep length, understand the axial distance between the fin root leading edge and the fin tip leading edge measured parallel to the rocket centerline. - TrapezoidalFin.sweep_angle : float + TrapezoidalFin.sweep_angle : float or None Fins sweep angle with respect to the rocket centerline. Must be given in degrees. + ``None`` when the sweep is given as a length. Setting ``sweep_length`` + replaces the angle, and a fin swept by an angle keeps that angle when + its span changes. TrapezoidalFin.rocket_diameter : float Reference diameter of the rocket, in meters. TrapezoidalFins.fin_area : float @@ -80,6 +83,42 @@ class TrapezoidalFin(Fin): TrapezoidalFin.cpz : float Fin set local center of pressure z coordinate. Has units of length and is given in meters. + TrapezoidalFin.cN : AeroCoefficient + Normal force coefficient, the force in the pitch plane. + TrapezoidalFin.cY : AeroCoefficient + Side force coefficient, the force in the yaw plane. + TrapezoidalFin.cA : AeroCoefficient + Axial force coefficient, the force along the rocket's axis. + TrapezoidalFin.cm : AeroCoefficient + Pitching moment coefficient. + TrapezoidalFin.cn : AeroCoefficient + Yawing moment coefficient. + TrapezoidalFin.cl : AeroCoefficient + Roll moment coefficient, from the roll damping only. A canted fin + rolls the rocket through its side force. + TrapezoidalFin.cL : Function + Lift coefficient, the force perpendicular to the airflow. + TrapezoidalFin.cD : Function + Drag coefficient, the force along the airflow. + TrapezoidalFin.cQ : Function + Crosswind coefficient, the side force relative to the airflow. + TrapezoidalFin.cN_alpha : AeroCoefficient + Slope of ``cN`` with the angle of attack, as a function of Mach. + Has units of 1/rad. + TrapezoidalFin.cY_beta : AeroCoefficient + Slope of ``cY`` with the sideslip angle, as a function of Mach. + Has units of 1/rad. + TrapezoidalFin.cm_alpha : AeroCoefficient + Slope of ``cm`` with the angle of attack, as a function of Mach. + Has units of 1/rad. + TrapezoidalFin.cn_beta : AeroCoefficient + Slope of ``cn`` with the sideslip angle, as a function of Mach. + Has units of 1/rad. + TrapezoidalFin.cl_0 : AeroCoefficient + Roll moment coefficient at zero roll rate, as a function of Mach. + TrapezoidalFin.cl_p : AeroCoefficient + Slope of ``cl`` with the reduced roll rate (roll damping), as a + function of Mach. """ def __init__( @@ -114,6 +153,8 @@ def __init__( cant_angle : int, float, optional Fins cant angle with respect to the rocket centerline. Must be given in degrees. + A positive cant angle gives a negative roll moment about the + rocket's axis (see :ref:`individual_fins`). sweep_length : int, float, optional Fins sweep length in meters. By sweep length, understand the axial distance between the fin root leading edge and the fin tip leading @@ -161,7 +202,7 @@ def __init__( sweep_length=sweep_length, sweep_angle=sweep_angle, ) - self._update_geometry_chain() + self._build_surface() self.evaluate_shape() self.prints = _TrapezoidalFinPrints(self) @@ -214,10 +255,7 @@ def evaluate_center_of_pressure(self): + self.tip_chord - self.root_chord * self.tip_chord / (self.root_chord + self.tip_chord) ) - self.cpx = 0 - self.cpy = self.Yma - self.cpz = cpz - self.cp = (self.cpx, self.cpy, self.cpz) + self._set_center_of_pressure((0, self.Yma, cpz)) def to_dict(self, include_outputs=False, **kwargs): data = super().to_dict(include_outputs=include_outputs, **kwargs) @@ -226,6 +264,13 @@ def to_dict(self, include_outputs=False, **kwargs): @classmethod def from_dict(cls, data): + # The sweep is given back the way the user gave it, as an angle or a + # length; the constructor takes only one of the two + sweep = ( + {"sweep_angle": data["sweep_angle"]} + if data.get("sweep_angle") is not None + else {"sweep_length": data.get("sweep_length")} + ) return cls( angular_position=data["angular_position"], root_chord=data["root_chord"], @@ -233,7 +278,7 @@ def from_dict(cls, data): span=data["span"], rocket_radius=data["rocket_radius"], cant_angle=data["cant_angle"], - sweep_length=data.get("sweep_length"), airfoil=data["airfoil"], name=data["name"], + **sweep, ) diff --git a/rocketpy/rocket/aero_surface/fins/trapezoidal_fins.py b/rocketpy/rocket/aero_surface/fins/trapezoidal_fins.py index dfffa4a83..8a3d140e4 100644 --- a/rocketpy/rocket/aero_surface/fins/trapezoidal_fins.py +++ b/rocketpy/rocket/aero_surface/fins/trapezoidal_fins.py @@ -48,9 +48,12 @@ class TrapezoidalFins(Fins): Fins sweep length in meters. By sweep length, understand the axial distance between the fin root leading edge and the fin tip leading edge measured parallel to the rocket centerline. - TrapezoidalFins.sweep_angle : float + TrapezoidalFins.sweep_angle : float or None Fins sweep angle with respect to the rocket centerline. Must be given in degrees. + ``None`` when the sweep is given as a length. Setting ``sweep_length`` + replaces the angle, and a fin swept by an angle keeps that angle when + its span changes. TrapezoidalFins.rocket_diameter : float Reference diameter of the rocket, in meters. TrapezoidalFins.reference_area : float @@ -81,11 +84,41 @@ class TrapezoidalFins(Fins): TrapezoidalFins.cpz : float Fin set local center of pressure z coordinate. Has units of length and is given in meters. - TrapezoidalFins.cl : Function - Roll-moment coefficient, inherited from the generic-surface model - (a function of the flow variables). Zero for a nose cone or tail; for a - fin set it carries the cant forcing and roll damping. The lift-curve - slope is ``clalpha``. + TrapezoidalFins.cN : AeroCoefficient + Normal force coefficient, the force in the pitch plane. + TrapezoidalFins.cY : AeroCoefficient + Side force coefficient, the force in the yaw plane. + TrapezoidalFins.cA : AeroCoefficient + Axial force coefficient, the force along the rocket's axis. + TrapezoidalFins.cm : AeroCoefficient + Pitching moment coefficient. + TrapezoidalFins.cn : AeroCoefficient + Yawing moment coefficient. + TrapezoidalFins.cl : AeroCoefficient + Roll moment coefficient, from the cant angle and the roll damping. + TrapezoidalFins.cL : Function + Lift coefficient, the force perpendicular to the airflow. + TrapezoidalFins.cD : Function + Drag coefficient, the force along the airflow. + TrapezoidalFins.cQ : Function + Crosswind coefficient, the side force relative to the airflow. + TrapezoidalFins.cN_alpha : AeroCoefficient + Slope of ``cN`` with the angle of attack, as a function of Mach. + Has units of 1/rad. + TrapezoidalFins.cY_beta : AeroCoefficient + Slope of ``cY`` with the sideslip angle, as a function of Mach. + Has units of 1/rad. + TrapezoidalFins.cm_alpha : AeroCoefficient + Slope of ``cm`` with the angle of attack, as a function of Mach. + Has units of 1/rad. + TrapezoidalFins.cn_beta : AeroCoefficient + Slope of ``cn`` with the sideslip angle, as a function of Mach. + Has units of 1/rad. + TrapezoidalFins.cl_0 : AeroCoefficient + Roll moment coefficient at zero roll rate, as a function of Mach. + TrapezoidalFins.cl_p : AeroCoefficient + Slope of ``cl`` with the reduced roll rate (roll damping), as a + function of Mach. TrapezoidalFins.clalpha : float Normal-force coefficient slope. Has units of 1/rad. """ @@ -120,6 +153,8 @@ def __init__( cant_angle : int, float, optional Fins cant angle with respect to the rocket centerline. Must be given in degrees. + A positive cant angle gives a negative roll moment about the + rocket's axis (see :ref:`individual_fins`). sweep_length : int, float, optional Fins sweep length in meters. By sweep length, understand the axial distance between the fin root leading edge and the fin tip leading @@ -172,7 +207,7 @@ def __init__( sweep_length=sweep_length, sweep_angle=sweep_angle, ) - self._update_geometry_chain() + self._build_surface() self.evaluate_shape() self.prints = _TrapezoidalFinsPrints(self) @@ -225,10 +260,7 @@ def evaluate_center_of_pressure(self): + self.tip_chord - self.root_chord * self.tip_chord / (self.root_chord + self.tip_chord) ) - self.cpx = 0 - self.cpy = 0 - self.cpz = cpz - self.cp = (self.cpx, self.cpy, self.cpz) + self._set_center_of_pressure((0, 0, cpz)) def to_dict(self, **kwargs): data = super().to_dict(**kwargs) @@ -239,6 +271,13 @@ def to_dict(self, **kwargs): @classmethod def from_dict(cls, data): + # The sweep is given back the way the user gave it, as an angle or a + # length; the constructor takes only one of the two + sweep = ( + {"sweep_angle": data["sweep_angle"]} + if data.get("sweep_angle") is not None + else {"sweep_length": data.get("sweep_length")} + ) return cls( n=data["n"], root_chord=data["root_chord"], @@ -248,5 +287,5 @@ def from_dict(cls, data): cant_angle=data["cant_angle"], airfoil=data["airfoil"], name=data["name"], - sweep_length=data.get("sweep_length"), + **sweep, ) diff --git a/rocketpy/rocket/aero_surface/generic_surface.py b/rocketpy/rocket/aero_surface/generic_surface.py index 174a95836..29771c953 100644 --- a/rocketpy/rocket/aero_surface/generic_surface.py +++ b/rocketpy/rocket/aero_surface/generic_surface.py @@ -1,4 +1,4 @@ -import inspect +import csv import math import numpy as np @@ -7,100 +7,139 @@ from rocketpy.mathutils.vector_matrix import Matrix, Vector from rocketpy.plots.aero_surface_plots import _GenericSurfacePlots from rocketpy.prints.aero_surface_prints import _GenericSurfacePrints +from rocketpy.rocket.aero_surface._helpers import ( + _as_function, + _body_to_wind_coefficients, + _total_angle_to_body_coefficients, + _wind_plane_lift_to_body_coefficients, + _wind_to_body_coefficients, +) from rocketpy.rocket.aero_surface.aero_coefficient import ( + SOURCE_ONLY_NAMES, AeroCoefficient, build_independent_vars, ) from rocketpy.tools import from_hex_decode, to_hex_encode -def _as_function(func, independent_vars, name): - """Wrap a variadic callable as a :class:`Function` over ``independent_vars``. - - ``Function`` reads its domain dimension from the callable's parameter count, - so a variadic wrapper is given an explicit signature to advertise one - parameter per independent variable. - """ - func.__signature__ = inspect.Signature( - inspect.Parameter(var, inspect.Parameter.POSITIONAL_OR_KEYWORD) - for var in independent_vars - ) - return Function(func, list(independent_vars), [name]) - - -def wind_to_body_coefficients(c_lift, c_drag, c_side, independent_vars): - """Rotate wind-frame force coefficients into the body frame. - - Given the lift, drag and side-force coefficients (each callable over the - surface's independent-variable tuple, with the angle of attack and sideslip - as the first two variables), return the body-frame normal, side and axial - coefficients ``(cN, cY, cA)`` as :class:`Function`s over the same variables. - """ - lift, drag, side = c_lift.get_value_opt, c_drag.get_value_opt, c_side.get_value_opt - - def normal(*args): - alpha, beta = args[0], args[1] - transverse = math.sin(beta) * side(*args) + math.cos(beta) * drag(*args) - return math.cos(alpha) * lift(*args) + math.sin(alpha) * transverse - - def yaw_side(*args): - beta = args[1] - return math.cos(beta) * side(*args) - math.sin(beta) * drag(*args) - - def axial(*args): - alpha, beta = args[0], args[1] - transverse = math.sin(beta) * side(*args) + math.cos(beta) * drag(*args) - return -math.sin(alpha) * lift(*args) + math.cos(alpha) * transverse - - return ( - _as_function(normal, independent_vars, "cN"), - _as_function(yaw_side, independent_vars, "cY"), - _as_function(axial, independent_vars, "cA"), - ) - - -def body_to_wind_coefficients(c_normal, c_side, c_axial, independent_vars): - """Rotate body-frame force coefficients into the wind frame. - - Inverse of :func:`wind_to_body_coefficients`: given the body-frame normal, - side and axial coefficients, return the wind-frame lift, drag and - side-force coefficients ``(cL, cD, cQ)`` as :class:`Function`s. - """ - normal = c_normal.get_value_opt - side = c_side.get_value_opt - axial = c_axial.get_value_opt - - def lift(*args): - alpha = args[0] - return math.cos(alpha) * normal(*args) - math.sin(alpha) * axial(*args) - - def drag(*args): - alpha, beta = args[0], args[1] - longitudinal = math.sin(alpha) * normal(*args) + math.cos(alpha) * axial(*args) - return -math.sin(beta) * side(*args) + math.cos(beta) * longitudinal - - def yaw_side(*args): - alpha, beta = args[0], args[1] - longitudinal = math.sin(alpha) * normal(*args) + math.cos(alpha) * axial(*args) - return math.cos(beta) * side(*args) + math.sin(beta) * longitudinal - - return ( - _as_function(lift, independent_vars, "cL"), - _as_function(drag, independent_vars, "cD"), - _as_function(yaw_side, independent_vars, "cQ"), - ) - - class GenericSurface: - """Defines a generic aerodynamic surface with custom force and moment - coefficients. The coefficients can be nonlinear functions of the angle of - attack, sideslip angle, Mach number, Reynolds number, pitch rate, yaw rate - and roll rate.""" + """An aerodynamic surface of the rocket, defined by its force and moment + coefficients. + + Every aerodynamic part of a rocket is a ``GenericSurface``. The nose cone, + the fins and the tail work out their coefficients from their geometry, while + a ``GenericSurface`` created directly takes the coefficients you give it, + from wind-tunnel data, CFD or a model of your own. The coefficients can be + nonlinear functions of the angle of attack, sideslip angle, Mach number, + Reynolds number, pitch rate, yaw rate and roll rate. + + Attributes + ---------- + GenericSurface.reference_area : float + Reference area, in square meters. + GenericSurface.reference_length : float + Reference length, in meters. + GenericSurface.reynolds_length : float + Length scale of the Reynolds number, in meters. + GenericSurface.name : str + Name of the surface. + GenericSurface.center_of_pressure : tuple + Point where the forces are applied, ``(x, y, z)`` in meters, as given. + GenericSurface.cp : tuple + The same point as ``(cpx, cpy, cpz)``. ``cpz`` is 0 when the center of + pressure varies with Mach, since the force is then applied at ``z = 0``. + GenericSurface.cpx : float + x coordinate of ``cp``, in meters. + GenericSurface.cpy : float + y coordinate of ``cp``, in meters. + GenericSurface.cpz : float + z coordinate of ``cp``, in meters. + GenericSurface.active_during : str or callable + When the surface produces force, as given. + GenericSurface.is_active : callable + ``is_active(t, flight)``: whether the surface produces force at time + ``t`` of the flight. + GenericSurface.force_convention : str + Frame the force coefficients were given in: ``"body"`` or ``"wind"``. + GenericSurface.independent_vars : list of str + The variables every coefficient is called with, in order. + GenericSurface.is_axisymmetric : bool + Whether the surface acts the same in the pitch and yaw planes. False + here, since given coefficients may differ between the planes. + GenericSurface.cN : AeroCoefficient + Normal force coefficient, the force in the pitch plane. + GenericSurface.cY : AeroCoefficient + Side force coefficient, the force in the yaw plane. + GenericSurface.cA : AeroCoefficient + Axial force coefficient, the force along the rocket's axis. + GenericSurface.cm : AeroCoefficient + Pitching moment coefficient, about ``center_of_pressure``. + GenericSurface.cn : AeroCoefficient + Yawing moment coefficient, about ``center_of_pressure``. + GenericSurface.cl : AeroCoefficient + Roll moment coefficient. + GenericSurface.cL : Function + Lift coefficient, the force perpendicular to the airflow. + GenericSurface.cD : Function + Drag coefficient, the force along the airflow. + GenericSurface.cQ : Function + Crosswind coefficient, the side force relative to the airflow. + GenericSurface.cN_alpha : AeroCoefficient + Slope of ``cN`` with the angle of attack, as a function of Mach. + Has units of 1/rad. + GenericSurface.cY_beta : AeroCoefficient + Slope of ``cY`` with the sideslip angle, as a function of Mach. + Has units of 1/rad. + GenericSurface.cm_alpha : AeroCoefficient + Slope of ``cm`` with the angle of attack, as a function of Mach. + Has units of 1/rad. + GenericSurface.cn_beta : AeroCoefficient + Slope of ``cn`` with the sideslip angle, as a function of Mach. + Has units of 1/rad. + GenericSurface.aerodynamic_center : Function + Pitch-plane aerodynamic center of the surface along the rocket's axis, + in meters from the surface's position (positive toward the nose), as a + function of Mach. It is the point where the change of the normal force + acts when the angle of attack changes a little from zero. It is the + same point as ``center_of_pressure`` when no moment coefficient is + given. + GenericSurface.aerodynamic_center_yaw : Function + Yaw-plane aerodynamic center of the surface along the rocket's axis, in + meters from the surface's position (positive toward the nose), as a + function of Mach: the same for the side force and the sideslip angle. + GenericSurface.prints : _GenericSurfacePrints + The prints of the surface. Use help(GenericSurface.prints) to know more. + GenericSurface.plots : _GenericSurfacePlots + The plots of the surface. Use help(GenericSurface.plots) to know more. + """ # Whether this surface contributes identically to the pitch and yaw planes. # ``False`` for a generic surface (its coefficients may differ between planes) is_axisymmetric = False + # Counts changes to the surface (its geometry, its center of pressure, its + # orientation), so a rocket using it can tell when to update what it + # derived from it. Every setter that changes the surface adds one. + _version = 0 + + # The frames the force coefficients can be given in + _FORCE_CONVENTIONS = ("body", "wind") + # Body-frame coefficients that were given in the plane of the wind (against + # the total angle of attack) and split between the pitch and yaw planes + _wind_plane_names = frozenset() + # Mach numbers at which a coefficient in the plane of the wind is checked to vanish at + # zero total angle of attack + _ZERO_ANGLE_MACHS = (0.0, 0.3, 0.9, 2.0) + # Step of the numerical stability slopes, in radians + _SLOPE_STEP = 1e-6 + # Coefficients that have a direction across the rocket's axis. Given against + # the total angle of attack alone, they act in the plane of the wind. + _DIRECTIONAL_COEFFICIENTS = ("cN", "cY", "cm", "cn", "cL", "cQ") + + # Force-coefficient names in each frame. Moments (cm/cn/cl) are frame-shared. + _WIND_FORCE_NAMES = ("cL", "cQ", "cD") + _BODY_FORCE_NAMES = ("cN", "cY", "cA") + def __init__( self, reference_area, @@ -114,70 +153,98 @@ def __init__( force_convention=None, active_during="always", ): - """Create a generic aerodynamic surface, defined by its aerodynamic - coefficients. This surface is used to model any aerodynamic surface - that does not fit the predefined classes. - - Important - --------- - All the aerodynamic coefficients can be input as callable functions of - angle of attack, angle of sideslip, Mach number, Reynolds number, - pitch rate, yaw rate and roll rate. For CSV files, the header must - contain at least one of the following: "alpha", "beta", "mach", - "reynolds", "pitch_rate", "yaw_rate" and "roll_rate". The - independent variable columns can be provided in any order. - - The Reynolds number ("reynolds") is by default built on the reference - length (the rocket diameter). Published rocket data and tools often base - Reynolds on the **body length** instead, which for a slender rocket is - much larger (Re scales with the chosen length). If your coefficient - table uses a different length than the reference length, pass that - length as ``reynolds_length`` so the Reynolds number the simulation - feeds your table matches the one it was built against. - - The angular-rate inputs ("pitch_rate", "yaw_rate", "roll_rate") are the - conventional **non-dimensional reduced rates**, - ``q* = q * L_ref / (2 * V)`` (and likewise for ``r``/``p``). - Provide coefficient tables against the reduced rates, not the raw body - rates in rad/s. + """Create an aerodynamic surface from its aerodynamic coefficients. - See Also - -------- - :ref:`genericsurfaces`. + Use it for a part that does not fit the predefined classes (nose cone, + fins, tail, ...), or for a model of the whole rocket. Parameters ---------- reference_area : int, float - Reference area of the aerodynamic surface. Has the unit of meters - squared. Commonly defined as the rocket's cross-sectional area. + Reference area of the surface, in square meters. Commonly the + rocket's cross-sectional area. reference_length : int, float - Reference length of the aerodynamic surface, in meters. Commonly the - rocket's diameter. Used to non-dimensionalize the moment coefficients - and the reduced rotation rates, and (unless ``reynolds_length`` is - given) as the length scale of the Reynolds number. - coefficients: dict - The six force and moment coefficients, by name. Any you leave out are - set to 0. Each one can be a constant number, a function of the flow - variables, a list of data points, or a path to a CSV file. By default - the force coefficients are the body-frame ones (see - ``force_convention``); the wind-frame names ``cL``/``cQ``/``cD`` are - also accepted. The coefficients are:\n - cN: str, callable, optional - Normal force coefficient (body frame). Default is 0.\n - cY: str, callable, optional - Side force coefficient (body frame). Default is 0.\n - cA: str, callable, optional - Axial force coefficient (body frame). Default is 0.\n - cm: str, callable, optional - Pitch moment coefficient. Default is 0.\n - cn: str, callable, optional - Yaw moment coefficient. Default is 0.\n - cl: str, callable, optional - Roll moment coefficient. Default is 0.\n + Reference length of the surface, in meters. Commonly the rocket's + diameter. Used to non-dimensionalize the moment coefficients and the + reduced rotation rates, and (unless ``reynolds_length`` is given) as + the length scale of the Reynolds number. + coefficients : dict + The force and moment coefficients, by name. Any you leave out are 0. + + - ``cN``: normal force coefficient (body frame). + - ``cY``: side force coefficient (body frame). + - ``cA``: axial force coefficient (body frame). + - ``cm``: pitch moment coefficient. + - ``cn``: yaw moment coefficient. + - ``cl``: roll moment coefficient. + + The wind-frame ``cL`` (lift), ``cQ`` (side force) and ``cD`` (drag) + can be given instead of ``cN``, ``cY`` and ``cA`` (see + ``force_convention``). The moments are taken about + ``center_of_pressure``. + + Most wind-tunnel reports and aerodynamics programs give the data + against the total angle of attack. Name the variable + ``alpha_total`` and that is all, for example + ``lambda alpha_total, mach: ...``. A normal force ``cN``, a lift + ``cL`` or a pitch moment ``cm`` given that way acts in the plane + that holds the rocket's axis and the wind, and is split between the + pitch and yaw planes for you. Three rules apply to it: + + - It must be zero at zero total angle, where the air has no + direction across the rocket, so a table must start at 0 degrees. + - Leave out ``cY``, ``cQ`` and ``cn``: the part in the other plane + comes from the split. + - If it also depends on ``phi``, it is used as given: write the + split yourself (``cN = f * sin(phi)``, ``cY = -f * cos(phi)``). + + Each coefficient can depend on these variables: + + - ``alpha``, ``beta``: angle of attack and sideslip angle, in + radians, or ``alpha_deg``, ``beta_deg`` in degrees. + - ``alpha_total``, ``phi``: total angle of attack (the angle + between the rocket's axis and the air) and roll angle of the + wind, in radians, or ``alpha_total_deg``, ``phi_deg`` in degrees. + - ``mach``: Mach number. + - ``reynolds``: Reynolds number (see ``reynolds_length``). + - ``pitch_rate``, ``yaw_rate``, ``roll_rate``: angular rates in + reduced form, such as ``q * L_ref / (2 * V)`` for the pitch rate, + not in rad/s. + + Each coefficient can be given as: + + - a number: a constant. + - a function whose arguments are named after the variables it uses, + for example ``lambda alpha, mach: ...``. + - the path to a CSV file whose header names the variables, in any + order, with the coefficient in the last column. + - a list or numpy array of data points, as a pair with the names of + its variables in order, for example + ``([[0, 0.4], [1, 0.6]], ["mach"])``. + - a :class:`Function`, whose input names say which variables it + uses, or as a pair like a list of data points. + - a pair ``(axes, values)`` for values on a regular grid, for + example ``({"alpha": alphas, "mach": machs}, values)`` with + ``values[i, j]`` at ``alphas[i]`` and ``machs[j]``. + - an :class:`AeroCoefficient`, used as it is. center_of_pressure : tuple, list, optional - Application point of the aerodynamic forces and moments. The - center of pressure is defined in the local coordinate system of the - aerodynamic surface. The default value is (0, 0, 0). + Point where the aerodynamic forces are applied and about which the + moment coefficients are taken, as ``(x, y, z)`` in meters. It is + measured from the position the surface is added to the rocket at: + ``z`` runs along the rocket's centerline and is positive toward the + nose, whichever coordinate system orientation the rocket uses. For example ``(0, 0, -0.3)`` applies + the force 0.3 m closer to the tail. The default value is (0, 0, 0). + + The ``z`` component may instead vary with Mach, the way programs + such as OpenRocket or RASAero report the center of pressure: give + it as a function of Mach (``lambda mach: ...``), a one-input + :class:`Function`, or a two-column table ``[[mach, z], ...]``. The + force is then applied at ``z = 0`` and its moment about the given + center of pressure is carried by the moment coefficients + (``cm + cN * z(mach) / L_ref`` and ``cn + cY * z(mach) / L_ref``), + so ``cm`` and ``cn`` are the moments about the center of pressure + itself, usually 0. Such a center of pressure is fixed when the + surface is created. name : str, optional Name of the aerodynamic surface. Default is 'Generic Surface'. reynolds_length : int, float, optional @@ -212,15 +279,16 @@ def __init__( a pre-built ``Function`` already carries. Only affects tabulated sources (constants and callables are evaluated directly). force_convention : str, optional - The frame your force coefficients are given in. ``"wind"`` for the - wind-frame coefficients ``cL`` (lift), ``cQ`` (side) and - ``cD`` (drag); ``"body"`` for the body-frame coefficients ``cN`` - (normal), ``cY`` (side) and ``cA`` (axial), the convention used by - DATCOM, wind tunnels and Barrowman. The moment coefficients - (``cm``, ``cn``, ``cl``) are the same in both. ``None`` (the default) - infers the frame from the coefficient names you pass. Whichever frame - you use, all nine coefficients are available as attributes afterwards - (the other frame is computed on demand). + The frame the force coefficients are given in: + + - ``"body"``: ``cN`` (normal), ``cY`` (side) and ``cA`` (axial), the + convention of wind tunnels and Barrowman. + - ``"wind"``: ``cL`` (lift), ``cQ`` (side) and ``cD`` (drag). + + The moment coefficients are the same in both frames. ``None`` (the + default) picks the frame from the coefficient names. Whichever + frame you use, the body-frame and wind-frame coefficients are all + available as attributes afterwards. active_during : str or callable, optional When this surface produces aerodynamic force during a simulation. Use it to model a surface that is only present in part of the flight, @@ -231,9 +299,25 @@ def __init__( - ``"power_on"``: only while the motor is burning (up to the motor's burn-out time). - ``"power_off"``: only after the motor has burned out. - - a function ``active_during(t, flight)`` returning ``True`` when the - surface is active at time ``t`` (in seconds) of the given + - a function ``active_during(t, flight)`` returning ``True`` when + the surface is active at time ``t`` (in seconds) of the given :class:`Flight`. Use this for any custom window. + + Raises + ------ + TypeError + If ``coefficients`` is not a dict, or ``center_of_pressure`` is not + an ``(x, y, z)`` triple. + ValueError + If a coefficient name, a variable name, ``force_convention`` or + ``active_during`` is not one of the accepted values, if wind-frame + and body-frame force coefficients are mixed without a + ``force_convention``, or if a coefficient given against the total + angle of attack breaks the rules above. + + See Also + -------- + :ref:`genericsurfaces` """ # Externally-supplied axes (e.g. control deflections). Subclasses set @@ -248,14 +332,10 @@ def __init__( self.reynolds_length = ( reference_length if reynolds_length is None else reynolds_length ) - self.center_of_pressure = center_of_pressure - self.cp = center_of_pressure - self.cpx = center_of_pressure[0] - self.cpy = center_of_pressure[1] - self.cpz = center_of_pressure[2] + self._set_center_of_pressure(center_of_pressure) self.name = name - self.active_during = self._validate_active_during(active_during) - self.is_active = self._build_activation_check(self.active_during) + self.active_during = active_during + self.is_active = self._activation_check(active_during) self._rotation_surface_to_body = self._default_surface_rotation() @@ -280,46 +360,74 @@ def _default_surface_rotation(self): """ return Matrix([[1, 0, 0], [0, 1, 0], [0, 0, 1]]) - @staticmethod - def _validate_active_during(active_during): - """Check the ``active_during`` policy and return it unchanged. - - Accepts one of the preset strings ``"always"``, ``"power_on"``, - ``"power_off"`` or a callable ``(t, flight) -> bool``; anything else - raises a ``ValueError`` so a typo is caught at construction rather than - silently keeping the surface active. - """ - if callable(active_during) or active_during in ( - "always", - "power_on", - "power_off", - ): - return active_during - raise ValueError( - "`active_during` must be one of 'always', 'power_on', 'power_off' " - "or a callable(t, flight) -> bool; " - f"got {active_during!r}." + @property + def center_of_pressure(self): + """The point the forces are applied at and the moments are taken about, + as ``(x, y, z)`` in meters in the surface's frame (see + :meth:`__init__`). Setting it moves the surface's force application + point, and a rocket holding the surface updates its aerodynamic + center.""" + return self._center_of_pressure + + @center_of_pressure.setter + def center_of_pressure(self, value): + self._set_center_of_pressure(value) + self._version += 1 + + def _set_center_of_pressure(self, value): + """Store the center of pressure without counting a change: the + constructor and the geometry-defined surfaces, which count their own + changes, use this.""" + try: + x, y, z = value + except (TypeError, ValueError) as exc: + raise TypeError( + "center_of_pressure must be a tuple (x, y, z) in meters, got " + f"{value!r}." + ) from exc + numeric = isinstance(z, (int, float, np.number)) + if hasattr(self, "cm") and (not numeric or self._xcp is not None): + raise ValueError( + "A center of pressure that varies with Mach is folded into the " + "moment coefficients when the surface is built: create a new " + "surface to change it." + ) + xcp = ( + None + if numeric + else AeroCoefficient( + z, + control_variables=getattr(self, "control_variables", ()), + name="center_of_pressure", + single_var="mach", + ) ) + self._center_of_pressure, self._xcp = tuple(value), xcp + self.cpx, self.cpy, self.cpz = x, y, 0.0 if xcp else z + self.cp = (self.cpx, self.cpy, self.cpz) @staticmethod - def _build_activation_check(active_during): - """Resolve an ``active_during`` policy into the ``is_active(t, flight)`` - function the flight integrator calls for every surface each step to skip - the ones that are not currently active. - - Resolving it once here keeps that per-step check free of policy - branching. A custom callable is used unchanged; each preset becomes a - small function of the simulation time ``t`` (in seconds) and the - ``flight`` being run, and ``"always"`` becomes a function that simply - returns ``True``. + def _activation_check(active_during): + """Turn an ``active_during`` policy into the ``is_active(t, flight)`` + function the flight calls every step to skip inactive surfaces. + + A callable is used as it is; ``"always"``, ``"power_on"`` and + ``"power_off"`` become small functions of the time ``t`` (s) and the + ``flight``. Anything else raises a ``ValueError``, so a typo is caught + when the surface is built instead of leaving it active. """ if callable(active_during): return active_during + if active_during == "always": + return lambda t, flight: True if active_during == "power_on": return lambda t, flight: t < flight.rocket.motor.burn_out_time if active_during == "power_off": return lambda t, flight: t >= flight.rocket.motor.burn_out_time - return lambda t, flight: True # "always" + raise ValueError( + "`active_during` must be one of 'always', 'power_on', 'power_off' " + f"or a callable(t, flight) -> bool; got {active_during!r}." + ) @property def force_application_point(self): @@ -330,41 +438,38 @@ def force_application_point(self): """ return Vector([self.cpx, self.cpy, self.cpz]) + @property + def _wind_coefficients(self): + """The wind-frame views ``(cL, cD, cQ)`` of the body-frame ``cN``, ``cY`` + and ``cA``. Built on first use and kept until one of those three is + replaced (for example after a change of geometry).""" + body = (self.cN, self.cY, self.cA) + cached_body, views = getattr(self, "_wind_coefficients_cache", (None, None)) + if cached_body is None or any(a is not b for a, b in zip(body, cached_body)): + views = _body_to_wind_coefficients(*body, self.independent_vars) + self._wind_coefficients_cache = (body, views) + return views + @property def cL(self): """Wind-frame lift coefficient, as a :class:`Function` of the surface's - independent variables. Derived from the canonical body-frame ``cN``, - ``cY`` and ``cA`` by the angle-of-attack/sideslip rotation.""" - return body_to_wind_coefficients( - self.cN, self.cY, self.cA, self.independent_vars - )[0] + independent variables. Derived from the body-frame ``cN``, ``cY`` and + ``cA`` (see :func:`_body_to_wind_coefficients`).""" + return self._wind_coefficients[0] @property def cD(self): """Wind-frame drag coefficient (derived from ``cN``/``cY``/``cA``).""" - return body_to_wind_coefficients( - self.cN, self.cY, self.cA, self.independent_vars - )[1] + return self._wind_coefficients[1] @property def cQ(self): """Wind-frame side-force coefficient (derived from ``cN``/``cY``/``cA``).""" - return body_to_wind_coefficients( - self.cN, self.cY, self.cA, self.independent_vars - )[2] + return self._wind_coefficients[2] def evaluate_coefficients(self): - """Hook for subclasses to (re)populate the aerodynamic coefficient - ``Function``s from their geometry. The base class builds coefficients - directly from the user-provided dictionary, so this is a no-op here. - Subclasses that derive coefficients from geometry (e.g. the Barrowman - surfaces) override this and call it again whenever their geometry - changes. - - Returns - ------- - None - """ + """Build the coefficients from the surface's geometry. Nothing to do + here; the Barrowman surfaces override it.""" def _evaluate_stability_derivatives(self): """Compute the coefficient derivatives used for stability and store them @@ -372,9 +477,12 @@ def _evaluate_stability_derivatives(self): attributes, then build the center-of-pressure accessors from them. A plain generic surface recovers each derivative from its body-frame - force and moment coefficients by numerical differentiation at - ``alpha = beta = 0`` with zero rates. The Barrowman surfaces instead set - these four attributes directly from geometry and only reuse + force and moment coefficients by numerical differentiation about one + point: ``alpha = beta = 0``, Reynolds number 0, every rotation rate 0 + and every control at 0, as a function of Mach. A coefficient that + changes with the Reynolds number is therefore linearized at Reynolds 0 + (the edge of its table, when tabulated). The Barrowman surfaces instead + set these four attributes directly from geometry and only reuse :meth:`_set_stability_accessors` (see the :class:`LinearGenericSurface` override). @@ -382,93 +490,78 @@ def _evaluate_stability_derivatives(self): ------- None """ - self.cN_alpha = AeroCoefficient( - self.cN.slope("alpha", "mach"), - depends_on=("mach",), - control_variables=self.control_variables, - name="cN_alpha", - ) - self.cm_alpha = AeroCoefficient( - self.cm.slope("alpha", "mach"), - depends_on=("mach",), - control_variables=self.control_variables, - name="cm_alpha", - ) - self.cY_beta = AeroCoefficient( - self.cY.slope("beta", "mach"), - depends_on=("mach",), - control_variables=self.control_variables, - name="cY_beta", - ) - self.cn_beta = AeroCoefficient( - self.cn.slope("beta", "mach"), - depends_on=("mach",), - control_variables=self.control_variables, - name="cn_beta", - ) + # A coefficient given against the total angle of attack is split with + # the sign of the angle, so its slope is taken from zero toward positive + # angles (stencil [0, 2 step]) rather than across zero. A moment may + # carry such a force (see _carry_moments_to_center_of_pressure), so the + # whole surface is read that way. + step = self._SLOPE_STEP + one_sided = bool(self._wind_plane_names) + + def slope(coefficient, angle): + at = {angle: step} if one_sided else None + return coefficient.slope(angle, "mach", at=at, dx=step) + + for name, angle in ( + ("cN", "alpha"), + ("cm", "alpha"), + ("cY", "beta"), + ("cn", "beta"), + ): + slope_name = f"{name}_{angle}" + setattr( + self, + slope_name, + AeroCoefficient( + slope(getattr(self, name), angle), + depends_on=("mach",), + control_variables=self.control_variables, + name=slope_name, + ), + ) self._set_stability_accessors() def _set_stability_accessors(self): - """Build the pitch- and yaw-plane center-of-pressure accessors from the + """Build the pitch- and yaw-plane aerodynamic-center accessors + (``aerodynamic_center``, ``aerodynamic_center_yaw``) from the stored coefficient derivatives (``cN_alpha``/``cm_alpha`` and ``cY_beta``/``cn_beta``), each evaluated at ``alpha = beta = 0`` with zero rates. Each accessor is a Mach-only :class:`Function` giving the surface's - center of pressure along the body z-axis. It combines the surface's - local application point with the offset implied by its moment - coefficient (``cp = application point - (moment slope / force slope) * - L_ref``). When a surface produces no force at some Mach the center of - pressure is undefined, so it falls back to the geometric application - point and drops out of the force-weighted average. + aerodynamic center along the body z-axis (positive toward the nose), + measured from the point the surface is positioned at. It combines the + surface's application point, mapped into the body frame, with the offset + implied by its moment coefficient (``application point + (moment slope + / force slope) * L_ref``). When a surface produces no force at some + Mach the aerodynamic center is undefined, so it falls back to the + application point and drops out of the force-weighted average. Returns ------- None """ - reference_length = self.reference_length - local_cpz = self.force_application_point[2] - def _cp_z(force_coeff, moment_coeff): - def cp_z(mach): + def _center_z(force_coeff, moment_coeff): + def center_z(mach): + # Same mapping the rocket uses to place the force in flight (see + # Rocket.evaluate_surfaces_cp_to_cdm), read on each call so it + # follows a change of geometry. + application_z = ( + self._rotation_surface_to_body @ self.force_application_point + )[2] slope = force_coeff.get_value_opt(0.0, 0.0, mach, 0.0, 0.0, 0.0, 0.0) if slope == 0: - return local_cpz + return application_z moment = moment_coeff.get_value_opt(0.0, 0.0, mach, 0.0, 0.0, 0.0, 0.0) - return local_cpz - moment / slope * reference_length + return application_z + moment / slope * self.reference_length - return Function(cp_z, "Mach", "Center of pressure to local origin (m)") - - self.center_of_pressure_z = _cp_z(self.cN_alpha, self.cm_alpha) - self.center_of_pressure_z_yaw = _cp_z(self.cY_beta, self.cn_beta) - - @staticmethod - def _coefficient_option(option, coeff_name): - """Resolve a per-coefficient interpolation/extrapolation setting. - - ``option`` may be a single value applied to every coefficient, a dict - mapping coefficient names to values (coefficients absent from the dict - fall back to the ``AeroCoefficient`` default), or ``None``. - - Parameters - ---------- - option : str, dict, or None - The interpolation/extrapolation argument passed to ``__init__``. - coeff_name : str - Name of the coefficient being built (e.g. ``"cD"``, ``"cm_alpha"``). - - Returns - ------- - str or None - The value to forward to :class:`AeroCoefficient` for this coefficient. - """ - if isinstance(option, dict): - return option.get(coeff_name) - return option + return Function( + center_z, "Mach", "Aerodynamic center to surface position (m)" + ) - # Force-coefficient names in each frame. Moments (cm/cn/cl) are frame-shared. - _WIND_FORCE_NAMES = ("cL", "cQ", "cD") - _BODY_FORCE_NAMES = ("cN", "cY", "cA") + self.aerodynamic_center = _center_z(self.cN_alpha, self.cm_alpha) + self.aerodynamic_center_yaw = _center_z(self.cY_beta, self.cn_beta) def _force_frames_present(self, coefficients): """Report which force frames the input coefficient names belong to, as @@ -499,38 +592,208 @@ def _resolve_force_convention(self, coefficients, force_convention): "coefficients; pass force_convention='wind' or 'body'." ) return "wind" if has_wind else "body" - if force_convention not in ("wind", "body"): + if force_convention not in self._FORCE_CONVENTIONS: raise ValueError( - f"force_convention must be 'wind' or 'body', got {force_convention!r}." + f"force_convention must be one of {self._FORCE_CONVENTIONS}, " + f"got {force_convention!r}." ) return force_convention + def _as_coefficient(self, source, name, single_var=None): + """Wrap a coefficient input as an :class:`AeroCoefficient` over this + surface's variables, with the interpolation and extrapolation the user + asked for under that coefficient's name. ``single_var`` names the + variable of a one-column table or headerless file that carries no name. + """ + # A setting is either one value for every coefficient or a dict by name + extrapolation, interpolation = ( + option.get(name) if isinstance(option, dict) else option + for option in (self._extrapolation, self._interpolation) + ) + coefficient = AeroCoefficient( + source, + control_variables=self.control_variables, + name=name, + extrapolation=extrapolation, + interpolation=interpolation, + single_var=single_var, + ) + return coefficient + + @staticmethod + def _is_in_wind_plane(coefficient): + """Whether a coefficient is given against the total angle of attack + alone, without the roll angle of the wind. A normal force, a lift or a + pitch moment given that way acts in the plane that holds the rocket's + axis and the wind. Given with ``phi`` too, the source says itself how + the coefficient turns with the wind, and it is used as it is.""" + angles = set(coefficient.source_angles) + return "alpha_total" in angles and "phi" not in angles + + def _split_along_crossflow(self, coefficient, names): + """The pitch- and yaw-plane parts of a coefficient taken in the plane of + the wind (see :func:`_total_angle_to_body_coefficients`), over alpha, beta + and the variables the coefficient uses.""" + used = [ + var + for var in self.independent_vars + if var in ("alpha", "beta") or var in coefficient.depends_on + ] + return _total_angle_to_body_coefficients(coefficient, used, names) + + def _wind_plane_input_to_body(self, coefficients): + """Convert the coefficients given in the plane of the wind (see + :meth:`_is_in_wind_plane`) into body-frame ones, and leave the others + as they are. + + ``cN`` and ``cm`` are split between the pitch and yaw planes (see + :func:`_total_angle_to_body_coefficients`). ``cL`` is first turned, with + ``cD``, into the normal force in the plane of the wind and the axial + force. The names of the split coefficients are kept in + ``_wind_plane_names``. + """ + self._wind_plane_names = frozenset() + in_plane = { + name + for name, coefficient in coefficients.items() + if name in self._DIRECTIONAL_COEFFICIENTS + and self._is_in_wind_plane(coefficient) + } + if not in_plane: + return coefficients + for name in sorted(in_plane): + if name not in ("cN", "cm", "cL"): + raise ValueError(self._not_in_wind_plane_message(name)) + self._check_against_total_angle(coefficients[name], name) + self._check_zero_at_zero_total_angle(coefficients[name], name) + body = dict(coefficients) + split = set() + + def refuse_partner(name, partner): + given = coefficients.get(partner) + if given is not None and not given.is_zero: + raise ValueError( + f"{partner} cannot be given together with a {name} against " + f"the total angle of attack: {name} then acts in the plane " + "of the wind and already provides the part in the other " + f"plane. Leave {partner} out." + ) + body.pop(partner, None) + + for name, partner in (("cN", "cY"), ("cm", "cn")): + if name in in_plane: + refuse_partner(name, partner) + names = (name, partner) + parts = self._split_along_crossflow(coefficients[name], names) + body.update(zip(names, parts)) + split.update(names) + if "cL" in in_plane: + refuse_partner("cL", "cQ") + lift = body.pop("cL") + drag = self._as_coefficient(body.pop("cD", 0), "cD") + used = [ + var + for var in self.independent_vars + if var in ("alpha", "beta") + or var in lift.depends_on + or var in drag.depends_on + ] + names = ("cN", "cY", "cA") + body.update( + zip(names, _wind_plane_lift_to_body_coefficients(lift, drag, used)) + ) + split.update(("cN", "cY")) + self._wind_plane_names = frozenset(split) + return body + + @staticmethod + def _not_in_wind_plane_message(name): + """Why ``name`` cannot be given against the total angle of attack + alone.""" + force = {"cY": "cN", "cn": "cm", "cQ": "cL"}[name] + return ( + f"{name} is given against the total angle of attack alone. A " + "coefficient given that way acts in the plane of the wind, " + f"where there is no side force or yaw moment: give {force} " + "instead, which is split between the pitch and yaw planes for " + f"you. If {name} does depend on the direction of the wind, give " + 'it against "alpha_total" and "phi", or against "alpha" and ' + '"beta".' + ) + + def _check_against_total_angle(self, coefficient, name): + """Reject a coefficient given against the total angle of attack that + also reads the signed partial angles ``alpha`` or ``beta``. + + The total angle sets the size of the force and the split along the + crossflow sets its direction, with the sign of the angle. A coefficient + that already carries that sign would get it twice. + """ + signed = sorted(set(coefficient.source_angles) & {"alpha", "beta"}) + if signed: + raise ValueError( + f"{name} is given against alpha_total together with " + f"{' and '.join(signed)}. Against the total angle of attack it " + "acts in the plane of the wind, and its direction comes from " + "the split between the pitch and yaw planes, so a signed angle " + "would put the sign on twice. Give it against alpha_total alone " + "(with phi if it depends on the roll angle of the wind), or " + "against alpha and beta." + ) + + def _check_zero_at_zero_total_angle(self, coefficient, name): + """Reject a coefficient in the plane of the wind that is not zero at + zero total angle of attack. + + There the crossflow has no direction, so a normal force or a pitch + moment has nowhere to point: the coefficient must vanish. A table that + starts above zero degrees fails too, since it holds its first value all + the way down to zero. Left in, the body-frame force would flip sign + every time the angle crosses zero, and the stability slope, taken over a + tiny step from zero, would come out as that jump divided by the step + (1e5 for a table starting at 2 degrees). + """ + args = [0.0] * len(self.independent_vars) + machs = self._ZERO_ANGLE_MACHS if "mach" in coefficient.depends_on else (0.0,) + for mach in machs: + args[2] = mach + value = coefficient(*args) + if abs(value) > 1e-9: + raise ValueError( + f"{name} is {value:.4g} at zero total angle of attack (Mach " + f"{mach:g}) but must be zero there: with no crossflow the " + "force has no direction to point in. Give 0 at " + "alpha_total = 0; a table must start at 0 degrees, since its " + "first value is held down to zero otherwise." + ) + def _wind_input_to_body(self, coefficients): """Convert a wind-frame force-coefficient input (``cL``/``cQ``/``cD``) into the canonical body-frame coefficients (``cN``/``cY``/``cA``), leaving the moment coefficients untouched.""" - wind = {} - passthrough = {} - for name, value in coefficients.items(): - if name in self._WIND_FORCE_NAMES: - wind[name] = value - else: - passthrough[name] = value - - def as_coefficient(source, name): - return AeroCoefficient( - source, - control_variables=self.control_variables, - name=name, - ) - - c_normal, c_yaw, c_axial = wind_to_body_coefficients( - as_coefficient(wind.get("cL", 0), "cL"), - as_coefficient(wind.get("cD", 0), "cD"), - as_coefficient(wind.get("cQ", 0), "cQ"), - self.independent_vars, - ) - return {"cN": c_normal, "cY": c_yaw, "cA": c_axial, **passthrough} + wind = { + name: self._as_coefficient(coefficients.get(name, 0), name) + for name in self._WIND_FORCE_NAMES + } + passthrough = { + name: value + for name, value in coefficients.items() + if name not in self._WIND_FORCE_NAMES + } + if all(coefficient.is_zero for coefficient in wind.values()): + return passthrough + + # The conversion reads alpha and beta on top of what the inputs use. + # Keeping only those variables stops a constant drag from looking like + # it depends on the Reynolds number or the rotation rates. + used = [ + var + for var in self.independent_vars + if var in ("alpha", "beta") + or any(var in coefficient.depends_on for coefficient in wind.values()) + ] + body = _wind_to_body_coefficients(wind["cL"], wind["cD"], wind["cQ"], used) + return {**passthrough, **dict(zip(self._BODY_FORCE_NAMES, body))} def _build_coefficients( self, coefficients, interpolation, extrapolation, force_convention @@ -553,104 +816,88 @@ def _build_coefficients( The frame the input force coefficients are given in, or ``None`` to infer it from the coefficient names. """ + self._interpolation = interpolation + self._extrapolation = extrapolation + if not isinstance(coefficients, dict): + raise TypeError( + "coefficients must be a dict from coefficient name to value, for " + f'example {{"cN": 2.0, "cA": "cA.csv"}}; got ' + f"{type(coefficients).__name__}. For one file holding several " + f"coefficients use {type(self).__name__}.from_csv." + ) default_coefficients = self._get_default_coefficients() self.force_convention = self._resolve_force_convention( coefficients, force_convention ) - # Wind-frame force input (cL/cQ/cD) is converted once to the canonical - # body-frame coefficients before validation. Each surface supplies the - # conversion appropriate to its coefficients: the generic surface rotates - # the full force coefficients, while the linear model recombines the - # coefficient derivatives (see LinearGenericSurface._wind_input_to_body). - # A non-dict input falls through to _check_coefficients, which rejects it. - if self.force_convention == "wind" and isinstance(coefficients, dict): + # Kept as given, so saving the surface needs no pickling + self._input_coefficients = { + name: self._as_coefficient(value, name) + for name, value in coefficients.items() + } + # Input in the plane of the wind, then in the wind frame, becomes + # body-frame coefficients + coefficients = self._wind_plane_input_to_body(self._input_coefficients) + if self.force_convention == "wind": coefficients = self._wind_input_to_body(coefficients) + coefficients = self._complete_body_coefficients(coefficients) self._check_coefficients(coefficients, default_coefficients) - coefficients = self._complete_coefficients(coefficients, default_coefficients) + coefficients = {**default_coefficients, **coefficients} - # ``_needs_reynolds`` lets the flight loop skip the per-step atmosphere - # lookups when no coefficient uses the Reynolds number. Only these - # primary coefficients are checked: they are what the surface evaluates, - # and the linear model's combined coefficients are linear combinations of - # them, so a Reynolds dependence always shows up here. + # Lets the flight skip the Reynolds number when no coefficient uses it self._needs_reynolds = False for coeff, coeff_value in coefficients.items(): - value = AeroCoefficient( - coeff_value, - control_variables=self.control_variables, - name=coeff, - extrapolation=self._coefficient_option(extrapolation, coeff), - interpolation=self._coefficient_option(interpolation, coeff), - ) + value = self._as_coefficient(coeff_value, coeff) setattr(self, coeff, value) if "reynolds" in value.depends_on: self._needs_reynolds = True + if self._xcp is not None: + self._carry_moments_to_center_of_pressure() - def _get_default_coefficients(self): - """Returns default coefficients + def _complete_body_coefficients(self, coefficients): + """Last step before the body-frame coefficients are stored. Nothing to + do here; the linear surface fills in its yaw plane.""" + return coefficients - Returns - ------- - default_coefficients: dict - Dictionary whose keys are the coefficients names and keys - are the default values. - """ - default_coefficients = { - "cN": 0, - "cY": 0, - "cA": 0, - "cm": 0, - "cn": 0, - "cl": 0, - } - return default_coefficients + def _carry_moments_to_center_of_pressure(self): + """With a center of pressure that varies with Mach the force is applied + at the surface's ``z = 0``, so its moment about the center of pressure + is added to the moment coefficients: ``cm + cN * z(mach) / L_ref`` and + ``cn + cY * z(mach) / L_ref`` (each derivative alike for the linear + model, ``cm_alpha + cN_alpha * z / L_ref`` and so on).""" + length = self.reference_length + + def carried(moment, force): + used = [ + var + for var in self.independent_vars + if var == "mach" or var in moment.depends_on or var in force.depends_on + ] + read_moment, read_force, read_z = ( + c.evaluator(used) for c in (moment, force, self._xcp) + ) - def _complete_coefficients(self, input_coefficients, default_coefficients): - """Creates a copy of the input coefficients dict and fill it with missing - keys with default values + def total(*args): + return read_moment(*args) + read_force(*args) * read_z(*args) / length - Parameters - ---------- - input_coefficients : str, dict - Coefficients dictionary passed by the user. If the user only specifies some - of the coefficients, the remaining are completed with class default - values - default_coefficients : dict - Default coefficients of the class + return self._as_coefficient( + _as_function(total, used, moment.name), moment.name + ) - Returns - ------- - coefficients : dict - Coefficients dictionary used to setup coefficient attributes - """ - # Shallow copy: only missing keys are added, so the user's dict is left - # intact. The values are not mutated here (each is wrapped in an - # AeroCoefficient, which copies it when it needs its own settings), so - # there is no need to deep-copy potentially large tabulated coefficients. - coefficients = dict(input_coefficients) - for coeff, value in default_coefficients.items(): - if coeff not in coefficients: - coefficients[coeff] = value + for name in self._get_default_coefficients(): + force_name = {"cm": "cN", "cn": "cY"}.get(name[:2]) + if force_name is not None: + force = getattr(self, force_name + name[2:]) + if not force.is_zero: + setattr(self, name, carried(getattr(self, name), force)) - return coefficients + @classmethod + def _get_default_coefficients(cls): + """The coefficients the surface holds, each with its default value.""" + return {"cN": 0, "cY": 0, "cA": 0, "cm": 0, "cn": 0, "cl": 0} def _check_coefficients(self, input_coefficients, default_coefficients): - """Check if input coefficients have only valid keys - - Parameters - ---------- - input_coefficients : str, dict - Coefficients dictionary passed by the user. If the user only specifies some - of the coefficients, the remaining are completed with class default - values - default_coefficients : dict - Default coefficients of the class - - Raises - ------ - ValueError - Raises a value error if the input coefficient has an invalid key - """ + """Raise a ``ValueError`` for a coefficient name the surface does not + have.""" invalid_keys = set(input_coefficients) - set(default_coefficients) if invalid_keys: raise ValueError( @@ -658,96 +905,10 @@ def _check_coefficients(self, input_coefficients, default_coefficients): "Check the documentation for valid names." ) - def _compute_from_coefficients( - self, - rho, - stream_speed, - alpha, - beta, - mach, - reynolds, - pitch_rate, - yaw_rate, - roll_rate, - ): - """Compute the aerodynamic forces and moments from the aerodynamic - coefficients. - - Parameters - ---------- - rho : float - Air density. - stream_speed : float - Magnitude of the airflow speed. - alpha : float - Angle of attack in radians. - beta : float - Sideslip angle in radians. - mach : float - Mach number. - reynolds : float - Reynolds number. - pitch_rate : float - Non-dimensional (reduced) pitch rate, ``q * L_ref / (2 * V)``. - yaw_rate : float - Non-dimensional (reduced) yaw rate, ``r * L_ref / (2 * V)``. - roll_rate : float - Non-dimensional (reduced) roll rate, ``p * L_ref / (2 * V)``. - - Returns - ------- - tuple of float - The body-frame force components ``(R1, R2, R3)`` and the moments - ``(pitch, yaw, roll)``. - """ - # Precompute common values - dyn_pressure_area = 0.5 * rho * stream_speed**2 * self.reference_area - dyn_pressure_area_length = dyn_pressure_area * self.reference_length - - # Coefficient arguments (base 7 vars, plus any extra axes appended by - # subclasses such as control deflections). - args = self._coefficient_arguments( - alpha, - beta, - mach, - reynolds, - pitch_rate, - yaw_rate, - roll_rate, - ) - - # Body-frame force components straight from the body-frame coefficients - # (normal cN, side cY, axial cA); no wind-to-body rotation needed. - normal = dyn_pressure_area * self.cN(*args) - yaw_side = dyn_pressure_area * self.cY(*args) - axial = dyn_pressure_area * self.cA(*args) - R1 = yaw_side - R2 = -normal - R3 = -axial - - # Compute aerodynamic moments - pitch = dyn_pressure_area_length * self.cm(*args) - yaw = dyn_pressure_area_length * self.cn(*args) - roll = dyn_pressure_area_length * self.cl(*args) - - return R1, R2, R3, pitch, yaw, roll - - def _coefficient_arguments( - self, - alpha, - beta, - mach, - reynolds, - pitch_rate, - yaw_rate, - roll_rate, - ): - """Returns the argument tuple passed to every coefficient ``Function``, - in ``self.independent_vars`` order. The base class provides the seven - standard inputs. Subclasses (e.g. :class:`ControllableGenericSurface`) - override this to append further axes such as control deflections. - """ - return (alpha, beta, mach, reynolds, pitch_rate, yaw_rate, roll_rate) + def _coefficient_arguments(self, *state): + """The arguments every coefficient is called with: the seven flow + variables in ``state``, plus any a subclass adds.""" + return state def compute_forces_and_moments( self, @@ -792,13 +953,12 @@ def compute_forces_and_moments( Returns ------- tuple of float - The aerodynamic forces (lift, side_force, drag) and moments - (pitch, yaw, roll) in the body frame. + The aerodynamic force components ``(R1, R2, R3)`` along the body + x, y and z axes and the moments ``(M1, M2, M3)`` about them, taken + about the rocket's center of dry mass. """ # Reynolds number at the surface altitude. Computed here (rather than in - # the flight loop) since it is only needed by generic surfaces, and only - # when a coefficient actually depends on it -- otherwise the two - # atmosphere lookups are skipped for every surface, every step. + # the flight loop) since it is only needed by generic surfaces if self._needs_reynolds: comp_density = density.get_value_opt(z) comp_dynamic_viscosity = dynamic_viscosity.get_value_opt(z) @@ -826,38 +986,196 @@ def compute_forces_and_moments( self.reference_length / (2 * stream_speed) if stream_speed > 0 else 0.0 ) - # Body-frame force components and moments straight from the body-frame - # coefficients (no wind-to-body rotation: the coefficients already live - # in the body frame). ``alpha``/``beta`` are still passed to the - # coefficients, they just no longer rotate the force. - R1, R2, R3, pitch, yaw, roll = self._compute_from_coefficients( - rho, - stream_speed, + args = self._coefficient_arguments( alpha, beta, stream_mach, reynolds, - omega[0] * reduced_rate_factor, # q* reduced pitch rate - omega[1] * reduced_rate_factor, # r* reduced yaw rate - omega[2] * reduced_rate_factor, # p* reduced roll rate + omega[0] * reduced_rate_factor, + omega[1] * reduced_rate_factor, + omega[2] * reduced_rate_factor, ) + force = 0.5 * rho * stream_speed**2 * self.reference_area + moment = force * self.reference_length + R1 = force * self.cY.get_value_opt(*args) + R2 = -force * self.cN.get_value_opt(*args) + R3 = -force * self.cA.get_value_opt(*args) + pitch = moment * self.cm.get_value_opt(*args) + yaw = moment * self.cn.get_value_opt(*args) + roll = moment * self.cl.get_value_opt(*args) # Dislocation of the aerodynamic application point to CDM M1, M2, M3 = Vector([pitch, yaw, roll]) + (cp ^ Vector([R1, R2, R3])) return R1, R2, R3, M1, M2, M3 - def to_dict(self, include_outputs=False, **kwargs): # pylint: disable=unused-argument - # The stored coefficients are always the canonical body-frame set (the - # names from ``_get_default_coefficients``: cN/cY/cA/... for a generic - # surface, the derivative set for the linear model), so they are saved - # with ``force_convention="body"`` and rebuilt directly on load. + @classmethod + def _input_coefficient_names(cls): + """Every name a coefficient can be given under: the body-frame names and + the wind-frame ones.""" + return set(cls._get_default_coefficients()) | set(cls._WIND_FORCE_NAMES) + + @classmethod + def _input_variable_names(cls, **kwargs): # pylint: disable=unused-argument + """Every name a variable can be given under, for a surface built with + the constructor arguments ``kwargs``.""" + return [*build_independent_vars(), *SOURCE_ONLY_NAMES] + + @classmethod + def from_csv( + cls, file_path, reference_area, reference_length, columns=None, **kwargs + ): + """Create the surface from one table file holding several coefficients. + + The file has one column per variable the coefficients are tabulated + against and one column per coefficient, in any order, for example:: + + alpha_deg, mach, cN, cA, cm + -2, 0.3, -0.085, 0.42, 0.260 + 0, 0.3, 0.000, 0.42, 0.000 + 2, 0.3, 0.085, 0.42, -0.260 + + Every coefficient is read over all the variable columns. When the rows + cover every combination of the variables' values, the table is + interpolated as a regular grid. + + Parameters + ---------- + file_path : str + Path to the ``.csv`` file. Its first line names the columns. + reference_area : int, float + Reference area of the surface, in squared meters. + reference_length : int, float + Reference length of the surface, in meters. + columns : dict, optional + Translation from the file's column names to RocketPy's, for files + written by other programs, for example ``{"Mach": "mach", "Alpha": + "alpha_deg", "CN": "cN", "CA Power-Off": "cA"}``. When given, only + the columns it lists are read and the others are ignored. When left + as ``None`` (the default) every column of the file must carry a + RocketPy name. + + The variable names are ``alpha`` and ``beta`` (radians) or + ``alpha_deg`` and ``beta_deg`` (degrees), ``alpha_total`` and + ``phi`` (total angle of attack and roll angle of the wind, also with + ``_deg``), ``mach``, ``reynolds``, + ``pitch_rate``, ``yaw_rate`` and ``roll_rate``, plus the control + names of a controllable surface. The coefficient names are those of + the class, such as ``cN``, ``cY``, ``cA`` (or ``cL``, ``cQ``, + ``cD``), ``cm``, ``cn`` and ``cl`` for a :class:`GenericSurface`. + Names are case sensitive: ``cN`` is the normal force and ``cn`` the + yaw moment. + **kwargs + Any other argument of the class, such as ``center_of_pressure``, + ``name``, ``interpolation`` or ``active_during``. + + Returns + ------- + GenericSurface + The surface, of the class this method was called on. + + Notes + ----- + RocketPy's ``alpha`` is measured in one plane and takes both signs. For + a table against the *total* angle of attack, which only has positive + angles, name the column ``alpha_total`` (or ``alpha_total_deg``). A + warning is raised when a table read + as ``alpha`` looks like it is against the total angle. + """ + with open(file_path, mode="r", encoding="utf-8") as file: + header = [name.strip() for name in next(csv.reader(file))] + data = np.atleast_2d(np.loadtxt(file_path, delimiter=",", skiprows=1)) + if columns is None: + names = header + else: + missing = [name for name in columns if name not in header] + if missing: + raise ValueError( + f"Column(s) {missing} not found in {file_path}. The file " + f"has the columns {header}." + ) + names = [columns.get(name) for name in header] + + variable_names = cls._input_variable_names(**kwargs) + coefficient_names = cls._input_coefficient_names() + unknown = [ + name + for name in names + if name is not None + and name not in variable_names + and name not in coefficient_names + ] + if unknown: + raise ValueError( + f"Column name(s) {unknown} of {file_path} are neither a variable " + f"({', '.join(variable_names)}) nor a coefficient of " + f"{cls.__name__}. Use the `columns` argument to translate the " + "file's column names, which also lets the other columns be " + "ignored." + ) + repeated = {name for name in names if name and names.count(name) > 1} + if repeated: + raise ValueError(f"Column name(s) {sorted(repeated)} appear twice.") + + variables = [i for i, name in enumerate(names) if name in variable_names] + values = [i for i, name in enumerate(names) if name in coefficient_names] + if not variables or not values: + raise ValueError( + f"{file_path} needs at least one variable column and one " + "coefficient column." + ) coefficients = { - name: getattr(self, name) for name in self._get_default_coefficients() + names[i]: (data[:, [*variables, i]], [names[j] for j in variables]) + for i in values } - # A preset ``active_during`` is stored as is; a custom (t, flight) -> bool - # function is pickled to text when allowed, otherwise dropped to "always" - # (a function cannot be restored without pickling). + return cls(reference_area, reference_length, coefficients, **kwargs) + + @classmethod + def _arguments_from_dict(cls, data): + """The constructor arguments stored by :meth:`to_dict`. Subclasses extend + it with their own arguments.""" + # A pickled function is restored, or "always" if that is not possible + active_during = data.get("active_during", "always") + if active_during not in ("always", "power_on", "power_off"): + try: + active_during = from_hex_decode(active_during) + except (TypeError, ValueError): + active_during = "always" + arguments = { + "reference_area": data["reference_area"], + "reference_length": data["reference_length"], + "coefficients": data["coefficients"], + "center_of_pressure": data.get("center_of_pressure", (0, 0, 0)), + "name": data.get("name", "Generic Surface"), + "reynolds_length": data.get("reynolds_length"), + "force_convention": data.get("force_convention", "body"), + "active_during": active_during, + } + return arguments + + def to_dict(self, include_outputs=False, **kwargs): # pylint: disable=unused-argument + """Return the surface as a dictionary, to save it and rebuild it later. + + The coefficients are saved as they were given, so a table stays a table + and loading the surface converts them again exactly as the constructor + did. + + Parameters + ---------- + include_outputs : bool, optional + Not used: a surface has no results to save. It is accepted so that + every RocketPy object is saved the same way. Default False. + **kwargs + ``allow_pickle`` (bool, default True): whether a custom + ``active_during`` function may be saved as pickled text. When it is + not allowed, the surface is saved as active ``"always"``. + + Returns + ------- + dict + The arguments needed to rebuild the surface with :meth:`from_dict`. + """ + # A function can only be saved by pickling it active_during = self.active_during if callable(active_during): active_during = ( @@ -865,38 +1183,34 @@ def to_dict(self, include_outputs=False, **kwargs): # pylint: disable=unused-ar if kwargs.get("allow_pickle", True) else "always" ) + x, y, z = self.center_of_pressure return { "reference_area": self.reference_area, "reference_length": self.reference_length, "reynolds_length": self.reynolds_length, - "coefficients": coefficients, - "center_of_pressure": self.center_of_pressure, + "coefficients": self._input_coefficients, + # The axial position as given: a number, or a function of Mach + "center_of_pressure": (x, y, self._xcp or z), "name": self.name, - "force_convention": "body", + "force_convention": self.force_convention, "active_during": active_during, } @classmethod def from_dict(cls, data): - # A preset ``active_during`` is used as is; anything else is unpickled - # back into the original function (falling back to "always" if it cannot - # be restored). - active_during = data.get("active_during", "always") - if active_during not in ("always", "power_on", "power_off"): - try: - active_during = from_hex_decode(active_during) - except (TypeError, ValueError): - active_during = "always" - return cls( - reference_area=data["reference_area"], - reference_length=data["reference_length"], - coefficients=data["coefficients"], - center_of_pressure=data.get("center_of_pressure", (0, 0, 0)), - name=data.get("name", "Generic Surface"), - reynolds_length=data.get("reynolds_length"), - force_convention=data.get("force_convention", "body"), - active_during=active_during, - ) + """Rebuild a surface saved with :meth:`to_dict`. + + Parameters + ---------- + data : dict + The dictionary returned by :meth:`to_dict`. + + Returns + ------- + GenericSurface + The surface, of the class this method is called on. + """ + return cls(**cls._arguments_from_dict(data)) def info(self): """Prints a summary of the surface's geometry and aerodynamic diff --git a/rocketpy/rocket/aero_surface/linear_generic_surface.py b/rocketpy/rocket/aero_surface/linear_generic_surface.py index 3b313bfef..789bbdff6 100644 --- a/rocketpy/rocket/aero_surface/linear_generic_surface.py +++ b/rocketpy/rocket/aero_surface/linear_generic_surface.py @@ -1,20 +1,99 @@ -import inspect - -from rocketpy.mathutils import Function from rocketpy.plots.aero_surface_plots import _LinearGenericSurfacePlots from rocketpy.prints.aero_surface_prints import _LinearGenericSurfacePrints -from rocketpy.rocket.aero_surface.aero_coefficient import AeroCoefficient +from rocketpy.rocket.aero_surface._helpers import _as_function from rocketpy.rocket.aero_surface.generic_surface import GenericSurface -# TODO: review note: ControllableGenericSurface should also be able to be modelled -# based on LinearGenericSurface.... class LinearGenericSurface(GenericSurface): - """An aerodynamic surface whose forces and moments vary linearly with the - flow angles and the rotation rates. Instead of full coefficient tables, you - give the coefficient *derivatives* (slopes) -- for example how much the normal - force changes per radian of angle of attack -- and the surface adds them up - linearly.""" + """An aerodynamic surface whose forces and moments vary linearly. + + They vary linearly with the flow angles and the rotation rates. Instead of + full coefficient tables, you give the coefficient *derivatives* (slopes), + for example how much the normal force changes per radian of angle of attack, + and the surface adds them up. + + Attributes + ---------- + LinearGenericSurface.cN_0 : AeroCoefficient + The 36 coefficient derivatives, ``cN_0``, ``cN_alpha``, ..., ``cl_r``, + named ``_`` as given in ``coefficients``. + LinearGenericSurface.cNf : Function + Forcing part of ``cN``, and likewise ``cYf``, ``cAf``, ``cmf``, ``cnf`` + and ``clf``: for example ``cmf = cm_0 + cm_alpha * alpha + cm_beta * beta``. + LinearGenericSurface.cNd : Function + Damping part of ``cN``, and likewise ``cYd``, ``cAd``, ``cmd``, ``cnd`` + and ``cld``: for example + ``cmd = cm_p * roll_rate + cm_q * pitch_rate + cm_r * yaw_rate``. + LinearGenericSurface.cN : Function + Normal force coefficient, the sum of its forcing and damping parts. + LinearGenericSurface.cY : Function + Side force coefficient, the sum of its forcing and damping parts. + LinearGenericSurface.cA : Function + Axial force coefficient, the sum of its forcing and damping parts. + LinearGenericSurface.cm : Function + Pitching moment coefficient, the sum of its forcing and damping parts. + LinearGenericSurface.cn : Function + Yawing moment coefficient, the sum of its forcing and damping parts. + LinearGenericSurface.cl : Function + Roll moment coefficient, the sum of its forcing and damping parts. + LinearGenericSurface.cL : Function + Lift coefficient, the force perpendicular to the airflow. + LinearGenericSurface.cD : Function + Drag coefficient, the force along the airflow. + LinearGenericSurface.cQ : Function + Crosswind coefficient, the side force relative to the airflow. + LinearGenericSurface.aerodynamic_center : Function + Pitch-plane aerodynamic center of the surface along the rocket's axis, + in meters from the surface's position (positive toward the nose), as a + function of Mach. It is the point where the change of the normal force + acts when the angle of attack changes a little from zero. It is the + same point as ``center_of_pressure`` when no moment coefficient is + given. + LinearGenericSurface.aerodynamic_center_yaw : Function + Yaw-plane aerodynamic center of the surface along the rocket's axis, in + meters from the surface's position (positive toward the nose), as a + function of Mach: the same for the side force and the sideslip angle. + LinearGenericSurface.reference_area : float + Reference area, in square meters. + LinearGenericSurface.reference_length : float + Reference length, in meters. + LinearGenericSurface.reynolds_length : float + Length scale of the Reynolds number, in meters. + LinearGenericSurface.name : str + Name of the surface. + LinearGenericSurface.center_of_pressure : tuple + Point where the forces are applied, ``(x, y, z)`` in meters, as given. + LinearGenericSurface.cp : tuple + The same point as ``(cpx, cpy, cpz)``. ``cpz`` is 0 when the center of + pressure varies with Mach, since the force is then applied at ``z = 0``. + LinearGenericSurface.cpx : float + x coordinate of ``cp``, in meters. + LinearGenericSurface.cpy : float + y coordinate of ``cp``, in meters. + LinearGenericSurface.cpz : float + z coordinate of ``cp``, in meters. + LinearGenericSurface.active_during : str or callable + When the surface produces force, as given. + LinearGenericSurface.is_active : callable + ``is_active(t, flight)``: whether the surface produces force at time + ``t`` of the flight. + LinearGenericSurface.force_convention : str + Frame the force coefficients were given in: ``"body"`` or ``"wind"``. + LinearGenericSurface.independent_vars : list of str + The variables every coefficient is called with, in order. + LinearGenericSurface.prints : _LinearGenericSurfacePrints + The prints of the surface. Use help(LinearGenericSurface.prints) to know more. + LinearGenericSurface.plots : _LinearGenericSurfacePlots + The plots of the surface. Use help(LinearGenericSurface.plots) to know more. + """ + + # The six coefficients and their terms: derivative suffix -> the variable it + # multiplies (``None`` for the constant term) + _COEFFICIENTS = ("cN", "cY", "cA", "cm", "cn", "cl") + _FORCING_TERMS = {"0": None, "alpha": "alpha", "beta": "beta"} + _DAMPING_TERMS = {"p": "roll_rate", "q": "pitch_rate", "r": "yaw_rate"} + # Force prefix in the body frame -> in the wind frame + _BODY_TO_WIND_PREFIX = {"cN": "cL", "cY": "cQ", "cA": "cD"} def __init__( self, @@ -28,214 +107,184 @@ def __init__( extrapolation=None, force_convention=None, active_during="always", + axisymmetric=False, ): - """Create a generic linear aerodynamic surface, defined by its - aerodynamic coefficients derivatives. This surface is used to model any - aerodynamic surface that does not fit the predefined classes. - - Important - --------- - All the aerodynamic coefficients can be input as callable functions of - angle of attack, angle of sideslip, Mach number, Reynolds number, - pitch rate, yaw rate and roll rate. For CSV files, the header must - contain at least one of the following: "alpha", "beta", "mach", - "reynolds", "pitch_rate", "yaw_rate" and "roll_rate". - - By default the force-coefficient derivatives are the body-frame ones - (``cN_*`` normal, ``cY_*`` side, ``cA_*`` axial; see - ``force_convention``). You may instead give the wind-frame derivatives - ``cL_*`` (lift), ``cQ_*`` (side) and ``cD_*`` (drag) -- for example - ``cL_alpha`` in place of ``cN_alpha``; they are converted once to the - body-frame set at construction. + """Create a linear aerodynamic surface from its coefficient derivatives. - See Also - -------- - :ref:`genericsurfaces`. + Instead of whole coefficients, you give how each coefficient changes with + the angle of attack, the sideslip angle and the rotation rates, and the + surface adds the terms up. Use it for stability-derivative data. Parameters ---------- reference_area : int, float - Reference area of the aerodynamic surface. Has the unit of meters - squared. Commonly defined as the rocket's cross-sectional area. + Reference area of the surface, in square meters. Commonly the + rocket's cross-sectional area. reference_length : int, float - Reference length of the aerodynamic surface. Has the unit of meters. - Commonly defined as the rocket's diameter. - coefficients: dict, optional + Reference length of the surface, in meters. Commonly the rocket's + diameter. Used to non-dimensionalize the moment coefficients and the + reduced rotation rates, and (unless ``reynolds_length`` is given) as + the length scale of the Reynolds number. + coefficients : dict The coefficient derivatives (slopes), by name. Any you leave out are set to 0. Each one can be a constant, a function, or a path to a data file, and says how one force or moment coefficient changes with one variable (angle in radians, or a non-dimensional rotation rate). The names follow the pattern ``_``: the coefficient - is normal force ``cN``, side force ``cY``, axial force ``cA``, pitch moment ``cm``, - yaw moment ``cn`` or roll moment ``cl``; the variable is ``0`` (the - value at zero angle of attack, zero sideslip and zero rates), + is normal force ``cN``, side force ``cY``, axial force ``cA``, pitch + moment ``cm``, yaw moment ``cn`` or roll moment ``cl``; the variable + is ``0`` (the value at zero angle of attack, zero sideslip and zero + rates), ``alpha``, ``beta``, ``p`` (roll rate), ``q`` (pitch rate) or ``r`` (yaw rate). With ``force_convention="wind"`` the force derivatives are named after the wind-frame coefficients instead (lift ``cL``, side ``cQ``, drag ``cD`` -- e.g. ``cL_alpha``, ``cD_0``, ``cQ_beta``); the - moment names are unchanged. The full (body-frame) list is:\n - cN_0: callable, str, optional - Coefficient of normal force at zero angle of attack. Default is 0.\n - cN_alpha: callable, str, optional - Coefficient of normal force derivative with respect to angle of attack. - Default is 0.\n - cN_beta: callable, str, optional - Coefficient of normal force derivative with respect to sideslip angle. - Default is 0.\n - cN_p: callable, str, optional - Coefficient of normal force derivative with respect to roll rate. - Default is 0.\n - cN_q: callable, str, optional - Coefficient of normal force derivative with respect to pitch rate. - Default is 0.\n - cN_r: callable, str, optional - Coefficient of normal force derivative with respect to yaw rate. - Default is 0.\n - cY_0: callable, str, optional - Coefficient of side force at zero angle of attack. - Default is 0.\n - cY_alpha: callable, str, optional - Coefficient of side force derivative with respect to angle of - attack. Default is 0.\n - cY_beta: callable, str, optional - Coefficient of side force derivative with respect to sideslip - angle. Default is 0.\n - cY_p: callable, str, optional - Coefficient of side force derivative with respect to roll rate. - Default is 0.\n - cY_q: callable, str, optional - Coefficient of side force derivative with respect to pitch rate. - Default is 0.\n - cY_r: callable, str, optional - Coefficient of side force derivative with respect to yaw rate. - Default is 0.\n - cA_0: callable, str, optional - Coefficient of axial force at zero angle of attack. Default is 0.\n - cA_alpha: callable, str, optional - Coefficient of axial force derivative with respect to angle of attack. - Default is 0.\n - cA_beta: callable, str, optional - Coefficient of axial force derivative with respect to sideslip angle. - Default is 0.\n - cA_p: callable, str, optional - Coefficient of axial force derivative with respect to roll rate. - Default is 0.\n - cA_q: callable, str, optional - Coefficient of axial force derivative with respect to pitch rate. - Default is 0.\n - cA_r: callable, str, optional - Coefficient of axial force derivative with respect to yaw rate. - Default is 0.\n - cm_0: callable, str, optional - Coefficient of pitch moment at zero angle of attack. - Default is 0.\n - cm_alpha: callable, str, optional - Coefficient of pitch moment derivative with respect to angle of - attack. Default is 0.\n - cm_beta: callable, str, optional - Coefficient of pitch moment derivative with respect to sideslip - angle. Default is 0.\n - cm_p: callable, str, optional - Coefficient of pitch moment derivative with respect to roll rate. - Default is 0.\n - cm_q: callable, str, optional - Coefficient of pitch moment derivative with respect to pitch rate. - Default is 0.\n - cm_r: callable, str, optional - Coefficient of pitch moment derivative with respect to yaw rate. - Default is 0.\n - cn_0: callable, str, optional - Coefficient of yaw moment at zero angle of attack. - Default is 0.\n - cn_alpha: callable, str, optional - Coefficient of yaw moment derivative with respect to angle of - attack. Default is 0.\n - cn_beta: callable, str, optional - Coefficient of yaw moment derivative with respect to sideslip angle. - Default is 0.\n - cn_p: callable, str, optional - Coefficient of yaw moment derivative with respect to roll rate. - Default is 0.\n - cn_q: callable, str, optional - Coefficient of yaw moment derivative with respect to pitch rate. - Default is 0.\n - cn_r: callable, str, optional - Coefficient of yaw moment derivative with respect to yaw rate. - Default is 0.\n - cl_0: callable, str, optional - Coefficient of roll moment at zero angle of attack. - Default is 0.\n - cl_alpha: callable, str, optional - Coefficient of roll moment derivative with respect to angle of - attack. Default is 0.\n - cl_beta: callable, str, optional - Coefficient of roll moment derivative with respect to sideslip - angle. Default is 0.\n - cl_p: callable, str, optional - Coefficient of roll moment derivative with respect to roll rate. - Default is 0.\n - cl_q: callable, str, optional - Coefficient of roll moment derivative with respect to pitch rate. - Default is 0.\n - cl_r: callable, str, optional - Coefficient of roll moment derivative with respect to yaw rate. - Default is 0.\n - center_of_pressure : tuple, optional - Application point of the aerodynamic forces and moments. The - center of pressure is defined in the local coordinate system of the - aerodynamic surface. The default value is (0, 0, 0). + moment names are unchanged. + + For example ``cN_alpha`` is how much the normal force coefficient + changes per radian of angle of attack, ``cm_q`` how much the pitch + moment coefficient changes with the pitch rate, ``cl_p`` the roll + damping, and ``cA_0`` the axial force (drag) coefficient at zero + angle. That gives 36 names in all: each of ``cN``, ``cY``, ``cA``, + ``cm``, ``cn``, ``cl`` combined with each of ``0``, ``alpha``, + ``beta``, ``p``, ``q``, ``r``. + + Each coefficient is the sum of its six terms, for example + ``cm = cm_0 + cm_alpha * alpha + cm_beta * beta + cm_p * roll_rate + + cm_q * pitch_rate + cm_r * yaw_rate``. A derivative that opposes the + motion, such as a pitch damping ``cm_q``, is given as a negative + number. For a rocket that behaves the same in every plane, the yaw + derivatives mirror the pitch ones with a sign flip on the force + and moment slopes (``cY_beta = -cN_alpha``, ``cn_beta = + -cm_alpha``) and none on the rate terms (``cY_r = cN_q``, + ``cn_r = cm_q``); see :ref:`lineargenericsurface_axisymmetric`. + + Each derivative can itself depend on ``alpha``, ``beta``, ``mach``, + ``reynolds``, ``pitch_rate``, ``yaw_rate`` and ``roll_rate`` (angles + in radians, or ``alpha_deg`` and ``beta_deg`` in degrees; rates in + reduced form, such as ``q * L_ref / (2 * V)`` for the pitch rate), + most often on Mach alone. It can be given as: + + - a number: a constant. + - a function whose arguments are named after the variables it uses, + for example ``lambda mach: ...``. + - the path to a CSV file whose header names the variables, with the + derivative in the last column. A two-column file without a header + is read as Mach against the derivative. + - a list or numpy array of data points. With two columns it is read + as Mach against the derivative; with more, give it as a pair with + the names of its variables, for example + ``(points, ["mach", "reynolds"])``. + - a :class:`Function`, or a pair ``(axes, values)`` for values on a + regular grid. + center_of_pressure : tuple, list, optional + Point where the aerodynamic forces are applied and about which the + moment derivatives are taken, as ``(x, y, z)`` in meters. It is + measured from the position the surface is added to the rocket at: + ``z`` runs along the rocket's centerline and is positive toward the + nose, whichever coordinate system orientation the rocket uses. The + default value is (0, 0, 0). + + The ``z`` component may instead vary with Mach: give it as a + function of Mach (``lambda mach: ...``), a one-input + :class:`Function`, or a two-column table ``[[mach, z], ...]``. The + force is then applied at ``z = 0`` and its moment about the given + center of pressure is added to the moment derivatives + (``cm_alpha + cN_alpha * z(mach) / L_ref`` and so on). Such a center + of pressure is fixed when the surface is created. name : str, optional - Name of the aerodynamic surface. Default is 'Generic Linear - Surface'. + Name of the surface. Default is ``"Generic Linear Surface"``. reynolds_length : int, float, optional Length scale, in meters, of the Reynolds number passed to the - coefficient derivatives. See :class:`GenericSurface`. ``None`` (the - default) uses ``reference_length`` (the diameter). + derivatives. Set it to the length your Reynolds-dependent data was + tabulated against (for example the rocket's body length). ``None`` + (the default) uses ``reference_length`` (the diameter). Has no + effect unless a derivative depends on ``reynolds``. interpolation : str or dict, optional - How tabulated coefficient derivatives interpolate between points. - The accepted methods depend on the coefficient's dimensionality: a - 1-D table (e.g. a Mach-only curve) accepts ``"linear"``, ``"akima"``, - ``"spline"`` and ``"polynomial"``; a multi-dimensional scattered - table accepts ``"linear"``, ``"shepard"`` and ``"rbf"``; and a - multi-dimensional table on a regular Cartesian grid accepts - ``"linear"``, ``"nearest"``, ``"slinear"``, ``"cubic"``, - ``"quintic"`` and ``"pchip"`` (with ``"spline"`` mapped to - ``"cubic"`` and ``"akima"`` to ``"pchip"``). Accepts either a simple - string or a dict keyed by coefficient name (names left out fall back - to the default). ``None`` (the default) uses ``"linear"`` for tables + How tabulated derivatives interpolate between points. The accepted + methods depend on the table: a 1-D table (e.g. a Mach-only curve) + accepts ``"linear"``, ``"akima"``, ``"spline"`` and ``"polynomial"``; + a multi-dimensional scattered table accepts ``"linear"``, + ``"shepard"`` and ``"rbf"``; and a multi-dimensional table on a + regular grid accepts ``"linear"``, ``"nearest"``, ``"slinear"``, + ``"cubic"``, ``"quintic"`` and ``"pchip"`` (with ``"spline"`` read as + ``"cubic"`` and ``"akima"`` as ``"pchip"``). Give one string for + every derivative, or a dict keyed by derivative name (names left out + use the default). ``None`` (the default) uses ``"linear"`` for tables built here and keeps a pre-built ``Function``'s own setting. extrapolation : str or dict, optional - How tabulated coefficient derivatives behave outside their data - range: ``"constant"`` holds the value at the nearest data edge, + How tabulated derivatives behave outside their data range: + ``"constant"`` holds the value at the nearest data edge, ``"natural"`` keeps following the curve, and ``"zero"`` returns 0. - Accepts either a simple string or a dict keyed by coefficient name - (names left out fall back to the default). ``None`` (the default) - uses ``"constant"`` for tables built here and keeps whatever a - pre-built ``Function`` already carries. Only affects tabulated - sources (constants and callables are evaluated directly). + Give one string for every derivative, or a dict keyed by derivative + name (names left out use the default). ``None`` (the default) uses + ``"constant"`` for tables built here and keeps whatever a pre-built + ``Function`` already carries. Only affects tabulated sources + (constants and functions are evaluated directly). force_convention : str, optional - The frame your force-coefficient derivatives are given in. ``"body"`` - for the body-frame derivatives ``cN_*`` (normal), ``cY_*`` (side) and - ``cA_*`` (axial); ``"wind"`` for the wind-frame derivatives - ``cL_*`` (lift), ``cQ_*`` (side) and ``cD_*`` (drag). The moment - derivatives (``cm_*``, ``cn_*``, ``cl_*``) are the same in both. - ``None`` (the default) infers the frame from the coefficient names you - pass and assumes body when none are given. A wind-frame input is - converted once to the body-frame derivatives the surface stores, by - linearizing the angle-of-attack/sideslip rotation about zero: the - straight renames ``cN_0 = cL_0``, ``cN_beta = cL_beta``, the rate - derivatives, and the cross terms ``cN_alpha = cL_alpha + cD_0``, - ``cY_beta = cQ_beta - cD_0``, ``cA_alpha = cD_alpha - cL_0`` and - ``cA_beta = cD_beta + cQ_0``. At zero angle this reduces to - ``cN = cL``, ``cY = cQ``, ``cA = cD``. + The frame the force derivatives are given in: + + - ``"body"``: ``cN_*`` (normal), ``cY_*`` (side) and ``cA_*`` + (axial). + - ``"wind"``: ``cL_*`` (lift), ``cQ_*`` (side) and ``cD_*`` (drag). + + The moment derivatives (``cm_*``, ``cn_*``, ``cl_*``) are the same + in both. ``None`` (the default) works out the frame from the names, + and uses ``"body"`` when there are no force derivatives. A + wind-frame input is converted once to the body-frame derivatives the + surface stores, by linearizing the angle-of-attack and sideslip + rotation about zero: the straight renames ``cN_0 = cL_0``, + ``cN_beta = cL_beta``, the rate derivatives, and the cross terms + ``cN_alpha = cL_alpha + cD_0``, ``cY_beta = cQ_beta - cD_0``, + ``cA_alpha = cD_alpha - cL_0`` and ``cA_beta = cD_beta + cQ_0``. At + zero angle this reduces to ``cN = cL``, ``cY = cQ``, ``cA = cD``. active_during : str or callable, optional When this surface produces aerodynamic force during a simulation: - ``"always"`` (default), ``"power_on"`` (only while the motor burns), - ``"power_off"`` (only after burnout), or a function - ``active_during(t, flight)`` returning ``True`` when the surface is - active at time ``t``. See :class:`GenericSurface` for details. - """ + - ``"always"`` (default): the surface always contributes force. + - ``"power_on"``: only while the motor is burning. + - ``"power_off"``: only after the motor has burned out. + - a function ``active_during(t, flight)`` returning ``True`` when + the surface is active at time ``t`` (in seconds) of the given + :class:`Flight`. + axisymmetric : bool, optional + Set it to ``True`` when the data describes a rocket (or a part) that + behaves the same in every plane through its axis, such as a rocket + with evenly spaced fins. You then give only the pitch-plane + derivatives (``cN_*`` or ``cL_*``, and ``cm_*``), as stability + derivatives are usually reported, and the yaw-plane ones are filled + in for you: ``cY_beta = -cN_alpha``, ``cn_beta = -cm_alpha``, + ``cY_r = cN_q`` and ``cn_r = cm_q``. The axial and roll derivatives + are used as given. + + With ``True``, do not give any yaw-plane derivative (``cY_*``, + ``cQ_*`` or ``cn_*``), nor a sideways force or moment at zero angle + (``cN_0``, ``cm_0``, ``cN_p``, ``cm_p``). A pitch-plane derivative may depend on + ``mach``, ``reynolds``, the rates and ``alpha_total``, but not on + ``alpha``, ``beta`` or ``phi``, which single out one plane. + + Default is ``False``: the two planes are used as given, and a plane + without derivatives produces no force. + + Raises + ------ + TypeError + If ``coefficients`` is not a dict, or ``center_of_pressure`` is not + an ``(x, y, z)`` triple. + ValueError + If a name is not one of the 36 derivatives (a coefficient value such + as ``cN`` included), if wind-frame and body-frame force derivatives + are mixed without a ``force_convention``, if ``force_convention`` + or ``active_during`` is not one of the accepted values, or if + ``axisymmetric`` is ``True`` and a derivative breaks the rules + above. + + See Also + -------- + :ref:`genericsurfaces` + """ + # Read while the coefficients are built + self._axisymmetric = bool(axisymmetric) super().__init__( reference_area=reference_area, reference_length=reference_length, @@ -254,440 +303,257 @@ def __init__( self.prints = _LinearGenericSurfacePrints(self) self.plots = _LinearGenericSurfacePlots(self) - def _evaluate_stability_derivatives(self): - """Build the center-of-pressure accessors for the linear model. - - The linear model already stores the coefficient derivatives - ``cN_alpha``/``cm_alpha`` (pitch) and ``cY_beta``/``cn_beta`` (yaw) as - the surface's own coefficients, so there is nothing to differentiate: - :meth:`_set_stability_accessors` reads them directly (evaluated at zero - alpha/beta and zero rates). Damping derivatives (``_p/_q/_r``) are - intentionally excluded from the stability center of pressure. - """ - self._set_stability_accessors() + @classmethod + def _get_default_coefficients(cls): + """Return the 36 derivative names (``cN_0`` ... ``cl_r``), each at 0.""" + return { + f"{coefficient}_{suffix}": 0 + for coefficient in cls._COEFFICIENTS + for suffix in (*cls._FORCING_TERMS, *cls._DAMPING_TERMS) + } - def _get_default_coefficients(self): - """Returns default coefficients + @classmethod + def _wind_default_coefficient_names(cls): + """Return the 36 derivative names with the force prefixes in the wind frame. - Returns - ------- - default_coefficients: dict - Dictionary whose keys are the coefficients names and keys - are the default values. + ``cN_*`` becomes ``cL_*``, ``cY_*`` becomes ``cQ_*`` and ``cA_*`` becomes + ``cD_*``; the moment names are unchanged. """ - default_coefficients = { - "cN_0": 0, - "cN_alpha": 0, - "cN_beta": 0, - "cN_p": 0, - "cN_q": 0, - "cN_r": 0, - "cY_0": 0, - "cY_alpha": 0, - "cY_beta": 0, - "cY_p": 0, - "cY_q": 0, - "cY_r": 0, - "cA_0": 0, - "cA_alpha": 0, - "cA_beta": 0, - "cA_p": 0, - "cA_q": 0, - "cA_r": 0, - "cm_0": 0, - "cm_alpha": 0, - "cm_beta": 0, - "cm_p": 0, - "cm_q": 0, - "cm_r": 0, - "cn_0": 0, - "cn_alpha": 0, - "cn_beta": 0, - "cn_p": 0, - "cn_q": 0, - "cn_r": 0, - "cl_0": 0, - "cl_alpha": 0, - "cl_beta": 0, - "cl_p": 0, - "cl_q": 0, - "cl_r": 0, - } - return default_coefficients + names = set() + for key in cls._get_default_coefficients(): + prefix, sep, suffix = key.partition("_") + names.add(f"{cls._BODY_TO_WIND_PREFIX.get(prefix, prefix)}{sep}{suffix}") + return names - # Body force-coefficient prefix -> wind force-coefficient prefix, used to - # name the accepted wind-frame inputs. The per-plane suffixes (_0, _alpha, - # _beta, _p, _q, _r) and the moment coefficients (cm/cn/cl) are frame-shared. - _BODY_TO_WIND_PREFIX = {"cN": "cL", "cY": "cQ", "cA": "cD"} + @classmethod + def _input_coefficient_names(cls): + """Return every name a derivative can be given under, in either frame.""" + return ( + set(cls._get_default_coefficients()) | cls._wind_default_coefficient_names() + ) def _force_frames_present(self, coefficients): - """Detect the force frame from the derivative-name prefixes: a wind key - looks like ``cL_alpha``/``cD_0``/``cQ_beta`` and a body key like - ``cN_alpha``/``cA_0``/``cY_beta``. The moment derivatives (``cm_*``, - ``cn_*``, ``cl_*``) are frame-shared and ignored here. + """Tell which force frames the names belong to, as ``(has_wind, has_body)``. + + The frame is read from the prefix: ``cL_alpha`` is wind, ``cN_alpha`` body. """ prefixes = {key.split("_", 1)[0] for key in coefficients} has_wind = bool(prefixes & set(self._WIND_FORCE_NAMES)) has_body = bool(prefixes & set(self._BODY_FORCE_NAMES)) return has_wind, has_body - def _wind_default_coefficient_names(self): - """The valid wind-frame input names: the body defaults with the force - prefixes swapped to wind (``cN_* -> cL_*``, ``cY_* -> cQ_*``, - ``cA_* -> cD_*``); the moment names are unchanged. + def _check_coefficients(self, input_coefficients, default_coefficients): + """Raise a ``ValueError`` for a name that is not one of the derivatives. + + On top of the generic check, a coefficient value such as ``cN`` gets a hint + that a derivative is expected. """ - names = set() - for key in self._get_default_coefficients(): - prefix, sep, suffix = key.partition("_") - wind_prefix = self._BODY_TO_WIND_PREFIX.get(prefix, prefix) - names.add(f"{wind_prefix}{sep}{suffix}") - return names + values = sorted( + set(input_coefficients) & GenericSurface._input_coefficient_names() + ) + if values: + raise ValueError( + f"{', '.join(values)}: LinearGenericSurface takes derivatives, " + f"such as {values[0]}_alpha or {values[0]}_q, not coefficient " + "values. For a coefficient given against the angle, use " + "GenericSurface." + ) + super()._check_coefficients(input_coefficients, default_coefficients) + + def _as_coefficient(self, source, name, single_var=None): + """Wrap a derivative as an :class:`AeroCoefficient`. + + A derivative's name already fixes its angle or rate, so a one-column table + or a headerless file is read against Mach. + """ + return super()._as_coefficient(source, name, single_var or "mach") def _wind_input_to_body(self, coefficients): - """Convert wind-frame coefficient derivatives (``cL_*``/``cD_*``/``cQ_*``) - into the canonical body-frame derivatives (``cN_*``/``cY_*``/``cA_*``). + """Convert wind-frame derivatives into body-frame ones. - The full body-frame force coefficients are the wind ones rotated by the - angle of attack and sideslip; linearizing that rotation about - ``alpha = beta = 0`` gives, to first order, a coefficient-derivative map - with four cross-frame terms:: + The wind-frame ``cL_*``, ``cQ_*`` and ``cD_*`` become ``cN_*``, ``cY_*`` + and ``cA_*``. Linearizing the rotation between the two frames about + ``alpha = beta = 0`` makes every force derivative a straight rename + (``cN_0 = cL_0``, ``cN_q = cL_q``, ...) except four cross terms:: cN_alpha = cL_alpha + cD_0 cA_alpha = cD_alpha - cL_0 cY_beta = cQ_beta - cD_0 cA_beta = cD_beta + cQ_0 - Every other derivative is a straight rename (``cN_0 = cL_0``, - ``cN_beta = cL_beta``, the rate derivatives ``cN_p = cL_p`` ..., and the - wind side/axial analogues). At zero angle this reduces to ``cN = cL``, - ``cY = cQ``, ``cA = cD``, matching the generic surface. The moment - derivatives (``cm_*``/``cn_*``/``cl_*``) are frame-shared and pass - through unchanged. + At zero angle this reduces to ``cN = cL``, ``cY = cQ``, ``cA = cD``. The + moment derivatives are the same in both frames and pass through. """ - invalid = set(coefficients) - self._wind_default_coefficient_names() - if invalid: - raise ValueError( - f"Invalid coefficient name(s) used in key(s): {', '.join(invalid)}. " - "Check the documentation for valid names." + self._check_coefficients(coefficients, self._wind_default_coefficient_names()) + + def combined(first_name, second_name, sign, name): + # first + sign * second, over only the variables the two use. When + # one is zero the other is kept as it is. + first, second = ( + self._as_coefficient(coefficients.get(n, 0), n) + for n in (first_name, second_name) + ) + if second.is_zero: + return first + if first.is_zero: + return second if sign > 0 else second * -1.0 + used = [ + var + for var in self.independent_vars + if var in first.depends_on or var in second.depends_on + ] + if not used: + return first.get_value_opt() + sign * second.get_value_opt() + evaluate_first = first.evaluator(used) + evaluate_second = second.evaluator(used) + return _as_function( + lambda *args: evaluate_first(*args) + sign * evaluate_second(*args), + used, + name, ) - - def wind(name): - return coefficients.get(name, 0) body = { - "cN_0": wind("cL_0"), - "cN_alpha": self._combine(wind("cL_alpha"), wind("cD_0"), 1.0, "cN_alpha"), - "cN_beta": wind("cL_beta"), - "cN_p": wind("cL_p"), - "cN_q": wind("cL_q"), - "cN_r": wind("cL_r"), - "cY_0": wind("cQ_0"), - "cY_alpha": wind("cQ_alpha"), - "cY_beta": self._combine(wind("cQ_beta"), wind("cD_0"), -1.0, "cY_beta"), - "cY_p": wind("cQ_p"), - "cY_q": wind("cQ_q"), - "cY_r": wind("cQ_r"), - "cA_0": wind("cD_0"), - "cA_alpha": self._combine(wind("cD_alpha"), wind("cL_0"), -1.0, "cA_alpha"), - "cA_beta": self._combine(wind("cD_beta"), wind("cQ_0"), 1.0, "cA_beta"), - "cA_p": wind("cD_p"), - "cA_q": wind("cD_q"), - "cA_r": wind("cD_r"), + f"{body_prefix}_{suffix}": coefficients.get(f"{wind_prefix}_{suffix}", 0) + for body_prefix, wind_prefix in self._BODY_TO_WIND_PREFIX.items() + for suffix in (*self._FORCING_TERMS, *self._DAMPING_TERMS) } - # Moment derivatives are the same in both frames; pass them through. + body["cN_alpha"] = combined("cL_alpha", "cD_0", 1.0, "cN_alpha") + body["cY_beta"] = combined("cQ_beta", "cD_0", -1.0, "cY_beta") + body["cA_alpha"] = combined("cD_alpha", "cL_0", -1.0, "cA_alpha") + body["cA_beta"] = combined("cD_beta", "cQ_0", 1.0, "cA_beta") for name, value in coefficients.items(): if name.split("_", 1)[0] not in self._WIND_FORCE_NAMES: body[name] = value return body - def _as_coefficient(self, source, name): - """Wrap a raw coefficient input as an :class:`AeroCoefficient` over this - surface's variables (used when recombining wind-frame derivatives). - """ - return AeroCoefficient( - source, - control_variables=self.control_variables, - name=name, + # Yaw-plane derivative suffix -> the pitch-plane suffix it comes from, for a + # surface that behaves the same in every plane: a quarter turn about the + # axis maps the pitch plane onto the yaw plane + _YAW_FROM_PITCH = { + "0": "0", + "alpha": "beta", + "beta": "alpha", + "q": "r", + "r": "q", + "p": "p", + } + + def _complete_body_coefficients(self, coefficients): + """Fill in the yaw-plane derivatives of an axisymmetric surface from + its pitch-plane ones: ``cY_beta = -cN_alpha``, ``cY_alpha = cN_beta``, + ``cY_r = cN_q``, ``cY_q = -cN_r``, ``cY_0 = cN_0`` and ``cY_p = cN_p``, + and the same from ``cm`` to ``cn``.""" + if not self._axisymmetric: + return coefficients + given = sorted( + name + for name, coefficient in self._input_coefficients.items() + if name.split("_")[0] in ("cY", "cQ", "cn") and not coefficient.is_zero ) - - def _combine(self, first, second, sign, name): - """Return a coefficient equal to ``first + sign * second``. - - When one term is identically zero the other is returned directly (as a - renamed coefficient), so a derivative that is really just a rename keeps - its original, low-dimensional form. Otherwise the two are summed by a - small wrapper evaluated over the full variable tuple. - """ - coeff_first = self._as_coefficient(first, name) - coeff_second = self._as_coefficient(second, name) - if coeff_second.is_zero: - return coeff_first - if coeff_first.is_zero: - return coeff_second if sign > 0 else coeff_second * -1.0 - first_opt = coeff_first.get_value_opt - second_opt = coeff_second.get_value_opt - - def combined(*args): - return first_opt(*args) + sign * second_opt(*args) - - combined.__signature__ = inspect.Signature( - inspect.Parameter(var, inspect.Parameter.POSITIONAL_OR_KEYWORD) - for var in self.independent_vars - ) - return self._as_coefficient(combined, name) - - _COEFFICIENT_INPUTS = [ - "alpha", - "beta", - "mach", - "reynolds", - "pitch_rate", - "yaw_rate", - "roll_rate", - ] - - def compute_forcing_coefficient(self, c_0, c_alpha, c_beta): - """Compose the forcing coefficient ``c_0 + c_alpha*alpha + c_beta*beta``, - evaluating only the non-zero terms. - - Two hot-loop optimizations: ``get_value_opt`` is the unvalidated fast - evaluator (for callable-source coefficients it is the raw source), and - terms that are identically zero are skipped entirely. For a Barrowman - surface each forcing coefficient has at most one non-zero derivative, so - this typically collapses to a single source call (or to a constant 0). - - Parameters - ---------- - c_0 : AeroCoefficient - Zero-angle derivative (constant term). - c_alpha : AeroCoefficient - Derivative with respect to the angle of attack ``alpha``. - c_beta : AeroCoefficient - Derivative with respect to the sideslip angle ``beta``. - - Returns - ------- - Function - Coefficient as a function of the independent variables - ``(alpha, beta, mach, reynolds, pitch_rate, yaw_rate, roll_rate)``. - """ - has_0 = not getattr(c_0, "is_zero_coefficient", False) - has_alpha = not getattr(c_alpha, "is_zero_coefficient", False) - has_beta = not getattr(c_beta, "is_zero_coefficient", False) - c_0_opt = c_0.get_value_opt - c_alpha_opt = c_alpha.get_value_opt - c_beta_opt = c_beta.get_value_opt - - if not (has_0 or has_alpha or has_beta): - - def total_coefficient( # pylint: disable=unused-argument - alpha, beta, mach, reynolds, pitch_rate, yaw_rate, roll_rate - ): - return 0.0 - - else: - - def total_coefficient( - alpha, beta, mach, reynolds, pitch_rate, yaw_rate, roll_rate - ): - value = 0.0 - if has_0: - value += c_0_opt( - alpha, beta, mach, reynolds, pitch_rate, yaw_rate, roll_rate - ) - if has_alpha: - value += ( - c_alpha_opt( - alpha, beta, mach, reynolds, pitch_rate, yaw_rate, roll_rate - ) - * alpha + if given: + raise ValueError( + f"{', '.join(given)} cannot be given with axisymmetric=True, " + "which fills in the yaw-plane derivatives from the pitch-plane " + "ones. Leave them out, or use axisymmetric=False and give both " + "planes." + ) + coefficients = dict(coefficients) + for pitch, yaw in (("cN", "cY"), ("cm", "cn")): + for suffix, source in self._YAW_FROM_PITCH.items(): + coefficients.pop(f"{yaw}_{suffix}", None) + name = f"{pitch}_{source}" + if name not in coefficients: + continue + derivative = self._as_coefficient(coefficients[name], name) + if derivative.is_zero: + continue + if source in ("0", "p"): + raise ValueError( + f"{name} cannot be given with axisymmetric=True: a " + "sideways force or moment at zero angle of attack " + "points in one direction, which a rocket that behaves " + "the same in every plane does not have. Leave it out, " + "or use axisymmetric=False and give both planes." ) - if has_beta: - value += ( - c_beta_opt( - alpha, beta, mach, reynolds, pitch_rate, yaw_rate, roll_rate - ) - * beta + one_plane = sorted( + set(derivative.source_angles) & {"alpha", "beta", "phi"} + ) + if one_plane: + raise ValueError( + f"{name} depends on {' and '.join(one_plane)}, which " + "singles out one plane, so it cannot be used with " + "axisymmetric=True. Give it against alpha_total, or " + "use axisymmetric=False and give both planes." ) - return value + sign = -1.0 if suffix in ("beta", "q") else 1.0 + coefficients[f"{yaw}_{suffix}"] = sign * derivative + return coefficients - return Function(total_coefficient, self._COEFFICIENT_INPUTS, ["coefficient"]) + def to_dict(self, include_outputs=False, **kwargs): + data = super().to_dict(include_outputs=include_outputs, **kwargs) + data["axisymmetric"] = self._axisymmetric + return data - def compute_damping_coefficient(self, c_p, c_q, c_r): - """Compose the damping coefficient - ``c_p*roll_rate + c_q*pitch_rate + c_r*yaw_rate``, evaluating only the - non-zero terms (see :meth:`compute_forcing_coefficient`). For a Barrowman - surface only ``cl_p`` (roll damping) is non-zero, so most damping - coefficients collapse to a constant 0. - - Parameters - ---------- - c_p : AeroCoefficient - Derivative with respect to the roll rate ``roll_rate``. - c_q : AeroCoefficient - Derivative with respect to the pitch rate ``pitch_rate``. - c_r : AeroCoefficient - Derivative with respect to the yaw rate ``yaw_rate``. - - Returns - ------- - Function - Coefficient as a function of the independent variables - ``(alpha, beta, mach, reynolds, pitch_rate, yaw_rate, roll_rate)``. - """ - has_p = not getattr(c_p, "is_zero_coefficient", False) - has_q = not getattr(c_q, "is_zero_coefficient", False) - has_r = not getattr(c_r, "is_zero_coefficient", False) - c_p_opt = c_p.get_value_opt - c_q_opt = c_q.get_value_opt - c_r_opt = c_r.get_value_opt - - if not (has_p or has_q or has_r): - - def total_coefficient( # pylint: disable=unused-argument - alpha, beta, mach, reynolds, pitch_rate, yaw_rate, roll_rate - ): - return 0.0 - - else: - - def total_coefficient( - alpha, beta, mach, reynolds, pitch_rate, yaw_rate, roll_rate - ): - value = 0.0 - if has_p: - value += ( - c_p_opt( - alpha, beta, mach, reynolds, pitch_rate, yaw_rate, roll_rate - ) - * roll_rate - ) - if has_q: - value += ( - c_q_opt( - alpha, beta, mach, reynolds, pitch_rate, yaw_rate, roll_rate - ) - * pitch_rate - ) - if has_r: - value += ( - c_r_opt( - alpha, beta, mach, reynolds, pitch_rate, yaw_rate, roll_rate - ) - * yaw_rate - ) - return value - - return Function(total_coefficient, self._COEFFICIENT_INPUTS, ["coefficient"]) + @classmethod + def _arguments_from_dict(cls, data): + arguments = super()._arguments_from_dict(data) + arguments["axisymmetric"] = data.get("axisymmetric", False) + return arguments def compute_all_coefficients(self): - """Compute all the aerodynamic coefficients from the derivatives.""" - self.cNf = self.compute_forcing_coefficient( - self.cN_0, self.cN_alpha, self.cN_beta - ) - self.cNd = self.compute_damping_coefficient(self.cN_p, self.cN_q, self.cN_r) - - self.cYf = self.compute_forcing_coefficient( - self.cY_0, self.cY_alpha, self.cY_beta - ) - self.cYd = self.compute_damping_coefficient(self.cY_p, self.cY_q, self.cY_r) + """Build the six coefficients from their derivatives. - self.cAf = self.compute_forcing_coefficient( - self.cA_0, self.cA_alpha, self.cA_beta - ) - self.cAd = self.compute_damping_coefficient(self.cA_p, self.cA_q, self.cA_r) - - self.cmf = self.compute_forcing_coefficient( - self.cm_0, self.cm_alpha, self.cm_beta - ) - self.cmd = self.compute_damping_coefficient(self.cm_p, self.cm_q, self.cm_r) - - self.cnf = self.compute_forcing_coefficient( - self.cn_0, self.cn_alpha, self.cn_beta - ) - self.cnd = self.compute_damping_coefficient(self.cn_p, self.cn_q, self.cn_r) - - self.clf = self.compute_forcing_coefficient( - self.cl_0, self.cl_alpha, self.cl_beta - ) - self.cld = self.compute_damping_coefficient(self.cl_p, self.cl_q, self.cl_r) - - self.cN = self.cNf - self.cY = self.cYf - self.cA = self.cAf - self.cm = self.cmf - self.cn = self.cnf - self.cl = self.clf + For each coefficient (``cN``, ``cY``, ``cA``, ``cm``, ``cn``, ``cl``) + three attributes are set, all functions of the surface's variables: - def _compute_from_coefficients( - self, - rho, - stream_speed, - alpha, - beta, - mach, - reynolds, - pitch_rate, - yaw_rate, - roll_rate, - ): - """Compute the aerodynamic forces and moments from the aerodynamic - coefficients. + - the whole coefficient, for example ``cm``, used in the simulation; + - its forcing part ``cmf = cm_0 + cm_alpha * alpha + cm_beta * beta``; + - its damping part + ``cmd = cm_p * roll_rate + cm_q * pitch_rate + cm_r * yaw_rate``. - Parameters - ---------- - rho : float - Air density. - stream_speed : float - Magnitude of the airflow speed. - alpha : float - Angle of attack in radians. - beta : float - Sideslip angle in radians. - mach : float - Mach number. - reynolds : float - Reynolds number. - pitch_rate : float - Non-dimensional (reduced) pitch rate, ``q * L_ref / (2 * V)``. - yaw_rate : float - Non-dimensional (reduced) yaw rate, ``r * L_ref / (2 * V)``. - roll_rate : float - Non-dimensional (reduced) roll rate, ``p * L_ref / (2 * V)``. - - Returns - ------- - tuple of float - The body-frame force components ``(R1, R2, R3)`` and the moments - ``(pitch, yaw, roll)``. + The whole coefficient is the sum of the two parts. A damping derivative + that opposes the motion is a negative number, so nothing is subtracted. """ - # Precompute common values - dyn_pressure_area = 0.5 * rho * stream_speed**2 * self.reference_area - dyn_pressure_area_length = dyn_pressure_area * self.reference_length - args = (alpha, beta, mach, reynolds, pitch_rate, yaw_rate, roll_rate) - - # Body-frame forces (forcing + reduced-rate damping), straight from the - # body-frame coefficients: normal cN, side cY, axial cA. - normal = dyn_pressure_area * ( - self.cNf.get_value_opt(*args) + self.cNd.get_value_opt(*args) - ) - yaw_side = dyn_pressure_area * ( - self.cYf.get_value_opt(*args) + self.cYd.get_value_opt(*args) - ) - axial = dyn_pressure_area * ( - self.cAf.get_value_opt(*args) + self.cAd.get_value_opt(*args) - ) - r1 = yaw_side - r2 = -normal - r3 = -axial + every_term = {**self._FORCING_TERMS, **self._DAMPING_TERMS} + for coefficient in self._COEFFICIENTS: + for suffix, terms in ( + ("f", self._FORCING_TERMS), + ("d", self._DAMPING_TERMS), + ("", every_term), + ): + setattr( + self, + coefficient + suffix, + self._linear_coefficient(coefficient, terms), + ) + + def _linear_coefficient(self, coefficient, terms): + """Sum the given terms of one coefficient into a :class:`Function`. + + Each term is a derivative times its variable, such as ``cm_q * pitch_rate``; + the ``_0`` term is used as it is. Zero derivatives are left out, which keeps + the sum cheap during the simulation. + """ + active = [] + for suffix, variable in terms.items(): + derivative = getattr(self, f"{coefficient}_{suffix}") + if not derivative.is_zero: + position = ( + None if variable is None else self.independent_vars.index(variable) + ) + active.append((derivative.get_value_opt, position)) + + def total(*args): + value = 0.0 + for evaluate, position in active: + term = evaluate(*args) + value += term if position is None else term * args[position] + return value + + return _as_function(total, self.independent_vars, coefficient) - # Compute aerodynamic moments (forcing + reduced-rate damping) - pitch = dyn_pressure_area_length * ( - self.cmf.get_value_opt(*args) + self.cmd.get_value_opt(*args) - ) - yaw = dyn_pressure_area_length * ( - self.cnf.get_value_opt(*args) + self.cnd.get_value_opt(*args) - ) - roll = dyn_pressure_area_length * ( - self.clf.get_value_opt(*args) + self.cld.get_value_opt(*args) - ) + def _evaluate_stability_derivatives(self): + """Build the center-of-pressure functions. - return r1, r2, r3, pitch, yaw, roll + The slopes ``cN_alpha``, ``cm_alpha``, ``cY_beta`` and ``cn_beta`` are the + given derivatives, so nothing is differentiated. + """ + self._set_stability_accessors() diff --git a/rocketpy/rocket/aero_surface/nose_cone.py b/rocketpy/rocket/aero_surface/nose_cone.py index 7f17c159b..cb16504ca 100644 --- a/rocketpy/rocket/aero_surface/nose_cone.py +++ b/rocketpy/rocket/aero_surface/nose_cone.py @@ -64,11 +64,36 @@ class NoseCone(_BarrowmanSurface): NoseCone.cpz : float Nose cone local center of pressure z coordinate. Has units of length and is given in meters. - NoseCone.cl : Function - Roll-moment coefficient, inherited from the generic-surface model - (a function of the flow variables). Zero for a nose cone or tail; for a - fin set it carries the cant forcing and roll damping. The lift-curve - slope is ``clalpha``. + NoseCone.cN : AeroCoefficient + Normal force coefficient, the force in the pitch plane. + NoseCone.cY : AeroCoefficient + Side force coefficient, the force in the yaw plane. + NoseCone.cA : AeroCoefficient + Axial force coefficient, the force along the rocket's axis. + NoseCone.cm : AeroCoefficient + Pitching moment coefficient. + NoseCone.cn : AeroCoefficient + Yawing moment coefficient. + NoseCone.cl : AeroCoefficient + Roll moment coefficient. Always zero for a nose cone. + NoseCone.cL : Function + Lift coefficient, the force perpendicular to the airflow. + NoseCone.cD : Function + Drag coefficient, the force along the airflow. + NoseCone.cQ : Function + Crosswind coefficient, the side force relative to the airflow. + NoseCone.cN_alpha : AeroCoefficient + Slope of ``cN`` with the angle of attack, as a function of Mach. + Has units of 1/rad. + NoseCone.cY_beta : AeroCoefficient + Slope of ``cY`` with the sideslip angle, as a function of Mach. + Has units of 1/rad. + NoseCone.cm_alpha : AeroCoefficient + Slope of ``cm`` with the angle of attack, as a function of Mach. + Has units of 1/rad. + NoseCone.cn_beta : AeroCoefficient + Slope of ``cn`` with the sideslip angle, as a function of Mach. + Has units of 1/rad. NoseCone.clalpha : float Normal-force coefficient slope. Has units of 1/rad. NoseCone.plots : plots.aero_surface_plots._NoseConePlots @@ -136,6 +161,9 @@ def __init__( # pylint: disable=too-many-statements self._rocket_radius = rocket_radius self._base_radius = base_radius + # The length the user gave. A bluff tip shortens ``_length``, and each + # new shape is worked out again from this one. + self._input_length = length self._length = length if bluffness is not None: if bluffness > 1 or bluffness < 0: # pragma: no cover @@ -166,8 +194,8 @@ def __init__( # pylint: disable=too-many-statements self.evaluate_lift_coefficient() self.evaluate_center_of_pressure() - # Translate the Barrowman geometry (clalpha, cpz) into the linear - # generic-surface coefficient model and build the shared compute path. + # Translate the Barrowman geometry (clalpha, cpz) into generic-surface + # coefficients. super().__init__( reference_area=self.reference_area, reference_length=self.reference_length, @@ -186,9 +214,12 @@ def rocket_radius(self): @rocket_radius.setter def rocket_radius(self, value): self._rocket_radius = value + self.reference_area = np.pi * value**2 + self.reference_length = 2 * value self.evaluate_geometrical_parameters() self.evaluate_lift_coefficient() self.evaluate_nose_shape() + self._geometry_changed() @property def base_radius(self): @@ -200,6 +231,7 @@ def base_radius(self, value): self.evaluate_geometrical_parameters() self.evaluate_lift_coefficient() self.evaluate_nose_shape() + self._geometry_changed() @property def length(self): @@ -207,9 +239,11 @@ def length(self): @length.setter def length(self, value): + self._input_length = value self._length = value self.evaluate_center_of_pressure() self.evaluate_nose_shape() + self._geometry_changed() @property def power(self): @@ -226,6 +260,7 @@ def power(self, value): self.evaluate_k() self.evaluate_center_of_pressure() self.evaluate_nose_shape() + self._geometry_changed() @property def kind(self): @@ -332,6 +367,7 @@ def theta_vonkarman(x): self.evaluate_center_of_pressure() self.evaluate_geometrical_parameters() self.evaluate_nose_shape() + self._geometry_changed() @property def bluffness(self): @@ -353,6 +389,7 @@ def bluffness(self, value): ) self._bluffness = value self.evaluate_nose_shape() + self._geometry_changed() def evaluate_geometrical_parameters(self): """Calculates and saves nose cone's radius ratio. @@ -394,6 +431,7 @@ def evaluate_nose_shape(self): # pylint: disable=too-many-statements """ number_of_points = 127 density_modifier = 3 # increase density of points to improve accuracy + self._length = self._input_length def find_x_intercept(x): # find the tangential intersection point between the circle and nosec curve @@ -503,10 +541,7 @@ def evaluate_center_of_pressure(self): Tuple containing cpx, cpy, cpz. """ - self.cpz = self.k * self.length - self.cpy = 0 - self.cpx = 0 - self.cp = (self.cpx, self.cpy, self.cpz) + self._set_center_of_pressure((0, 0, self.k * self.length)) return self.cp def draw(self, *, filename=None): @@ -549,7 +584,7 @@ def all_info(self): def to_dict(self, **kwargs): data = { - "_length": self._length, + "_length": self._input_length, "_kind": self._kind, "_base_radius": self._base_radius, "_bluffness": self._bluffness, @@ -560,7 +595,7 @@ def to_dict(self, **kwargs): if kwargs.get("include_outputs", False): clalpha = self.clalpha if kwargs.get("discretize", False): - clalpha = clalpha.set_discrete(0, 4, 50) + clalpha = clalpha.set_discrete(0, 4, 50, mutate_self=False) data["cp"] = self.cp data["clalpha"] = clalpha diff --git a/rocketpy/rocket/aero_surface/tail.py b/rocketpy/rocket/aero_surface/tail.py index 7ccd2e1e3..7a20a16f0 100644 --- a/rocketpy/rocket/aero_surface/tail.py +++ b/rocketpy/rocket/aero_surface/tail.py @@ -40,11 +40,36 @@ class Tail(_BarrowmanSurface): z local coordinate of the center of pressure of the tail. Tail.cp : tuple Tuple containing the coordinates of the center of pressure of the tail. - Tail.cl : Function - Roll-moment coefficient, inherited from the generic-surface model - (a function of the flow variables). Zero for a nose cone or tail; for a - fin set it carries the cant forcing and roll damping. The lift-curve - slope is ``clalpha``. + Tail.cN : AeroCoefficient + Normal force coefficient, the force in the pitch plane. + Tail.cY : AeroCoefficient + Side force coefficient, the force in the yaw plane. + Tail.cA : AeroCoefficient + Axial force coefficient, the force along the rocket's axis. + Tail.cm : AeroCoefficient + Pitching moment coefficient. + Tail.cn : AeroCoefficient + Yawing moment coefficient. + Tail.cl : AeroCoefficient + Roll moment coefficient. Always zero for a tail. + Tail.cL : Function + Lift coefficient, the force perpendicular to the airflow. + Tail.cD : Function + Drag coefficient, the force along the airflow. + Tail.cQ : Function + Crosswind coefficient, the side force relative to the airflow. + Tail.cN_alpha : AeroCoefficient + Slope of ``cN`` with the angle of attack, as a function of Mach. + Has units of 1/rad. + Tail.cY_beta : AeroCoefficient + Slope of ``cY`` with the sideslip angle, as a function of Mach. + Has units of 1/rad. + Tail.cm_alpha : AeroCoefficient + Slope of ``cm`` with the angle of attack, as a function of Mach. + Has units of 1/rad. + Tail.cn_beta : AeroCoefficient + Slope of ``cn`` with the sideslip angle, as a function of Mach. + Has units of 1/rad. Tail.clalpha : float Normal-force coefficient slope. Has the unit of 1/rad. Tail.slant_length : float @@ -91,8 +116,8 @@ def __init__(self, top_radius, bottom_radius, length, rocket_radius, name="Tail" self.evaluate_lift_coefficient() self.evaluate_center_of_pressure() - # Translate the Barrowman geometry into the linear generic-surface - # coefficient model and build the shared compute path. + # Translate the Barrowman geometry (clalpha, cpz) into generic-surface + # coefficients. super().__init__( reference_area=self.reference_area, reference_length=self.reference_length, @@ -114,6 +139,7 @@ def top_radius(self, value): self.evaluate_geometrical_parameters() self.evaluate_lift_coefficient() self.evaluate_center_of_pressure() + self._geometry_changed() @property def bottom_radius(self): @@ -125,6 +151,7 @@ def bottom_radius(self, value): self.evaluate_geometrical_parameters() self.evaluate_lift_coefficient() self.evaluate_center_of_pressure() + self._geometry_changed() @property def length(self): @@ -135,6 +162,7 @@ def length(self, value): self._length = value self.evaluate_geometrical_parameters() self.evaluate_center_of_pressure() + self._geometry_changed() @property def rocket_radius(self): @@ -143,7 +171,10 @@ def rocket_radius(self): @rocket_radius.setter def rocket_radius(self, value): self._rocket_radius = value + self.reference_area = np.pi * value**2 + self.reference_length = 2 * value self.evaluate_lift_coefficient() + self._geometry_changed() def evaluate_geometrical_parameters(self): """Calculates and saves tail's slant length and surface area. @@ -203,10 +234,7 @@ def evaluate_center_of_pressure(self): cpz = (self.length / 3) * (1 + (1 - r) / (1 - r**2)) # Store values as class attributes - self.cpx = 0 - self.cpy = 0 - self.cpz = cpz - self.cp = (self.cpx, self.cpy, self.cpz) + self._set_center_of_pressure((0, 0, cpz)) def info(self): self.prints.geometry() @@ -228,7 +256,7 @@ def to_dict(self, **kwargs): if kwargs.get("include_outputs", False): clalpha = self.clalpha if kwargs.get("discretize", False): - clalpha = clalpha.set_discrete(0, 4, 50) + clalpha = clalpha.set_discrete(0, 4, 50, mutate_self=False) data.update( { diff --git a/rocketpy/rocket/components.py b/rocketpy/rocket/components.py index 57e4d12f8..26b119aeb 100644 --- a/rocketpy/rocket/components.py +++ b/rocketpy/rocket/components.py @@ -1,6 +1,17 @@ from collections import namedtuple from copy import deepcopy +from rocketpy.mathutils.vector_matrix import Vector + + +def position_vector(position): + """The position of a component as a Vector. A single number is the + coordinate along the rocket axis, with x = y = 0; a tuple, list or Vector + is the full (x, y, z) position.""" + if isinstance(position, (Vector, tuple, list)): + return Vector(position) + return Vector([0, 0, position]) + class Components: """A Collection Class to hold components of the Rocket class. Each component @@ -156,6 +167,31 @@ def remove(self, component): else: raise ValueError(f"Component {component} not found in components {self}") + def set_position(self, component, position): + """Move a component to a new position, keeping its place in the list. + + Parameters + ---------- + component : Any + The component to be moved. + position : int, float, tuple, list, Vector + The new position of the component relative to the rocket's + coordinate system origin: a number is the coordinate along the + rocket axis, a tuple, list or Vector the full (x, y, z) position. + + Returns + ------- + None + """ + position = position_vector(position) + for index, comp in enumerate(self._components): + if comp.component == component: + self.__position_list[index] = position + self._components[index] = self.component_tuple(component, position) + break + else: + raise ValueError(f"Component {component} not found in components {self}") + def pop(self, index=-1): """Pop a component from the list of components. diff --git a/rocketpy/rocket/helpers.py b/rocketpy/rocket/helpers.py deleted file mode 100644 index 26593c832..000000000 --- a/rocketpy/rocket/helpers.py +++ /dev/null @@ -1,280 +0,0 @@ -"""Helper functions backing some :class:`rocketpy.Rocket` methods. - -These carry the heavier computations behind the rocket's full-body aerodynamic -reduction (``to_coefficients`` / ``to_surface``), kept out of ``rocket.py`` so -that module stays focused on the rocket's public interface. Each function takes -the rocket it operates on as its first argument. -""" - -import math - -import numpy as np - -from rocketpy.mathutils.function import Function -from rocketpy.mathutils.vector_matrix import Vector -from rocketpy.rocket.aero_surface.aero_coefficient import AeroCoefficient - - -def zero_drag(rocket, which): - """Set one of the rocket's built-in drag curves to zero. ``which`` is - ``"power_on"`` or ``"power_off"``. Rebuilds the ``*_drag_7d``, - ``*_drag_by_mach`` and the public ``*_drag`` alias to match, mirroring how - they are built in ``Rocket.__init__``. - """ - label = "Power On" if which == "power_on" else "Power Off" - setattr( - rocket, - f"{which}_drag_7d", - AeroCoefficient( - 0, - name=f"Drag Coefficient with {label}", - extrapolation="constant", - single_var="mach", - ), - ) - by_mach = Function( - lambda mach: 0.0, - inputs="Mach Number", - outputs=f"Drag Coefficient with {label}", - interpolation="linear", - extrapolation="constant", - ) - setattr(rocket, f"{which}_drag_by_mach", by_mach) - setattr(rocket, f"{which}_drag", by_mach) - setattr(rocket, f"_{which}_drag_input", 0) - - -def summed_force_and_moment(rocket, alpha, beta, mach, omega, speed=1.0): - """Total body-frame force ``(R1, R2, R3)`` and moment ``(M1, M2, M3)`` about - the center of dry mass, summed over every aerodynamic surface of ``rocket`` - at a flow state and set of body rates. - - Mirrors the per-surface computation the flight integrator performs: each - surface is fed its own local stream velocity, which includes the - ``omega x cp`` lever-arm term, so the sum captures the pitch and yaw damping - the distributed surfaces produce through their fore-and-aft positions. - Evaluated at unit air density; the result scales out of any dimensionless - coefficient, and the chosen ``speed`` cancels from every coefficient built - from it. ``omega`` is the body angular rate in rad/s. - """ - stream_direction = Vector([-math.tan(beta), -math.tan(alpha), -1.0]) - stream_at_cdm = stream_direction / abs(stream_direction) * speed - body_rates = Vector(list(omega)) - density = Function(1.0) - dynamic_viscosity = Function(1e30) # vanishing-Reynolds limit - speed_of_sound = speed / mach if mach > 0 else 1e30 - totals = np.zeros(6) - for surface, _ in rocket.aerodynamic_surfaces: - cp = rocket.surfaces_cp_to_cdm[surface] - comp_stream = stream_at_cdm - (body_rates ^ cp) - comp_speed = abs(comp_stream) - forces = surface.compute_forces_and_moments( - comp_stream, - comp_speed, - comp_speed / speed_of_sound, - 1.0, - cp, - body_rates, - density, - dynamic_viscosity, - 0.0, - ) - totals += np.array(forces) - return totals - - -def neutral_point_and_slope(rocket, alpha, beta, mach, plane="pitch", step=1e-4): - """Local (tangent) neutral point and force-curve slope at a finite incidence. - - Generalizes the aerodynamic center to a non-zero angle of attack. The neutral - point is the point about which the aerodynamic moment does not change for a - *small* perturbation of the incidence angle around the given ``(alpha, beta)`` - state, i.e. the tangent of the moment-versus-force curve at that state. It is - obtained by central-differencing the rocket's summed body-frame force and - moment (about the center of dry mass) with respect to the plane's incidence - angle, then forming ``x_cdm + csys * L_ref * (dCm/da) / (dCN/da)``. - - For a rocket whose surfaces are all linear in incidence (the built-in - Barrowman surfaces) the result is independent of ``alpha``/``beta`` and equals - :attr:`rocketpy.Rocket.aerodynamic_center`. It moves with incidence only when - a surface's normal-force coefficient is nonlinear in the incidence angle (for - example a Galejs ``sin**2(alpha)`` body-lift term added as a - :class:`rocketpy.GenericSurface`). - - Parameters - ---------- - rocket : rocketpy.Rocket - The rocket to evaluate. - alpha, beta : float - Angle of attack and sideslip angle, in radians, defining the state the - neutral point is taken about. - mach : float - Free-stream Mach number. - plane : str, optional - ``"pitch"`` (perturb ``alpha``, use the normal force and pitch moment) or - ``"yaw"`` (perturb ``beta``, use the side force and yaw moment). Default - ``"pitch"``. - step : float, optional - Half-step, in radians, of the central difference. Default ``1e-4``. - - Returns - ------- - tuple of float - ``(neutral_point, slope)``: the neutral-point axial position in the - user-defined rocket frame, and the force-curve slope ``dCN/da`` (pitch) - or ``dCY/db`` (yaw) at the state. When the slope vanishes (no lift at all) - the neutral point falls back to the zero-incidence aerodynamic center. - """ - rocket.evaluate_surfaces_cp_to_cdm() - reference_length = 2 * rocket.radius - dynamic_pressure_area = 0.5 * rocket.area # unit speed, unit density - dynamic_pressure_area_length = dynamic_pressure_area * reference_length - - def coefficients(a, b): - r1, r2, _, m1, m2, _ = summed_force_and_moment( - rocket, a, b, mach, (0.0, 0.0, 0.0) - ) - if plane == "yaw": - return r1 / dynamic_pressure_area, m2 / dynamic_pressure_area_length - return -r2 / dynamic_pressure_area, m1 / dynamic_pressure_area_length - - if plane == "yaw": - force_high, moment_high = coefficients(alpha, beta + step) - force_low, moment_low = coefficients(alpha, beta - step) - else: - force_high, moment_high = coefficients(alpha + step, beta) - force_low, moment_low = coefficients(alpha - step, beta) - - force_slope = (force_high - force_low) / (2 * step) - moment_slope = (moment_high - moment_low) / (2 * step) - if force_slope == 0: - center = ( - rocket.aerodynamic_center_yaw - if plane == "yaw" - else rocket.aerodynamic_center - ) - return center.get_value_opt(mach), 0.0 - neutral_point = rocket.center_of_dry_mass_position + ( - rocket._csys * reference_length * moment_slope / force_slope - ) - return neutral_point, force_slope - - -def full_body_coefficients(rocket, machs=None, force_convention="body"): - """Compute the rocket's lumped stability-derivative set, split by motor - phase. Backs :meth:`rocketpy.Rocket.to_coefficients`; see that method for the - full description of the returned coefficient sets and their limitations. - """ - if force_convention not in ("body", "wind"): - raise ValueError( - f"force_convention must be 'body' or 'wind', got {force_convention!r}." - ) - if machs is None: - machs = np.arange(0.0, 3.01, 0.02) - machs = np.asarray(machs, dtype=float) - # Make sure each surface's center-of-pressure offset is current. - rocket.evaluate_surfaces_cp_to_cdm() - - reference_length = 2 * rocket.radius - dynamic_pressure_area = 0.5 * rocket.area # unit speed, unit density - dynamic_pressure_area_length = dynamic_pressure_area * reference_length - - def coefficients_at(alpha, beta, red_pitch, red_yaw, red_roll, mach): - # reduced rate -> body rate at unit speed: omega = rate * 2 V / L_ref - rate_factor = 2.0 / reference_length - omega = ( - red_pitch * rate_factor, - red_yaw * rate_factor, - red_roll * rate_factor, - ) - r1, r2, _, m1, m2, m3 = summed_force_and_moment( - rocket, alpha, beta, mach, omega - ) - return { - "cN": -r2 / dynamic_pressure_area, - "cY": r1 / dynamic_pressure_area, - "cm": m1 / dynamic_pressure_area_length, - "cn": m2 / dynamic_pressure_area_length, - "cl": m3 / dynamic_pressure_area_length, - } - - step = 1e-5 - - def slope(field, coeff): - values = [] - for mach in machs: - state = { - "alpha": 0.0, - "beta": 0.0, - "red_pitch": 0.0, - "red_yaw": 0.0, - "red_roll": 0.0, - } - high = coefficients_at(mach=mach, **{**state, field: step}) - low = coefficients_at(mach=mach, **{**state, field: -step}) - values.append((high[coeff] - low[coeff]) / (2 * step)) - return np.array(values) - - # Motor-independent derivative values on the Mach grid, in the body frame. - # The rocket's shape does not change with the motor, so these are shared by - # both phases; only the drag below differs. - derivatives = { - "cN_alpha": slope("alpha", "cN"), - "cm_alpha": slope("alpha", "cm"), - "cN_q": slope("red_pitch", "cN"), - "cm_q": slope("red_pitch", "cm"), - "cY_beta": slope("beta", "cY"), - "cn_beta": slope("beta", "cn"), - "cY_r": slope("red_yaw", "cY"), - "cn_r": slope("red_yaw", "cn"), - "cl_p": slope("red_roll", "cl"), - } - # The drag is a Mach curve at zero incidence (the axial coefficient's - # constant term), and it is the one term that differs by motor phase. - drag_by_phase = { - "power_off": rocket.power_off_drag_by_mach, - "power_on": rocket.power_on_drag_by_mach, - } - - result = {} - for phase, drag_curve in drag_by_phase.items(): - values = { - **derivatives, - "cA_0": np.array([drag_curve.get_value_opt(m) for m in machs]), - } - if force_convention == "wind": - values = body_derivatives_to_wind(values) - result[phase] = { - coeff_name: Function( - np.column_stack([machs, curve]), - "Mach", - coeff_name, - interpolation="akima", - extrapolation="constant", - ) - for coeff_name, curve in values.items() - } - return result - - -def body_derivatives_to_wind(body): - """Express a body-frame derivative set (``cN_*``/``cY_*``/``cA_*``) in the - wind-frame names (``cL_*``/``cQ_*``/``cD_*``); the moment derivatives are - frame-shared. Two cross terms fold the axial force in at incidence, - ``cL_alpha = cN_alpha - cA_0`` and ``cQ_beta = cY_beta + cA_0`` -- the linear - inverse of the wind-to-body rotation :class:`LinearGenericSurface` applies to - a wind-frame input, so feeding the result back with - ``force_convention="wind"`` recovers the same body-frame surface. Operates on - the tabulated derivative values (arrays over the Mach grid). - """ - drag = body.get("cA_0", 0.0) - rename = {"cN": "cL", "cY": "cQ", "cA": "cD"} - wind = {} - for key, value in body.items(): - prefix, sep, suffix = key.partition("_") - wind[f"{rename.get(prefix, prefix)}{sep}{suffix}"] = value - if "cN_alpha" in body: - wind["cL_alpha"] = body["cN_alpha"] - drag - if "cY_beta" in body: - wind["cQ_beta"] = body["cY_beta"] + drag - return wind diff --git a/rocketpy/rocket/point_mass_rocket.py b/rocketpy/rocket/point_mass_rocket.py index dc198c41f..f21a77074 100644 --- a/rocketpy/rocket/point_mass_rocket.py +++ b/rocketpy/rocket/point_mass_rocket.py @@ -51,12 +51,6 @@ class PointMassRocket(Rocket): power_on_drag : Function Rocket's drag coefficient as a function of Mach number when the motor is on. Alias for ``power_on_drag_by_mach``. - power_off_drag_input : int, float, callable, array, string, Function - Original user input for the drag coefficient with motor off. - Preserved for reconstruction and Monte Carlo workflows. - power_on_drag_input : int, float, callable, array, string, Function - Original user input for the drag coefficient with motor on. - Preserved for reconstruction and Monte Carlo workflows. power_off_drag_7d : AeroCoefficient Drag coefficient callable over seven independent variables in the order: alpha, beta, mach, reynolds, pitch_rate, yaw_rate, roll_rate. diff --git a/rocketpy/rocket/rocket.py b/rocketpy/rocket/rocket.py index 7d7fc075d..cc513ee9e 100644 --- a/rocketpy/rocket/rocket.py +++ b/rocketpy/rocket/rocket.py @@ -1,7 +1,7 @@ import inspect import math import warnings -from typing import Iterable +from collections.abc import Iterable import numpy as np @@ -11,11 +11,29 @@ from rocketpy.motors.empty_motor import EmptyMotor from rocketpy.plots.rocket_plots import _RocketPlots from rocketpy.prints.rocket_prints import _RocketPrints +from rocketpy.rocket._helpers import ( + center_of_pressure_position, + corrective_and_damping_moments, + disturbance_response, + full_body_coefficients, + is_axisymmetric, + is_incidence_linear, + lateral_inertia_and_rate, + lumped_control_names, + lumped_surface_coefficients, + moment_slopes_left_out, + neutral_point_and_slope, + stability_margin_and_slope, + stability_surfaces, + uses_rate_coefficients, +) from rocketpy.rocket.aero_surface import ( AirBrakes, + ControllableGenericSurface, EllipticalFins, Fin, Fins, + GenericSurface, NoseCone, RailButtons, Tail, @@ -26,14 +44,8 @@ from rocketpy.rocket.aero_surface.fins.free_form_fin import FreeFormFin from rocketpy.rocket.aero_surface.fins.free_form_fins import FreeFormFins from rocketpy.rocket.aero_surface.fins.trapezoidal_fin import TrapezoidalFin -from rocketpy.rocket.aero_surface.generic_surface import GenericSurface from rocketpy.rocket.aero_surface.linear_generic_surface import LinearGenericSurface -from rocketpy.rocket.components import Components -from rocketpy.rocket.helpers import ( - full_body_coefficients, - neutral_point_and_slope, - zero_drag, -) +from rocketpy.rocket.components import Components, position_vector from rocketpy.rocket.parachute import Parachute from rocketpy.tools import ( deprecated, @@ -42,7 +54,7 @@ ) -# pylint: disable=too-many-instance-attributes, too-many-public-methods, too-many-instance-attributes +# pylint: disable=too-many-instance-attributes, too-many-public-methods class Rocket: """Keeps rocket information. @@ -141,45 +153,52 @@ class Rocket: Rocket._controllers : list Collection of controllers of the rocket. Rocket.aerodynamic_center : Function - Function of Mach number expressing the rocket's aerodynamic center - (the linearized, small-incidence center of pressure) position relative - to the user defined rocket reference system. ``Rocket.cp_position`` is an - alias for this attribute. See :doc:`Positions and Coordinate Systems - ` for more information. + Position of the rocket's aerodynamic center, in meters, as a function + of Mach number, in the user defined rocket reference system. It is the + point the static margin is measured from. For a rocket built from nose + cones, fins and tails it is the same point as the center of pressure. + See :doc:`Positions and Coordinate Systems ` for more + information. + Rocket.cp_position : Function + Position of the rocket's center of pressure, in meters, as a function + of Mach number, in the user defined rocket reference system. Same + Function as ``Rocket.aerodynamic_center``. Rocket.stability_margin : Function - Stability margin of the rocket, in calibers, as a function of angle of - attack (radians), mach number and time. Stability margin is defined as - the distance between the center of pressure and the center of mass, - divided by the rocket's diameter. The angle-of-attack argument matters - only when a surface is nonlinear in incidence (see - ``Rocket.is_incidence_linear``); otherwise it has no effect. + Stability margin of the rocket, in calibers, as a function of Mach + number and time, with the rocket flying straight into the air (zero + angle of attack). It is the distance from the center of mass to the + center of pressure, divided by the rocket's diameter. Rocket.static_margin : Function - Static margin of the rocket, in calibers, as a function of time. Static - margin is defined as the distance between the center of pressure and the - center of mass, divided by the rocket's diameter. - Rocket.static_margin : float - Float value corresponding to rocket static margin when - loaded with propellant in units of rocket diameter or calibers. + Static margin of the rocket, in calibers, as a function of time. It is + the distance from the center of mass to the center of pressure at zero + airspeed (``cp_position`` at Mach 0), divided by the rocket's diameter. + Rocket.stability_phase : str + Which motor phase the aerodynamic center, the margins and the lumped + coefficients (``to_coefficients``) describe when a surface is only + active during one phase (its ``active_during``): ``"power_off"`` + (default, the rocket after burnout) or ``"power_on"`` (while the motor + burns). Has no effect on a rocket whose surfaces are all always active, + nor on the flight itself, which switches surfaces on and off by time. Rocket.power_off_drag : Function Rocket's drag coefficient as a function of Mach number when the - motor is off. Alias for ``power_off_drag_by_mach``. + motor is off. Alias for ``power_off_drag_by_mach``. Assign a new drag + curve to it to replace the drag used in the simulation. Rocket.power_on_drag : Function Rocket's drag coefficient as a function of Mach number when the - motor is on. Alias for ``power_on_drag_by_mach``. - Rocket.power_off_drag_input : int, float, callable, string, array, Function - Original user input for rocket's drag coefficient when the motor is - off. Preserved for reconstruction and Monte Carlo workflows. - Rocket.power_on_drag_input : int, float, callable, string, array, Function - Original user input for rocket's drag coefficient when the motor is - on. Preserved for reconstruction and Monte Carlo workflows. + motor is on. Alias for ``power_on_drag_by_mach``. Assign a new drag + curve to it to replace the drag used in the simulation. Rocket.power_off_drag_7d : AeroCoefficient Rocket's drag coefficient with motor off, callable over the seven independent variables (alpha, beta, mach, reynolds, pitch_rate, - yaw_rate, roll_rate) and stored at its intrinsic dimensionality. + yaw_rate, roll_rate), with the three rates non-dimensional + (``rate * diameter / (2 * airspeed)``). It is the rocket's axial force + coefficient: the force acts along the rocket's centerline. Rocket.power_on_drag_7d : AeroCoefficient Rocket's drag coefficient with motor on, callable over the seven independent variables (alpha, beta, mach, reynolds, pitch_rate, - yaw_rate, roll_rate) and stored at its intrinsic dimensionality. + yaw_rate, roll_rate), with the three rates non-dimensional + (``rate * diameter / (2 * airspeed)``). It is the rocket's axial force + coefficient: the force acts along the rocket's centerline. Rocket.power_off_drag_by_mach : Function Rocket's drag coefficient with motor off as a function of Mach number. Rocket.power_on_drag_by_mach : Function @@ -251,9 +270,10 @@ def __init__( # pylint: disable=too-many-statements power_on_drag, center_of_mass_without_motor, coordinate_system_orientation="tail_to_nose", + length=None, ): - """Initializes Rocket class, process inertial, geometrical and - aerodynamic parameters. + """Initialize the rocket from its inertial, geometrical and aerodynamic + parameters. Parameters ---------- @@ -273,18 +293,30 @@ def __init__( # pylint: disable=too-many-statements in the direction of e_i x e_j. Alternatively, the inertia tensor can be given as (I_11, I_22, I_33), where I_12 = I_13 = I_23 = 0. This can also be called as "rocket dry inertia tensor". - power_off_drag : int, float, callable, string, array - Rocket's drag coefficient when the motor is off. Can be given as an - entry to the Function class. See help(Function) for more - information. If int or float is given, it is assumed constant. If - callable, string or array is given, it must be a function of Mach - number only. - power_on_drag : int, float, callable, string, array - Rocket's drag coefficient when the motor is on. Can be given as an - entry to the Function class. See help(Function) for more - information. If int or float is given, it is assumed constant. If - callable, string or array is given, it must be a function of Mach - number only. + power_off_drag : int, float, callable, string, array, Function + Rocket's drag coefficient when the motor is off, based on the + rocket's cross-section area (``pi * radius**2``). It can be a number, + a ``.csv`` file or list of points with the Mach number in the first + column and the drag coefficient in the second, a function such as + ``lambda mach: ...``, or a :class:`Function`. + + The coefficient may also depend on ``alpha`` and ``beta`` (angle of + attack and sideslip, rad), or ``alpha_total`` in their place, + ``mach``, ``reynolds`` (based on the rocket's diameter) and + ``pitch_rate``, ``yaw_rate`` and ``roll_rate`` (rate in rad/s times + the diameter, divided by twice the airspeed). Name the arguments of + the function, or the columns of the ``.csv`` file, after the ones + used, for example ``lambda alpha, mach: ...``. + + Outside the range of its data, a table or a ``.csv`` file holds the + value at its nearest end; a function is evaluated as given. + + For the coefficients of the whole rocket (lift, drag and moment + against angle of attack), see :meth:`add_full_body_aerodynamics`. + power_on_drag : int, float, callable, string, array, Function + Rocket's drag coefficient when the motor is on. Given in the same + way as ``power_off_drag``. If you only have one drag curve, use it + for both. center_of_mass_without_motor : int, float Position, in m, of the rocket's center of mass without motor relative to the rocket's coordinate system. Default is 0, which @@ -305,6 +337,15 @@ def __init__( # pylint: disable=too-many-statements coordinate system with the rocket's axis of symmetry pointing from the rocket's nose cone to the rocket's tail. Default is "tail_to_nose". + length : int, float, optional + Overall length of the rocket, from the nose tip to the aft end, in + meters. It is only used to report the static and stability margins + as a percentage of the rocket's length (prints and plots). When not + given, the length is measured from the nose cone to the aft-most + tail, fin set or motor nozzle (see :attr:`length`). Give it when + the rocket has no nose cone, such as a rocket described only by a + :class:`rocketpy.GenericSurface`; otherwise the percentage is left + out. Default is ``None``. Returns ------- @@ -337,6 +378,7 @@ def __init__( # pylint: disable=too-many-statements self.center_of_mass_without_motor = center_of_mass_without_motor self.radius = radius self.area = np.pi * self.radius**2 + self._length = length self._is_point_mass = False # Eccentricity data initialization @@ -355,9 +397,17 @@ def __init__( # pylint: disable=too-many-statements self.sensors_by_name = {} self.aerodynamic_surfaces = Components() self.surfaces_cp_to_cdm = {} + # What the values derived from the surfaces were last built for (see + # _refresh_aerodynamics, _refresh_aerodynamic_center, _refresh_margins) + self._lever_arms_stamp = None + self._aerodynamic_center_stamp = None + self._margins_stamp = None # Set once a full-body model replaces the modeled aerodynamics # (add_full_body_aerodynamics(overwrite=True)); warns on later surface adds. self._aerodynamics_overwritten = False + # Which motor phase the stability analysis describes when a surface is + # only active during one of them (see ``stability_phase``). + self.stability_phase = "power_off" self.rail_buttons = Components() self._aerodynamic_center = Function( @@ -374,13 +424,11 @@ def __init__( # pylint: disable=too-many-statements lambda time: 0, inputs="Time (s)", outputs="Static Margin (c)" ) self._stability_margin = Function( - lambda alpha, mach, time: 0, - inputs=["Angle of Attack (rad)", "Mach", "Time (s)"], + lambda mach, time: 0, + inputs=["Mach", "Time (s)"], outputs="Stability Margin (c)", ) - # Yaw-plane counterparts. The pitch-plane attributes above remain the - # primary (default) margin; these expose the yaw plane for - # non-axisymmetric rockets (see ``evaluate_center_of_pressure``). + # Yaw-plane counterparts self._aerodynamic_center_yaw = Function( lambda mach: 0, inputs="Mach Number", @@ -395,44 +443,14 @@ def __init__( # pylint: disable=too-many-statements lambda time: 0, inputs="Time (s)", outputs="Static Margin - Yaw (c)" ) self._stability_margin_yaw = Function( - lambda beta, mach, time: 0, - inputs=["Sideslip Angle (rad)", "Mach", "Time (s)"], + lambda mach, time: 0, + inputs=["Mach", "Time (s)"], outputs="Stability Margin - Yaw (c)", ) # Define aerodynamic drag coefficients used during flight simulation - self.power_off_drag_7d = AeroCoefficient( - power_off_drag, - name="Drag Coefficient with Power Off", - extrapolation="constant", - single_var="mach", - ) - self.power_on_drag_7d = AeroCoefficient( - power_on_drag, - name="Drag Coefficient with Power On", - extrapolation="constant", - single_var="mach", - ) - self.power_on_drag_by_mach = Function( - lambda mach: self.power_on_drag_7d(0, 0, mach, 0, 0, 0, 0), - inputs="Mach Number", - outputs="Drag Coefficient with Power On", - interpolation="linear", - extrapolation="constant", - ) - self.power_off_drag_by_mach = Function( - lambda mach: self.power_off_drag_7d(0, 0, mach, 0, 0, 0, 0), - inputs="Mach Number", - outputs="Drag Coefficient with Power Off", - interpolation="linear", - extrapolation="constant", - ) - # Saving raw user input for reconstruction and Monte Carlo - self._power_off_drag_input = power_off_drag - self._power_on_drag_input = power_on_drag - # Public API attributes: keep as Function (Mach-only) for backward compatibility - self.power_off_drag = self.power_off_drag_by_mach - self.power_on_drag = self.power_on_drag_by_mach + self._set_drag("power_off", power_off_drag) + self._set_drag("power_on", power_on_drag) # Create a, possibly, temporary empty motor # self.motors = Components() # currently unused, only 1 motor is supported @@ -453,19 +471,81 @@ def __init__( # pylint: disable=too-many-statements self.evaluate_reduced_mass() self.evaluate_thrust_to_weight() - # The aerodynamic center and the margins are evaluated lazily - self._cp_outdated = True - self._margin_outdated = True - # Whether the neutral point moves with angle of attack; set when the - # margins are evaluated (see evaluate_stability_margin). + # Attributes for lazy evaluation of aerodynamic centers and margins self._is_incidence_linear = True - # Flag for rocket non-axisymmetric warning. Used to show warning once. - self._axisymmetry_warned = False + self._uses_rate_coefficients = False + # Whether the rocket behaves the same in every plane + self._is_axisymmetric = True # Initialize plots and prints object self.prints = _RocketPrints(self) self.plots = _RocketPlots(self) + def _set_drag(self, which, source): + """Set one of the rocket's drag curves from a user input. + + Builds the two attributes of that curve: ``_drag_7d``, the + coefficient used in the simulation, and ``_drag_by_mach``, its + view against Mach number alone (also read as ``_drag``). + + Parameters + ---------- + which : str + ``"power_off"`` or ``"power_on"``. + source : int, float, callable, string, array, Function + The drag coefficient, as accepted by ``power_off_drag`` in + :meth:`__init__`. + """ + label = "Power On" if which == "power_on" else "Power Off" + setattr( + self, + f"{which}_drag_7d", + AeroCoefficient( + source, + name=f"Drag Coefficient with {label}", + extrapolation="constant", + single_var="mach", + ), + ) + # Reads the coefficient on each call, so it follows a later change of it + # (for example the Monte Carlo drag factor). + by_mach = Function( + lambda mach: getattr(self, f"{which}_drag_7d")(0, 0, mach, 0, 0, 0, 0), + inputs="Mach Number", + outputs=f"Drag Coefficient with {label}", + interpolation="linear", + extrapolation="constant", + ) + setattr(self, f"{which}_drag_by_mach", by_mach) + + @property + def power_off_drag(self): + """Drag coefficient with the motor off, as a Function of Mach number. + + It is read at zero angle of attack and zero rates. Assign a new drag + curve to it, in any of the forms accepted by :meth:`__init__`, to + replace the drag used in the simulation. + """ + return self.power_off_drag_by_mach + + @power_off_drag.setter + def power_off_drag(self, source): + self._set_drag("power_off", source) + + @property + def power_on_drag(self): + """Drag coefficient with the motor on, as a Function of Mach number. + + It is read at zero angle of attack and zero rates. Assign a new drag + curve to it, in any of the forms accepted by :meth:`__init__`, to + replace the drag used in the simulation. + """ + return self.power_on_drag_by_mach + + @power_on_drag.setter + def power_on_drag(self, source): + self._set_drag("power_on", source) + def _check_missing_components(self): """Check if the rocket is missing any essential components and issue a warning. @@ -656,48 +736,90 @@ def evaluate_thrust_to_weight(self): # Lazily-evaluated aerodynamic outputs. - def _ensure_aerodynamic_center(self): - """Recompute the pitch/yaw aerodynamic centers if a surface changed.""" - if self._cp_outdated: - self.evaluate_center_of_pressure() # clears ``_cp_outdated`` - - def _ensure_margins(self): - """Recompute the static/stability margins if a surface or the center of - mass changed. The underlying aerodynamic center is refreshed lazily by - the margin source closures.""" - if self._margin_outdated: - self._margin_outdated = False + # The values the rocket derives from its surfaces are rebuilt when read, and + # only if something they depend on changed. + + def _surfaces_stamp(self): + """What the aerodynamic center depends on: each surface, its version + (counted up when its geometry changes) and where it was placed.""" + return ( + self._csys, + self.radius, + self.stability_phase, + tuple( + (surface, surface._version, position) + for surface, position in self.aerodynamic_surfaces + ), + ) + + def _refresh_aerodynamics(self): + """Bring up to date what the simulation reads from the surfaces: each + surface's center of pressure relative to the center of dry mass (for an + individual fin, its leading edge moves with the cant angle). Returns the + surfaces stamp.""" + surfaces_stamp = self._surfaces_stamp() + stamp = ( + surfaces_stamp, + self.center_of_dry_mass_position, + self.cm_eccentricity_x, + self.cm_eccentricity_y, + ) + if stamp != self._lever_arms_stamp: + self._lever_arms_stamp = stamp + self.evaluate_surfaces_cp_to_cdm() + return surfaces_stamp + + def _refresh_aerodynamic_center(self): + """Rebuild the pitch/yaw aerodynamic centers if a surface changed. + Returns the surfaces stamp.""" + stamp = self._refresh_aerodynamics() + if stamp != self._aerodynamic_center_stamp: + # Set before computing: evaluate_center_of_pressure reads the + # aerodynamic center it is building (the axisymmetry check). + self._aerodynamic_center_stamp = stamp + self.evaluate_center_of_pressure() + return stamp + + def _refresh_margins(self): + """Rebuild the static/stability margins if a surface or the center of + mass changed.""" + stamp = (self._refresh_aerodynamic_center(), self.center_of_mass) + if stamp != self._margins_stamp: self.evaluate_stability_margin() self.evaluate_static_margin() + # Set after computing, so a rebuild that fails is tried again + self._margins_stamp = stamp @property def aerodynamic_center(self): - """Pitch-plane aerodynamic center vs Mach (lazily evaluated).""" - self._ensure_aerodynamic_center() + """Position of the rocket's aerodynamic center, in meters, as a function + of Mach number, in the user-defined rocket coordinate system. + + The aerodynamic center is the point where the extra aerodynamic force + appears when the rocket tilts a little away from the airflow. The rocket + is stable when it is behind the center of mass. For a rocket built from + nose cones, fins and tails it is the center of pressure. + """ + self._refresh_aerodynamic_center() return self._aerodynamic_center @property def aerodynamic_center_yaw(self): - """Yaw-plane aerodynamic center vs Mach (lazily evaluated).""" - self._ensure_aerodynamic_center() + """Position of the rocket's aerodynamic center in the yaw plane, in + meters, as a function of Mach number. Equals :attr:`aerodynamic_center` + for an axisymmetric rocket. + """ + self._refresh_aerodynamic_center() return self._aerodynamic_center_yaw - def neutral_point(self, alpha, mach): - """Pitch-plane neutral point at a finite angle of attack, in meters. - - The neutral point is the point about which the aerodynamic pitching - moment does not change for a small change in angle of attack. It is the - angle-of-attack-aware generalization of the - :attr:`aerodynamic_center`: evaluated at ``alpha = 0`` the two are equal, - and for a rocket built only from the linear Barrowman surfaces the - neutral point does not move with angle of attack at all. + def neutral_point(self, alpha, mach, beta=0.0): + """Position of the rocket's neutral point at an angle of attack, in + meters. - It moves with angle of attack only when a surface's normal force is - nonlinear in the angle of attack, for example a Galejs body-lift term - (growing like ``sin**2(alpha)``) added as a - :class:`rocketpy.GenericSurface`. In that case the neutral point migrates - as the angle of attack changes, exactly the behavior OpenRocket models, - and the flight stability margin follows it. + The neutral point is the point where the extra aerodynamic force + appears when the angle of attack changes a little from the given one. + The stability margin is measured from it. At zero angle of attack it is + the :attr:`aerodynamic_center`. Parameters ---------- @@ -705,22 +827,23 @@ def neutral_point(self, alpha, mach): Angle of attack, in radians, to evaluate the neutral point at. mach : float Free-stream Mach number. + beta : float, optional + Sideslip angle, in radians, the rocket is at. Default 0. Returns ------- float - Axial position of the pitch-plane neutral point in the user-defined - rocket coordinate system, in meters. + Position of the neutral point along the rocket's axis, in meters, + in the user-defined rocket coordinate system. """ - return neutral_point_and_slope(self, alpha, 0.0, mach, "pitch")[0] + return neutral_point_and_slope(self, alpha, beta, mach, "pitch")[0] - def neutral_point_yaw(self, beta, mach): - """Yaw-plane neutral point at a finite sideslip angle, in meters. + def neutral_point_yaw(self, beta, mach, alpha=0.0): + """Position of the rocket's neutral point in the yaw plane at a sideslip + angle, in meters. - Yaw-plane counterpart of :meth:`neutral_point`: the point about which the - yaw moment does not change for a small change in sideslip angle, - evaluated at the given sideslip angle. Equal to - :attr:`aerodynamic_center_yaw` at ``beta = 0``. + Same as :meth:`neutral_point`, for the sideslip angle instead of the + angle of attack. At zero angle it is :attr:`aerodynamic_center_yaw`. Parameters ---------- @@ -728,249 +851,440 @@ def neutral_point_yaw(self, beta, mach): Sideslip angle, in radians, to evaluate the neutral point at. mach : float Free-stream Mach number. + alpha : float, optional + Angle of attack, in radians, the rocket is at. Default 0. + + Returns + ------- + float + Position of the yaw-plane neutral point along the rocket's axis, in + meters, in the user-defined rocket coordinate system. + """ + return neutral_point_and_slope(self, alpha, beta, mach, "yaw")[0] + + def center_of_pressure(self, alpha, mach, beta=0.0): + """Position of the rocket's center of pressure at an angle of attack, in + meters. + + The center of pressure is the point where the whole aerodynamic force + on the rocket acts. For a rocket built from nose cones, fins and tails + it does not move with the angle of attack and equals + :attr:`cp_position`. + + Parameters + ---------- + alpha : float + Angle of attack, in radians. For an axisymmetric rocket, pass the + total angle of attack and leave ``beta`` at 0. + mach : float + Free-stream Mach number. + beta : float, optional + Sideslip angle, in radians, the rocket is at. Default 0. + + Returns + ------- + float + Position of the center of pressure along the rocket's axis, in + meters, in the user-defined rocket coordinate system. At zero angle + of attack there is no sideways force, and :attr:`cp_position` is + returned. + """ + return center_of_pressure_position(self, alpha, beta, mach, "pitch") + + def center_of_pressure_yaw(self, beta, mach, alpha=0.0): + """Position of the rocket's center of pressure in the yaw plane at a + sideslip angle, in meters. + + Same as :meth:`center_of_pressure`, for the side force and the sideslip + angle. Only needed for a rocket that is not axisymmetric. + + Parameters + ---------- + beta : float + Sideslip angle, in radians. + mach : float + Free-stream Mach number. + alpha : float, optional + Angle of attack, in radians, the rocket is at. Default 0. Returns ------- float - Axial position of the yaw-plane neutral point in the user-defined - rocket coordinate system, in meters. + Position of the yaw-plane center of pressure along the rocket's + axis, in meters, in the user-defined rocket coordinate system. At + zero sideslip angle there is no side force, and + :attr:`aerodynamic_center_yaw` is returned. """ - return neutral_point_and_slope(self, 0.0, beta, mach, "yaw")[0] + return center_of_pressure_position(self, alpha, beta, mach, "yaw") @property def total_lift_coeff_der(self): - """Total normal-force-coefficient derivative vs Mach (lazily evaluated).""" - self._ensure_aerodynamic_center() + """How fast the rocket's normal force coefficient grows with the angle of + attack, in 1/rad, as a function of Mach number: the sum over all + aerodynamic surfaces, referenced to the rocket's cross-sectional area. + """ + self._refresh_aerodynamic_center() return self._total_lift_coeff_der @property def total_side_coeff_der(self): - """Total side-force-coefficient derivative vs Mach (lazily evaluated).""" - self._ensure_aerodynamic_center() + """How fast the rocket's side force coefficient grows with the sideslip + angle, in 1/rad, as a function of Mach number. Equals + :attr:`total_lift_coeff_der` for an axisymmetric rocket. + """ + self._refresh_aerodynamic_center() return self._total_side_coeff_der @property def static_margin(self): - """Pitch-plane static margin (calibers) vs time (lazily evaluated).""" - self._ensure_margins() + """Static margin of the rocket, in calibers, as a function of time (s). + + It is the distance from the center of mass to the center of pressure at + zero airspeed (:attr:`cp_position` at Mach 0), divided by the rocket's + diameter. It is positive when the rocket is stable. + """ + self._refresh_margins() return self._static_margin @property def static_margin_yaw(self): - """Yaw-plane static margin (calibers) vs time (lazily evaluated).""" - self._ensure_margins() + """Static margin of the rocket in the yaw plane, in calibers, as a + function of time (s). Equals :attr:`static_margin` for an axisymmetric + rocket. + """ + self._refresh_margins() return self._static_margin_yaw @property def stability_margin(self): - """Pitch-plane stability margin (calibers) as a function of angle of - attack (radians), Mach and time (lazily evaluated). The angle-of-attack - argument matters only for a rocket that is nonlinear in incidence (see - :attr:`is_incidence_linear`); otherwise it has no effect and the margin - reduces to the Mach-and-time value.""" - self._ensure_margins() + """Stability margin of the rocket, in calibers, as a function of Mach + number and time (s), with the rocket flying straight into the air (zero + angle of attack). + + It is the distance from the center of mass to the center of pressure, + divided by the rocket's diameter. It is positive when the rocket is + stable. For the margin at an angle of attack, see + :meth:`neutral_point`; for the margin along a flight, see + :attr:`rocketpy.Flight.stability_margin`. + """ + self._refresh_margins() return self._stability_margin @property def stability_margin_yaw(self): - """Yaw-plane stability margin (calibers) as a function of sideslip angle - (radians), Mach and time (lazily evaluated). Equal to - :attr:`stability_margin` for an axisymmetric rocket.""" - self._ensure_margins() + """Stability margin of the rocket in the yaw plane, in calibers, as a + function of Mach number and time (s), with zero sideslip angle. Equals + :attr:`stability_margin` for an axisymmetric rocket. + """ + self._refresh_margins() return self._stability_margin_yaw @property def length(self): - """Overall aerodynamic length of the rocket, in meters. + """Overall length of the rocket, from the nose tip to the aft end, in + meters, or ``None`` when it cannot be measured. - This is the axial distance from the fore-most point of the rocket (the - nose cone tip) to the aft-most point of the rocket. It is measured along - the rocket axis and does not depend on the chosen coordinate-system - orientation. + The ``length`` given when the rocket was built, if any. Otherwise it is + measured from the rocket's parts: from the tip of the nose cone to the + aft-most point among the tails, the fins and the motor nozzle. This + needs a nose cone and at least one of a tail, a fin set or a motor. + Without them (for example a rocket described only by a + :class:`rocketpy.GenericSurface`) the two ends are not known and the + length is ``None``; give ``length`` when building the rocket instead. - The aft-most point is usually the trailing edge of the last fin set or - the base of the aft tail, but if the motor nozzle extends past the last - aerodynamic surface, the nozzle sets the aft end instead. The rocket - must have at least one aerodynamic surface with a defined axial extent - (a nose cone, tail or fin set); otherwise a ``ValueError`` is raised. - - This length is what the hobby-rocketry convention of expressing the - static/stability margin as a *percentage of body length* is measured - against, as opposed to the caliber (diameter) convention used by - ``static_margin`` and ``stability_margin``. + The length is only used to show the static and stability margins as a + percentage of the rocket's length in the prints and plots. They leave + that percentage out when the length is ``None``. Returns ------- - float - Overall aerodynamic length of the rocket, in meters. + float or None + Overall length of the rocket, in meters. It does not depend on the + coordinate system orientation. """ - fore_points = [] - aft_points = [] + if self._length is not None: + return self._length + has_nose = False + has_aft_end = False + points = [] for surface, position in self.aerodynamic_surfaces: - if isinstance(surface, (NoseCone, Tail)): + if isinstance(surface, NoseCone): + has_nose = True + axial_extent = surface.length + elif isinstance(surface, Tail): + has_aft_end = True axial_extent = surface.length elif isinstance(surface, (Fins, Fin)): + has_aft_end = True axial_extent = surface.root_chord else: - # Generic/controllable surfaces have no defined axial extent; - # they contribute a single point at their reference position. - axial_extent = 0.0 + # Generic/controllable surfaces have no defined axial extent + # and say nothing about where the rocket ends. + continue # The reference point and the point one axial extent toward the tail # (the tail direction is -_csys along the z axis). Taking the global # extremes makes the result independent of which end is the reference. - fore_points.append(position.z) - aft_points.append(position.z - self._csys * axial_extent) - - if not fore_points: - raise ValueError( - "The rocket must have at least one aerodynamic surface to have a " - "defined length." - ) - - all_points = fore_points + aft_points - # Include the nozzle if a real motor extends past the aerodynamic - # surfaces. nozzle_position is already in the rocket reference frame. + points.append(position.z) + points.append(position.z - self._csys * axial_extent) + # nozzle_position is already in the rocket reference frame. if getattr(self, "motor", None) is not None and not isinstance( self.motor, EmptyMotor ): - all_points.append(self.nozzle_position) - return max(all_points) - min(all_points) + has_aft_end = True + points.append(self.nozzle_position) + if not (has_nose and has_aft_end): + return None + return max(points) - min(points) def evaluate_center_of_pressure(self): - """Evaluates the rocket's aerodynamic center (and cp_position) as a - function of Mach number, relative to the user-defined rocket reference - system. + """Compute the rocket's center of pressure as a function of Mach number. + + The result is stored in ``aerodynamic_center`` (also read as + ``cp_position``): the average position of the aerodynamic surfaces, + each weighted by how fast its normal force grows with the angle of + attack. This is the center of pressure at a small angle of attack. - The aerodynamic center is the linearized (small-incidence, alpha=beta=0) - center of pressure: the normal-force-slope-weighted average of every - aerodynamic surface's location. + The same is done for the yaw plane (``aerodynamic_center_yaw``), with + the side force and the sideslip angle. For an axisymmetric rocket the + two are equal. When they differ a warning is shown, because + ``static_margin`` and ``stability_margin`` then describe the pitch + plane only. - It is computed independently for the **pitch** plane - (``aerodynamic_center``, from the normal-force/pitch-moment slopes) and - the **yaw** plane (``aerodynamic_center_yaw``, from the - side-force/yaw-moment slopes). For an axisymmetric rocket the two - coincide. When they differ (a non-axisymmetric configuration), a warning - is raised because the scalar ``static_margin``/``stability_margin`` - attributes describe the pitch plane only. + A surface active during only one motor phase (its ``active_during``) + is counted only when that phase is the rocket's ``stability_phase`` + (``"power_off"`` by default); a warning says so when one is left out. Returns ------- self.aerodynamic_center : Function - Function of Mach number expressing the rocket's pitch-plane - aerodynamic center position relative to the user-defined rocket - reference system. See :doc:`Positions and Coordinate Systems + Position of the rocket's pitch-plane aerodynamic center, in meters, + as a function of Mach number, in the user-defined rocket coordinate + system. See :doc:`Positions and Coordinate Systems ` for more information. """ - # Mark the pitch/yaw centers up to date before computing, so that a read - # of the ``aerodynamic_center`` property during this method (the - # ``is_axisymmetric`` check below) returns the value being built here - # rather than recursing back into this method. - self._cp_outdated = False - # Re-Initialize total force coefficient derivatives and AC positions self._total_lift_coeff_der.set_source(lambda mach: 0) self._aerodynamic_center.set_source(lambda mach: 0) self._total_side_coeff_der.set_source(lambda mach: 0) self._aerodynamic_center_yaw.set_source(lambda mach: 0) - # Calculate total force coefficient derivatives and aerodynamic center - if len(self.aerodynamic_surfaces) > 0: - for aero_surface, position in self.aerodynamic_surfaces: - # Force-curve slopes as Functions of Mach, from the surface's - # coefficient derivatives sliced at zero alpha/beta and zero - # rates. The yaw slope is the sign-flipped ``cY_beta`` so an - # axisymmetric surface gives the same signed weight as the pitch - # plane (their margins then coincide when symmetric). - lift_coeff_der = aero_surface.cN_alpha.slice("mach") - side_coeff_der = -1.0 * aero_surface.cY_beta.slice("mach") - cp_z = aero_surface.center_of_pressure_z - cp_z_yaw = aero_surface.center_of_pressure_z_yaw - # ref_factor corrects force for different reference areas - ref_factor = aero_surface.reference_area / self.area - self._total_lift_coeff_der += ref_factor * lift_coeff_der - self._aerodynamic_center += ( - ref_factor * lift_coeff_der * (position.z - self._csys * cp_z) - ) + # Surfaces active only in the other motor phase are left out. This + # method runs once per configuration, so the notice is shown once. + surfaces = stability_surfaces(self) + if len(surfaces) != len(self.aerodynamic_surfaces): + warnings.warn( + "The aerodynamic center, the margins and the lumped coefficients " + f"describe the rocket during '{self.stability_phase}': surfaces " + "active only in the other motor phase are left out. Set " + "`rocket.stability_phase` to 'power_on' or 'power_off' to choose.", + stacklevel=2, + ) + + # Calculate total force coefficient derivatives and aerodynamic center. + # The parts of the surfaces' moments the weighted average leaves out + # are kept apart and added before dividing. + self.evaluate_surfaces_cp_to_cdm() + pitch_left_out, yaw_left_out = [], [] + for aero_surface, position in surfaces: + # Force-curve slopes as Functions of Mach, from the surface's + # coefficient derivatives sliced at zero alpha/beta and zero + # rates. The yaw slope is the sign-flipped ``cY_beta`` so an + # axisymmetric surface gives the same signed weight as the pitch + # plane (their margins then coincide when symmetric). + lift_coeff_der = aero_surface.cN_alpha.slice("mach") + side_coeff_der = -1.0 * aero_surface.cY_beta.slice("mach") + cp_z = aero_surface.aerodynamic_center + cp_z_yaw = aero_surface.aerodynamic_center_yaw + # ref_factor corrects force for different reference areas + ref_factor = aero_surface.reference_area / self.area + self._total_lift_coeff_der += ref_factor * lift_coeff_der + self._aerodynamic_center += ( + ref_factor * lift_coeff_der * (position.z + self._csys * cp_z) + ) - # Yaw plane. - self._total_side_coeff_der += ref_factor * side_coeff_der - self._aerodynamic_center_yaw += ( - ref_factor * side_coeff_der * (position.z - self._csys * cp_z_yaw) + # Yaw plane. + self._total_side_coeff_der += ref_factor * side_coeff_der + self._aerodynamic_center_yaw += ( + ref_factor * side_coeff_der * (position.z + self._csys * cp_z_yaw) + ) + pitch, yaw = moment_slopes_left_out( + self, aero_surface, position, lift_coeff_der, side_coeff_der + ) + if pitch is not None: + pitch_left_out.append(pitch) + if yaw is not None: + yaw_left_out.append(yaw) + # Avoid errors when only zero-lift surfaces are added + if self._total_lift_coeff_der.get_value(0) != 0: + for moment_slope in pitch_left_out: + self._aerodynamic_center += moment_slope + self._aerodynamic_center /= self._total_lift_coeff_der + if self._total_side_coeff_der.get_value(0) != 0: + for moment_slope in yaw_left_out: + self._aerodynamic_center_yaw += moment_slope + self._aerodynamic_center_yaw /= self._total_side_coeff_der + + # Non-axisymmetry advisory. This method runs once per configuration of + # the surfaces, so the warning is shown once per configuration. + # Nose, tail and fin sets contribute identically to both planes; with + # any other surface the rocket's own derivatives decide. + self._is_axisymmetric = all( + surface.is_axisymmetric for surface, _ in surfaces + ) or is_axisymmetric(self) + if not self._is_axisymmetric: + # Largest difference of the two aerodynamic centers over Mach 0 to 3 + max_diff = max( + abs( + self.aerodynamic_center.get_value_opt(mach) + - self.aerodynamic_center_yaw.get_value_opt(mach) + ) + for mach in np.linspace(0.0, 3.0, 16) + ) + if max_diff > 1e-6 * (2 * self.radius): + reason = ( + "Pitch- and yaw-plane aerodynamic centers differ " + f"(max difference ~{max_diff:.4g} m)" + ) + else: + reason = ( + "The pitch and yaw planes differ in strength, or couple " + "differently seen from each" ) - # Avoid errors when only zero-lift surfaces are added - if self._total_lift_coeff_der.get_value(0) != 0: - self._aerodynamic_center /= self._total_lift_coeff_der - if self._total_side_coeff_der.get_value(0) != 0: - self._aerodynamic_center_yaw /= self._total_side_coeff_der - - # Non-axisymmetry advisory. Latched once per configuration: the flag is - # re-armed whenever a surface is added (see add_surfaces) - if not self._axisymmetry_warned and not self.is_axisymmetric: - self._axisymmetry_warned = True - max_diff = self._cp_plane_max_difference() warnings.warn( - "Pitch- and yaw-plane aerodynamic centers differ " - f"(max difference ~{max_diff:.4g} m): the rocket is not " - "axisymmetric. 'aerodynamic_center', 'static_margin' and " - "'stability_margin' describe the PITCH plane. Use " - "'aerodynamic_center_yaw', 'static_margin_yaw' and " - "'stability_margin_yaw' for the yaw plane.", + f"{reason}: the rocket is not axisymmetric. " + "'aerodynamic_center', 'static_margin' and 'stability_margin' " + "describe the PITCH plane. Use 'aerodynamic_center_yaw', " + "'static_margin_yaw' and 'stability_margin_yaw' for the yaw " + "plane.", stacklevel=2, ) + # The margins are built from the center of pressure + self._margins_stamp = None return self._aerodynamic_center - def _cp_plane_max_difference(self): - """Largest pitch- vs yaw-plane aerodynamic center difference, in meters. - The difference is sampled densely across the subsonic, transonic and""" - # 0 to 3 in 0.2 steps covers RocketPy's flight regimes (sub/trans/ - # supersonic) with enough resolution that a real asymmetry, which spans a - # Mach *range*, cannot fall entirely between sample points. This only - # runs for rockets that contain a generic surface or individual fin (see - # the by-construction short-circuit in ``is_axisymmetric``). - sample_machs = np.linspace(0.0, 3.0, 16) - return max( - abs( - self.aerodynamic_center.get_value_opt(mach) - - self.aerodynamic_center_yaw.get_value_opt(mach) + def disturbance_response( + self, + speed, + time=0.0, + disturbance=5.0, + density=1.225, + speed_of_sound=340.29, + plane="pitch", + duration=None, + ): + """Compute how the rocket would swing back after a sudden disturbance, + such as a gust, at a flight condition you choose. + + The rocket is tilted by ``disturbance`` away from its flight direction + and released. The result shows how its angle swings back: how fast it + oscillates and how quickly the oscillation dies out. No flight + simulation is needed, so it is a quick way to compare designs (fin + size, ballast, inertia) at a condition such as the rail exit. + + The airspeed, air density and rocket mass are held fixed, so this is a + snapshot of one instant: during the motor burn the real conditions + change while the rocket swings. It is valid for small angles. For the + response at an instant of a simulated flight, see + :meth:`rocketpy.Flight.disturbance_response`. + + Parameters + ---------- + speed : float + Airspeed of the rocket, in m/s. For example the speed at which it + leaves the rail. + time : float, optional + Time since motor ignition, in seconds. It sets how much propellant + is left, and so the rocket's mass, center of mass and inertia, and + whether the motor is still burning (a burning motor adds damping). + Default is 0. + disturbance : float, optional + Angle the rocket is tilted by, in degrees. Default is 5. + density : float, optional + Air density, in kg/m³. Default is 1.225 (sea level). + speed_of_sound : float, optional + Speed of sound, in m/s, used to find the Mach number. Default is + 340.29 (sea level). + plane : str, optional + ``"pitch"`` or ``"yaw"``. The two only differ for a rocket that is + not axisymmetric. Default is ``"pitch"``. + duration : float, optional + How long to follow the response, in seconds. By default, long + enough for the oscillation to settle. + + Returns + ------- + rocketpy.Function + Angle of the rocket, in degrees, as a function of the time since + the disturbance, in seconds. Call ``.plot()`` on it to see the + curve; its title shows the natural frequency and damping ratio. + + Raises + ------ + ValueError + For a point mass rocket, which has no attitude to disturb. + + Examples + -------- + >>> response = rocket.disturbance_response(speed=30, time=0.4) # doctest: +SKIP + >>> response.plot() # doctest: +SKIP + """ + inertia_about_cdm = self.I_22 if plane == "yaw" else self.I_11 + if not isinstance(inertia_about_cdm, Function): + inertia, inertia_rate = 0.0, 0.0 # a point mass rocket + else: + inertia, inertia_rate = lateral_inertia_and_rate( + self, inertia_about_cdm, time ) - for mach in sample_machs + corrective, damping = corrective_and_damping_moments( + self, + 0.0, + 0.0, + speed / speed_of_sound, + time, + speed, + density, + 0.5 * density * speed**2, + inertia_rate, + plane, ) + return disturbance_response(corrective, damping, inertia, disturbance, duration) @property def is_axisymmetric(self): - """``True`` when the rocket's pitch- and yaw-plane aerodynamic centers - coincide (to caliber-scale tolerance). When ``False`` the rocket is not - axisymmetric: ``aerodynamic_center``, ``static_margin`` and - ``stability_margin`` describe the PITCH plane only and differ from their - ``*_yaw`` counterparts (``aerodynamic_center_yaw``, - ``static_margin_yaw``, ``stability_margin_yaw``).""" - # Nose, tail and fin sets contribute identically to both planes. - if all( - getattr(surface, "is_axisymmetric", False) - for surface, _ in self.aerodynamic_surfaces - ): - return True - # Tolerance relative to the rocket diameter (caliber-scale). - return self._cp_plane_max_difference() <= 1e-6 * (2 * self.radius) + """Whether the rocket behaves the same in every plane through its axis, + at small angles of attack. + + ``True`` for a rocket with evenly spaced fins. ``False`` for example + with canards on a single axis or a single fin; the pitch and yaw planes + then have separate values (:attr:`static_margin` and + :attr:`static_margin_yaw`, and so on). + """ + self._refresh_aerodynamic_center() + return self._is_axisymmetric @property def is_incidence_linear(self): - """``True`` when the rocket's aerodynamics are linear in the angle of - attack, so the neutral point (and therefore the stability margin) does - not move as the angle of attack changes. This holds for a rocket built - only from the linear Barrowman surfaces. It is ``False`` when a surface's - normal force is nonlinear in incidence, such as a Galejs body-lift term - added as a :class:`rocketpy.GenericSurface`, in which case - :attr:`stability_margin` varies with its angle-of-attack argument.""" - self._ensure_margins() + """Whether every aerodynamic force on the rocket grows in proportion to + the angle of attack. + + ``True`` for a rocket built from nose cones, fins and tails. ``False`` + when a :class:`rocketpy.GenericSurface` has a force that does not, such + as a body-lift term; the stability margin then varies with the angle of + attack (see :meth:`neutral_point`). Checked up to 15 degrees, at Mach 0.3, 0.9 and 2. + """ + self._refresh_margins() return self._is_incidence_linear @property def cp_position(self): - """Alias for :attr:`aerodynamic_center`. Traditional center of pressure - position, defined as the linearized (small-incidence) center of pressure, - is the same as the aerodynamic center.""" + """Position of the rocket's center of pressure, in meters, as a function + of Mach number, in the user-defined rocket coordinate system. + + It is the point where the aerodynamic force acts at a small angle of + attack. The rocket is stable when it is behind the center of mass. Same + Function as :attr:`aerodynamic_center`. + """ return self.aerodynamic_center def evaluate_surfaces_cp_to_cdm(self): @@ -988,10 +1302,23 @@ def evaluate_surfaces_cp_to_cdm(self): self.__evaluate_single_surface_cp_to_cdm(surface, position) return self.surfaces_cp_to_cdm + def _surface_origin(self, surface, position): + """Where a surface's own frame sits, in the user's coordinate system: + the position it was added at, except for an individual fin, whose frame + sits at its root leading edge once the cant angle is applied. A fin + added at a position on the rocket axis (x = y = 0) sits on the body + surface at its angular position.""" + if isinstance(surface, Fin): + on_axis = position.x == 0 and position.y == 0 + return surface._compute_leading_edge_position( + position.z if on_axis else position, self._csys + ) + return position + def __evaluate_single_surface_cp_to_cdm(self, surface, position): - """Calculates the relative position of each aerodynamic surface - center of pressure to the rocket's center of dry mass in Body Axes - Coordinate System.""" + """Store where one surface applies its force, relative to the rocket's + center of dry mass, in the body axes.""" + position = self._surface_origin(surface, position) # position of the surfaces coordinate system origin in body frame pos_origin = Vector( [ @@ -1004,183 +1331,73 @@ def __evaluate_single_surface_cp_to_cdm(self, surface, position): # applies its resultant force at its center of pressure and transports # the moment geometrically; the surface-local application point is mapped # into the body frame by ``_rotation_surface_to_body`` - application_point = getattr( - surface, - "force_application_point", - Vector([surface.cpx, surface.cpy, surface.cpz]), - ) pos = ( - surface._rotation_surface_to_body @ application_point + pos_origin - ) # TODO: this should be recomputed whenever cant angle changes for fin - self.surfaces_cp_to_cdm[surface] = pos - - def _evaluate_is_incidence_linear(self): - """Detect whether the rocket's aerodynamics are linear in the angle of - attack, i.e. whether the neutral point moves as the angle of attack - changes. Returns ``True`` for a rocket built only from the linear - Barrowman surfaces and ``False`` when a surface's normal force is - nonlinear in incidence (for example a Galejs body-lift term added as a - :class:`rocketpy.GenericSurface`). - - The check central-differences the neutral point at zero and at five - degrees of incidence, in both the pitch and yaw planes; if either plane - moves, the rocket is treated as incidence-nonlinear. - """ - probe_mach = 0.3 - probe_alpha = math.radians(5.0) - for plane in ("pitch", "yaw"): - if plane == "yaw": - point_zero = neutral_point_and_slope(self, 0.0, 0.0, probe_mach, "yaw") - point_five = neutral_point_and_slope( - self, 0.0, probe_alpha, probe_mach, "yaw" - ) - else: - point_zero = neutral_point_and_slope( - self, 0.0, 0.0, probe_mach, "pitch" - ) - point_five = neutral_point_and_slope( - self, probe_alpha, 0.0, probe_mach, "pitch" - ) - if abs(point_five[0] - point_zero[0]) > 1e-6: - return False - return True - - def _neutral_point_margin_slope(self, incidence, mach, time, plane="pitch"): - """Stability margin (in calibers) and local normal-force-curve slope at a - single flow state, the shared computation behind ``stability_margin`` and - the flight dynamic-stability oscillator. - - For a rocket that is linear in incidence the neutral point is the - zero-incidence :attr:`aerodynamic_center` and ``incidence`` is ignored, - keeping the fast analytic path (and byte-for-byte the previous margin - values). Otherwise the neutral point and slope are found at ``incidence`` - by :func:`rocketpy.rocket.helpers.neutral_point_and_slope`, so a surface - that is nonlinear in the angle of attack (e.g. a Galejs body-lift - :class:`rocketpy.GenericSurface`) makes the margin move with incidence. - - Parameters - ---------- - incidence : float - Angle of attack (pitch) or sideslip angle (yaw), in radians. - mach : float - Free-stream Mach number. - time : float - Flight time, in seconds, at which the center of mass is taken. - plane : str, optional - ``"pitch"`` or ``"yaw"``. Default ``"pitch"``. - - Returns - ------- - tuple of float - ``(margin, slope)``: the stability margin in calibers and the local - force-curve slope (``dCN/dalpha`` for pitch, ``dCY/dbeta`` for yaw). - """ - self._ensure_margins() - if plane == "yaw": - center = self.aerodynamic_center_yaw - slope_curve = self.total_side_coeff_der - else: - center = self.aerodynamic_center - slope_curve = self.total_lift_coeff_der - - if self._is_incidence_linear: - neutral_point = center.get_value_opt(mach) - slope = slope_curve.get_value_opt(mach) - elif plane == "yaw": - neutral_point, slope = neutral_point_and_slope( - self, 0.0, incidence, mach, "yaw" - ) - else: - neutral_point, slope = neutral_point_and_slope( - self, incidence, 0.0, mach, "pitch" - ) - - margin = ( - self._csys - * (self.center_of_mass.get_value_opt(time) - neutral_point) - / (2 * self.radius) + surface._rotation_surface_to_body @ surface.force_application_point + + pos_origin ) - return margin, slope + self.surfaces_cp_to_cdm[surface] = pos def evaluate_stability_margin(self): - """Calculates the stability margin of the rocket as a function of angle - of attack, Mach number and time. + """Compute the stability margin of the rocket as a function of Mach + number and time. Returns ------- stability_margin : Function - Stability margin of the rocket, in calibers, as a function of angle - of attack (radians), Mach number and time. The stability margin is - the distance between the center of pressure and the center of mass, - divided by the rocket's diameter. It depends on the angle of attack - only when a surface is nonlinear in incidence; for a rocket built - from the linear Barrowman surfaces the angle-of-attack argument has - no effect. - """ - self._is_incidence_linear = self._evaluate_is_incidence_linear() + Stability margin of the rocket, in calibers, as a function of Mach + number and time (s), at zero angle of attack: the distance from the + center of mass to the center of pressure, divided by the rocket's + diameter. + """ + self._is_incidence_linear = is_incidence_linear(self) + self._uses_rate_coefficients = uses_rate_coefficients(self) self._stability_margin.set_source( - lambda alpha, mach, time: self._neutral_point_margin_slope( - alpha, mach, time, "pitch" + lambda mach, time: stability_margin_and_slope( + self, 0.0, 0.0, mach, time, "pitch" )[0] ) - self._stability_margin.set_inputs(["Angle of Attack (rad)", "Mach", "Time (s)"]) # Yaw-plane stability margin (equal to the pitch plane when axisymmetric) self._stability_margin_yaw.set_source( - lambda beta, mach, time: self._neutral_point_margin_slope( - beta, mach, time, "yaw" + lambda mach, time: stability_margin_and_slope( + self, 0.0, 0.0, mach, time, "yaw" )[0] ) - self._stability_margin_yaw.set_inputs( - ["Sideslip Angle (rad)", "Mach", "Time (s)"] - ) return self._stability_margin def evaluate_static_margin(self): - """Calculates the static margin of the rocket as a function of time. + """Compute the static margin of the rocket as a function of time. Returns ------- static_margin : Function - Static margin of the rocket, in calibers, as a function of time. - Static margin is defined as the distance between the center of - pressure and the center of mass, divided by the rocket's diameter. + Static margin of the rocket, in calibers, as a function of time + (s): the distance from the center of mass to the center of pressure + at zero airspeed (``cp_position`` at Mach 0), divided by the + rocket's diameter. """ - # Calculate static margin - self._static_margin.set_source( - lambda time: ( - ( - self.center_of_mass.get_value_opt(time) - - self.aerodynamic_center.get_value_opt(0) - ) - / (2 * self.radius) - ) - ) - # Change sign if coordinate system is upside down - self._static_margin *= self._csys - self._static_margin.set_inputs("Time (s)") - self._static_margin.set_outputs("Static Margin (c)") - self._static_margin.set_title("Static Margin") - self._static_margin.set_discrete( - lower=0, upper=self.motor.burn_out_time, samples=200 - ) - # Yaw-plane static margin (equal to the pitch plane when axisymmetric) - self._static_margin_yaw.set_source( - lambda time: ( + # The sign flip for an upside-down coordinate system is part of the + # formula, so each Function is updated in place and a reference kept by + # the user stays current. + def margin_about(center): + return lambda time: ( ( self.center_of_mass.get_value_opt(time) - - self.aerodynamic_center_yaw.get_value_opt(0) + - getattr(self, center).get_value_opt(0) ) / (2 * self.radius) + * self._csys ) - ) - self._static_margin_yaw *= self._csys - self._static_margin_yaw.set_inputs("Time (s)") - self._static_margin_yaw.set_outputs("Static Margin - Yaw (c)") - self._static_margin_yaw.set_title("Static Margin - Yaw") - self._static_margin_yaw.set_discrete( - lower=0, upper=self.motor.burn_out_time, samples=200 - ) + + for margin, center, label in ( + (self._static_margin, "aerodynamic_center", "Static Margin"), + (self._static_margin_yaw, "aerodynamic_center_yaw", "Static Margin - Yaw"), + ): + margin.set_source(margin_about(center)) + margin.set_inputs("Time (s)") + margin.set_outputs(f"{label} (c)") + margin.set_title(label) + margin.set_discrete(lower=0, upper=self.motor.burn_out_time, samples=200) return self._static_margin def evaluate_dry_inertias(self): @@ -1359,7 +1576,7 @@ def evaluate_nozzle_gyration_tensor(self): Matrix containing the nozzle gyration tensor. """ S_noz_33 = 0.5 * self.motor.nozzle_radius**2 - S_noz_11 = S_noz_22 = 0.5 * S_noz_33 + 0.25 * self.nozzle_to_cdm**2 + S_noz_11 = S_noz_22 = 0.5 * S_noz_33 + self.nozzle_to_cdm**2 S_noz_12, S_noz_13, S_noz_23 = 0, 0, 0 # Due to axis symmetry self.nozzle_gyration_tensor = Matrix( [ @@ -1512,8 +1729,6 @@ def add_motor(self, motor, position): # pylint: disable=too-many-statements self.evaluate_reduced_mass() self.evaluate_thrust_to_weight() self.evaluate_surfaces_cp_to_cdm() - # The motor changes the CM (and the margins) - self._margin_outdated = True self.evaluate_com_to_cdm_function() self.evaluate_nozzle_gyration_tensor() @@ -1521,20 +1736,7 @@ def __add_single_surface(self, surface, position): """Adds a single aerodynamic surface to the rocket. Makes checks for rail buttons case, and position type. """ - if isinstance(surface, (TrapezoidalFin, EllipticalFin, FreeFormFin)): - # TODO: the leading edge position should be recomputed whenever cant - # angle of the fin changes, but currently it is only computed at the - # moment the fin is added to the rocket. Detecting when the cant - # angle changes is hard, because it is a parameter of the fin, while - # the leading edge position is only defined on the rocket - position = surface._compute_leading_edge_position(position, self._csys) - else: - position = ( - Vector([0, 0, position]) - if not isinstance(position, (Vector, tuple, list)) - else Vector(position) - ) - + position = position_vector(position) if isinstance(surface, RailButtons): self.rail_buttons = Components() self.rail_buttons.add(surface, position) @@ -1544,14 +1746,14 @@ def __add_single_surface(self, surface, position): def add_surfaces(self, surfaces, positions): """Adds one or more aerodynamic surfaces to the rocket. The aerodynamic - surface must be an instance of a class that inherits from the - AeroSurface (e.g. NoseCone, TrapezoidalFins, etc.) + surface must be an instance of a class that inherits from + GenericSurface (e.g. NoseCone, TrapezoidalFins, etc.) Parameters ---------- - surfaces : list[AeroSurface], AeroSurface + surfaces : list[GenericSurface], GenericSurface Aerodynamic surface to be added to the rocket. Can be a list of - AeroSurface if more than one surface is to be added. + surfaces if more than one surface is to be added. positions : int, float, tuple, list, Vector Position(s) of the aerodynamic surface's reference point. Can be: @@ -1565,7 +1767,10 @@ def add_surfaces(self, surfaces, positions): For NoseCone type, position is the tip coordinate along the axis. For Fins type, position refers to the z-coordinate of the root chord leading-edge point closest to the nose cone, before any - cant-angle offset is considered. + cant-angle offset is considered. For an individual fin + (TrapezoidalFin, EllipticalFin, FreeFormFin), a single number + places that point on the body surface at the fin's angular + position; a full (x, y, z) is used as given. For Tail type, position is relative to the point belonging to the tail which is highest in the rocket coordinate system. For RailButtons type, position is relative to the lower rail button. @@ -1600,14 +1805,6 @@ def add_surfaces(self, surfaces, positions): else: self.__add_single_surface(surfaces, positions) - # Adding a surface changes both the aerodynamic center and the margins - self._cp_outdated = True - self._margin_outdated = True - # Re-arm the non-axisymmetry advisory: the warning is latched once per - # configuration (see evaluate_center_of_pressure), so a new surface may - # legitimately warn again about the new configuration. - self._axisymmetry_warned = False - def add_full_body_aerodynamics(self, surfaces, position=None, overwrite=False): """Add a prebuilt full-body aerodynamic surface: the whole rocket modeled as a single surface. Instead of (or in addition to) modeling @@ -1642,8 +1839,9 @@ def add_full_body_aerodynamics(self, surfaces, position=None, overwrite=False): position : int, float, optional Position along the rocket's center axis (in the user coordinate system) where the surface's resultant force is applied and about - which its moment coefficients are taken. Defaults to the center of - dry mass position. + which its moment coefficients are taken. Defaults to the rocket's + center of dry mass at the time of the call, so add the motor first + (before that, it is the center of mass without the motor). overwrite : bool, optional If ``True``, make this the rocket's only aerodynamics: every aerodynamic surface already on the rocket is removed first, and both @@ -1665,14 +1863,32 @@ def add_full_body_aerodynamics(self, surfaces, position=None, overwrite=False): surface_list = ( list(surfaces) if isinstance(surfaces, (list, tuple)) else [surfaces] ) + added = [] for surface in surface_list: + # pylint: disable-next=protected-access + yaw_is_zero = all( + getattr(surface, name).is_zero + for name in surface._get_default_coefficients() + if name.partition("_")[0] in ("cY", "cn") + ) + if yaw_is_zero: + warnings.warn( + f"'{surface.name}' has no yaw-plane coefficients (cY and cn " + "are zero): the rocket will have no side force or yaw moment " + "in flight. If the data describes an axisymmetric rocket, " + "build a LinearGenericSurface with axisymmetric=True, or " + "give the cN and cm of a GenericSurface against alpha_total.", + UserWarning, + stacklevel=2, + ) self.add_surfaces(surface, position) + added.append(surface) if overwrite: # Re-arm the "added after" guard now that the full-body model is set. self._aerodynamics_overwritten = True - return surfaces + return added if isinstance(surfaces, (list, tuple)) else added[0] def _clear_aerodynamic_surfaces(self): """Wipe the rocket's aerodynamics so a full-body model can fully replace @@ -1684,8 +1900,8 @@ def _clear_aerodynamic_surfaces(self): self.surfaces_cp_to_cdm.clear() # Clear both built-in drag curves; the supplied surface(s) now provide # the complete aerodynamics, including any drag they carry. - zero_drag(self, "power_on") - zero_drag(self, "power_off") + self._set_drag("power_on", 0) + self._set_drag("power_off", 0) warnings.warn( "add_full_body_aerodynamics(overwrite=True): the rocket's existing " "aerodynamic surfaces and both built-in drag curves (power_on_drag, " @@ -1694,156 +1910,307 @@ def _clear_aerodynamic_surfaces(self): UserWarning, stacklevel=3, ) - self._cp_outdated = True - self._margin_outdated = True - self._axisymmetry_warned = False # New adds are welcome again; the guard is re-armed once the full-body # surfaces are in place (see add_full_body_aerodynamics). self._aerodynamics_overwritten = False - def to_coefficients(self, machs=None, force_convention="body"): + def to_coefficients( + self, + machs=None, + force_convention="body", + model="linear", + angles=None, + rates=True, + reynolds=None, + controls=None, + ): """Return the whole rocket's aerodynamic coefficients, split by motor phase. - Sweeps the rocket's aerodynamic surfaces and lumps them into the - rocket's complete stability-derivative set about the dry center of mass: - the normal-force and pitch-moment slopes ``cN_alpha``/``cm_alpha`` - (pitch), the side-force and yaw-moment slopes ``cY_beta``/``cn_beta`` - (yaw), the pitch and yaw rate damping ``cN_q``/``cm_q`` and - ``cY_r``/``cn_r``, the fin roll damping ``cl_p`` and the drag ``cA_0``. + Sweeps the rocket's aerodynamic surfaces and lumps them into one set + of coefficients about the dry center of mass, in one of two forms: + + - ``model="linear"`` (default): the 36 derivatives a + :class:`LinearGenericSurface` takes. For each of the normal force + ``cN``, side force ``cY``, axial force ``cA``, pitch moment ``cm``, + yaw moment ``cn`` and roll moment ``cl``, its value at zero angle + and zero rates (``_0``) and its slopes with the angle of attack + (``_alpha``), the sideslip angle (``_beta``) and the reduced roll, + pitch and yaw rates (``_p``, ``_q``, ``_r``), each a curve over + Mach. Terms that are zero at every Mach number are left out, so an + ordinary rocket gets the familiar ``cN_alpha``, ``cm_alpha``, + ``cY_beta``, ``cn_beta``, ``cN_q``, ``cm_q``, ``cY_r``, ``cn_r``, + ``cl_p`` and ``cA_0``; canted fins add their roll forcing ``cl_0``, + and a rocket that is not axisymmetric keeps its cross terms + (``cN_beta``, ``cm_beta``, ...) and any force or moment it has at + zero angle. + - ``model="table"``: the six coefficients ``cN``, ``cY``, ``cA``, + ``cm``, ``cn`` and ``cl`` as tables over the angle of attack, the + sideslip angle and Mach, read at zero rates, which keep any curve + in the angles (a stall, a body-lift term, the cross coupling of a + lopsided rocket). For an axisymmetric rocket the sweep is over the + total angle of attack and Mach only: ``cN``, ``cA``, ``cm`` and + ``cl`` are then tables of ``alpha_total`` and ``mach``, and ``cY`` + and ``cn`` are left out since they follow from ``cN`` and ``cm`` + by the split along the crossflow (see + :class:`GenericSurface`). + The rate terms are added as in the linear model, ``cN_q``, + ``cm_q``, ``cY_r``, ``cn_r``, ``cl_p`` and any other non-zero one, + each a curve over Mach. The result is returned as two coefficient sets, ``"power_off"`` - (coasting) and ``"power_on"`` (motor burning). + (coasting) and ``"power_on"`` (motor burning). Each is built from the + surfaces active during its phase (a surface's ``active_during``) and + that phase's drag curve. The axial coefficient is the rocket's drag + coefficient of the phase plus the axial force of the surfaces. Important --------- - The resulting coefficients are a **linear summary tabulated only against - Mach**: the derivatives are taken at zero angle of attack, zero sideslip - and zero rates, so only their Mach dependence is kept. This leaves out: - - - **Incidence and rate nonlinearity.** Only the slope at zero is - retained, so any curvature in angle of attack, sideslip or the body - rates is not represented. - - **Reynolds dependence.** The derivatives are measured in the - vanishing-Reynolds limit, so a coefficient that varies with Reynolds - number is frozen at that value rather than following the flight - Reynolds number. - - **Control-surface dependence.** Deflection axes of a - :class:`ControllableGenericSurface` are not carried into the summary. - - **Induced drag.** The axial coefficient is constant in incidence, so - drag does not increase with angle of attack. - - These are exactly the assumptions of RocketPy's built-in Barrowman - surfaces (:class:`NoseCone`, :class:`Tail` and the fin sets), which are - already linear, Mach-tabulated and Reynolds-independent. A rocket built - only from them is therefore reproduced exactly, with nothing lost. The - limitations matter only when you have added a :class:`GenericSurface` or - :class:`ControllableGenericSurface` (or a Reynolds-dependent - :class:`LinearGenericSurface`) whose coefficients truly vary with - incidence beyond a straight line, with Reynolds number, or with a - control deflection. + By default the coefficients depend on Mach number (and, for the table + model, on the angles) only. Three things are then held fixed, and each + can be included with an optional argument: + + - **Reynolds number.** Held at zero unless ``reynolds`` is given. + - **Control deflections.** Each control of a + :class:`ControllableGenericSurface` is held at its current value + unless it is listed in ``controls``. + - **How the rate terms change with the angles.** The rate terms are + read at zero angle unless ``rates="at_each_angle"`` (table model). + In every case the effect of a rate is a straight line: the rate term + times the rate. + + Each value added to ``reynolds`` or ``controls`` multiplies the number + of points computed, so keep those lists short. + + The linear model also keeps only the slope at zero angle, so any + curve in the angle of attack or sideslip is lost, the drag rise with + angle of attack (induced drag) among them. The table model keeps those + curves within the range of ``angles`` and holds the edge value beyond + it. + + RocketPy's built-in Barrowman surfaces (:class:`NoseCone`, + :class:`Tail`, the fin sets and the individual fins, canted or not) + are linear, Mach-tabulated and Reynolds-independent, so a rocket built + only from them is reproduced exactly by the linear model to first + order in the angles, and by the table model at every angle of an + axisymmetric rocket. The optional arguments matter when you have added + a :class:`GenericSurface` or :class:`ControllableGenericSurface` (or a + Reynolds-dependent :class:`LinearGenericSurface`) whose coefficients + vary with Reynolds number, with a control deflection or, for the + linear model, with the angles beyond a straight line. Parameters ---------- machs : sequence of float, optional - Mach numbers at which the derivatives are sampled and tabulated. - Defaults to ``0`` to ``3`` in steps of ``0.02``. + Mach numbers at which the coefficients are read and tabulated. + Defaults to ``0`` to ``3`` in steps of ``0.02`` for the linear + model and of ``0.05`` for the table model. force_convention : str, optional The frame the force coefficients are named in. ``"body"`` (default) - gives the body-frame set : normal ``cN_*``, side ``cY_*`` and axial - ``cA_0`` (drag). ``"wind"`` gives the wind-frame set: lift - ``cL_*``, side ``cQ_*`` and drag ``cD_0``. The moment derivatives - (``cm_*``, ``cn_*``, ``cl_p``) are the same in both. + gives the body-frame set: normal ``cN_*``, side ``cY_*`` and axial + ``cA_*``. ``"wind"`` gives the wind-frame set: lift ``cL_*``, side + ``cQ_*`` and drag ``cD_*``. The moment derivatives (``cm_*``, + ``cn_*``, ``cl_*``) are the same in both. The table model gives + the body frame only. + model : str, optional + ``"linear"`` (default) for the derivatives, ``"table"`` for the + tables over the angles. See above. + angles : sequence of float, optional + Angles, in radians, the table model is read at, used for both the + angle of attack and the sideslip angle (for an axisymmetric + rocket, their absolute values give the total angles of attack). + Defaults to -30 to 30 degrees every 2 degrees, that is + ``np.radians(np.arange(-30, 31, 2))``. Widen it for a surface + that stalls beyond that. Ignored by the linear model. + rates : bool or str, optional + How the rate terms are included: + + - ``True`` (default): each rate term is read at zero angle of + attack and sideslip. + - ``"at_each_angle"``: table model only. Each rate term is read at + every angle of the table, so damping that changes with the + angle of attack is kept. The rate terms are then tables like the + coefficients, and the rocket is swept over both the angle of + attack and the sideslip angle even when it is axisymmetric. It + takes about seven times as long as ``True``. + - ``False``: no rate terms. The linear model then has no ``_p``, + ``_q`` or ``_r`` terms and the table model holds the static + tables only, which describe the rocket as a wind tunnel does, + held still, with no aerodynamic damping at all. + reynolds : float or sequence of float, optional + Reynolds number of the rocket, based on its diameter + (``air density * airspeed * 2 * radius / air viscosity``): + + - ``None`` (default): the coefficients are read at zero Reynolds + number. + - a single number: the coefficients are read at that Reynolds + number, for example ``1e6``. + - a list: the coefficients are read at each value and gain + ``reynolds`` as an input, for example ``[1e5, 1e6, 1e7]``. + + It only has an effect when a surface or a drag curve of the rocket + depends on the Reynolds number. + controls : dict, optional + Controls to keep as inputs of the coefficients, as a dict from the + name of a control of a :class:`ControllableGenericSurface` to the + values it is read at (at least two), for example + ``{"deflection": np.radians([-10, 0, 10])}``. Each control listed + becomes an input with that name. A control that is not listed is + held at its current value. The controls of the rocket's surfaces + are left as they were. + + When several surfaces use the same control name, each is kept as + a separate input named ``_`` (in lower + case, with ``_`` for spaces). Giving the shared name reads all of + them at the same values and shows a warning with the names; give + those names to choose the values of each. With a control listed, + the table model sweeps both the angle of attack and the sideslip + angle. Default ``None``. Returns ------- dict A dict with keys ``"power_off"`` and ``"power_on"``. Each value is - itself a dict mapping a coefficient name to its curve over Mach (a - :class:`rocketpy.Function`). The body-frame set is ``cN_alpha``, - ``cm_alpha``, ``cN_q``, ``cm_q``, ``cY_beta``, ``cn_beta``, - ``cY_r``, ``cn_r``, ``cl_p`` and ``cA_0``. + itself a dict mapping a coefficient name to a + :class:`rocketpy.Function`. Its inputs are, in this order: the + angles (table model only: ``alpha_total``, or ``alpha`` and + ``beta``), ``mach``, ``reynolds`` (when a list was given) and one + per control listed. A derivative of the linear model and a rate + term read at zero angle have no angle inputs. + + Examples + -------- + Coefficients as tables over the angles, Mach and three Reynolds + numbers: + + >>> coefficients = rocket.to_coefficients( # doctest: +SKIP + ... model="table", reynolds=[1e5, 1e6, 1e7] + ... ) + >>> cN = coefficients["power_off"]["cN"] # doctest: +SKIP + >>> cN(0.05, 0.6, 1e6) # alpha_total, mach, reynolds # doctest: +SKIP """ - return full_body_coefficients(self, machs, force_convention) + return full_body_coefficients( + self, machs, force_convention, model, angles, rates, reynolds, controls + ) def to_surface( self, machs=None, force_convention="body", name="Full Body Aerodynamics", + model="linear", + angles=None, + rates=True, + reynolds=None, + controls=None, ): - """Collapse the whole assembled rocket aerodynamics into two - :class:`rocketpy.LinearGenericSurface` objects, one for coasting and one - for powered flight. It reproduces the source rocket's aerodynamics, so a - bare rocket carrying the same body and motor plus this pair flies the - same as the fully modeled rocket. + """Collapse the whole assembled rocket aerodynamics into two surfaces, + one for coasting and one for powered flight. It reproduces the source + rocket's aerodynamics, so a bare rocket carrying the same body and + motor plus this pair flies the same as the fully modeled rocket. Important --------- - The resulting coefficients are a **linear summary tabulated only against - Mach**: the derivatives are taken at zero angle of attack, zero sideslip - and zero rates, so only their Mach dependence is kept. This leaves out: - - - **Incidence and rate nonlinearity.** Only the slope at zero is - retained, so any curvature in angle of attack, sideslip or the body - rates is not represented. - - **Reynolds dependence.** The derivatives are measured in the - vanishing-Reynolds limit, so a coefficient that varies with Reynolds - number is frozen at that value rather than following the flight - Reynolds number. - - **Control-surface dependence.** Deflection axes of a - :class:`ControllableGenericSurface` are not carried into the summary. - - **Induced drag.** The axial coefficient is constant in incidence, so - drag does not increase with angle of attack. - - These are exactly the assumptions of RocketPy's built-in Barrowman - surfaces (:class:`NoseCone`, :class:`Tail` and the fin sets), which are - already linear, Mach-tabulated and Reynolds-independent. A rocket built - only from them is therefore reproduced exactly, with nothing lost. The - limitations matter only when you have added a :class:`GenericSurface` or - :class:`ControllableGenericSurface` (or a Reynolds-dependent - :class:`LinearGenericSurface`) whose coefficients truly vary with - incidence beyond a straight line, with Reynolds number, or with a - control deflection. + With ``model="linear"`` (default) the surfaces are + :class:`rocketpy.LinearGenericSurface` objects holding a linear summary + of the rocket, tabulated against Mach. With ``model="table"`` they are + :class:`rocketpy.GenericSurface` objects holding the coefficients as + tables over the angles and Mach, which keep any curve in the angles. + See :meth:`to_coefficients` for what each model keeps and leaves out. Parameters ---------- machs : sequence of float, optional - Mach numbers at which the derivatives are sampled and tabulated. - Defaults to ``0`` to ``3`` in steps of ``0.02``. + Mach numbers at which the coefficients are read and tabulated. + Defaults to ``0`` to ``3`` in steps of ``0.02`` for the linear + model and of ``0.05`` for the table model. force_convention : str, optional The frame the force coefficients are expressed in. ``"body"`` (default) gives the body-frame set: normal ``cN``, side ``cY`` and axial ``cA`` (drag). ``"wind"`` gives the wind-frame set: lift ``cL``, side ``cQ`` and drag ``cD``. The moment coefficients are the - same in both. + same in both. The table model gives the body frame only. name : str, optional Base name of the returned surfaces. Default ``"Full Body Aerodynamics"``. + model : str, optional + ``"linear"`` (default) or ``"table"``. See :meth:`to_coefficients`. + angles : sequence of float, optional + Angles, in radians, the table model is read at. See + :meth:`to_coefficients`. + rates : bool or str, optional + ``True`` (default), ``False`` or ``"at_each_angle"``: whether and + how the surfaces carry the rate (damping) terms. See + :meth:`to_coefficients`. + reynolds : float or sequence of float, optional + Reynolds number of the rocket, based on its diameter: a single + value to read the coefficients at, or a list to make the surfaces + follow the Reynolds number of the flight. Default ``None`` (zero). + See :meth:`to_coefficients`. + controls : dict, optional + Controls the surfaces keep, from control name to the values it is + read at, for example ``{"deflection": np.radians([-10, 0, 10])}``. + Needs ``model="table"``; the surfaces returned are then + :class:`rocketpy.ControllableGenericSurface` objects with those + controls, each starting at 0. Default ``None``. See + :meth:`to_coefficients`. Returns ------- - list of rocketpy.LinearGenericSurface + list of rocketpy.LinearGenericSurface, rocketpy.GenericSurface or \ +rocketpy.ControllableGenericSurface Two surfaces, ``[power_off, power_on]``, each carrying the whole - rocket's coefficient derivatives referenced to the rocket - cross-section area and diameter and taken about the center of dry - mass, and gated to its motor phase. + rocket's coefficients referenced to the rocket cross-section area + and diameter and taken about the center of dry mass, and gated to + its motor phase. """ + if controls and model != "table": + raise ValueError( + "A surface that keeps controls needs model='table'; the linear " + "model has no control inputs." + ) coefficients = self.to_coefficients( machs=machs, force_convention=force_convention, + model=model, + angles=angles, + rates=rates, + reynolds=reynolds, + controls=controls, ) - return [ - LinearGenericSurface( - reference_area=self.area, - reference_length=2 * self.radius, - coefficients=coefficients[phase], - force_convention=force_convention, - name=f"{name} ({phase.replace('_', ' ')})", - active_during=phase, - ) - for phase in ("power_off", "power_on") - ] + surfaces = [] + for phase in ("power_off", "power_on"): + label = f"{name} ({phase.replace('_', ' ')})" + if model == "table" and controls: + surface = ControllableGenericSurface( + reference_area=self.area, + reference_length=2 * self.radius, + coefficients=lumped_surface_coefficients(coefficients[phase]), + controls=lumped_control_names(coefficients[phase]), + force_convention="body", + name=label, + active_during=phase, + ) + elif model == "table": + surface = GenericSurface( + reference_area=self.area, + reference_length=2 * self.radius, + coefficients=lumped_surface_coefficients(coefficients[phase]), + force_convention="body", + name=label, + active_during=phase, + ) + else: + surface = LinearGenericSurface( + reference_area=self.area, + reference_length=2 * self.radius, + coefficients=coefficients[phase], + force_convention=force_convention, + name=label, + active_during=phase, + ) + surfaces.append(surface) + return surfaces def _add_controllers(self, controllers): """Adds a controller to the rocket. @@ -1972,7 +2339,7 @@ def add_nose( @deprecated( reason="This method is set to be deprecated in version 1.0.0 and fully " - "removed by version 1.14.0", + "removed by version 1.16.0", alternative="Rocket.add_trapezoidal_fins", ) def add_fins(self, *args, **kwargs): # pragma: no cover @@ -1982,6 +2349,17 @@ def add_fins(self, *args, **kwargs): # pragma: no cover same arguments and signature.""" return self.add_trapezoidal_fins(*args, **kwargs) + @staticmethod + def _check_fin_set_count(n): + """Raise a ``ValueError`` unless a fin set has more than 2 fins.""" + if n <= 2: + raise ValueError( + "Number of fins must be greater than 2. " + "For 1 or 2 fins, create each fin as a TrapezoidalFin, " + "EllipticalFin or FreeFormFin object and add it to the rocket " + "using the add_surfaces method." + ) + def add_trapezoidal_fins( self, n, @@ -2061,12 +2439,7 @@ def add_trapezoidal_fins( fin_set : TrapezoidalFins Fin set object created. """ - if n <= 2: - raise ValueError( - "Number of fins must be greater than 2. " - "For 1 or 2 fins, create a FreeFormFin object " - "and add it to the rocket using the add_surfaces method." - ) + self._check_fin_set_count(n) # Modify radius if not given, use rocket radius, otherwise use given. radius = radius if radius is not None else self.radius @@ -2153,12 +2526,7 @@ def add_elliptical_fins( fin_set : EllipticalFins Fin set object created. """ - if n <= 2: - raise ValueError( - "Number of fins must be greater than 2. " - "For 1 or 2 fins, create a FreeFormFin object " - "and add it to the rocket using the add_surfaces method." - ) + self._check_fin_set_count(n) radius = radius if radius is not None else self.radius fin_set = EllipticalFins(n, root_chord, span, radius, cant_angle, airfoil, name) @@ -2226,12 +2594,7 @@ def add_free_form_fins( fin_set : FreeFormFins Fin set object created. """ - if n <= 2: - raise ValueError( - "Number of fins must be greater than 2. " - "For 1 or 2 fins, create a FreeFormFin object " - "and add it to the rocket using the add_surfaces method." - ) + self._check_fin_set_count(n) # Modify radius if not given, use rocket radius, otherwise use given. radius = radius if radius is not None else self.radius @@ -2592,10 +2955,6 @@ def controller_wrapper(context): sampling_rate, context["state"], controller_memory.get("observed_variables", []), - # The controller's live controlled objects rather than - # the captured ``air_brakes``: after a flight is loaded - # from a file the two differ, and only the former is the - # air brakes the rocket actually uses for drag. context["controlled_objects"], context["sensors"], context["environment"], @@ -2715,6 +3074,8 @@ def add_cm_eccentricity(self, x, y): """ self.cm_eccentricity_x = x self.cm_eccentricity_y = y + # The center of mass moved sideways: so did every surface relative to it + self._refresh_aerodynamics() self.add_cp_eccentricity(-x, -y) self.add_thrust_eccentricity(-x, -y) return self @@ -2835,6 +3196,21 @@ def all_info(self): # pylint: disable=too-many-statements def to_dict(self, **kwargs): + """Return the rocket as a dictionary, to save it and rebuild it later. + + Parameters + ---------- + **kwargs + ``include_outputs`` (bool, default False) also saves the values the + rocket computes, such as its mass and margins over time, and + ``discretize`` (bool, default False) saves those as tables sampled + over the motor's burn instead of as functions. + + Returns + ------- + dict + The arguments needed to rebuild the rocket with :meth:`from_dict`. + """ discretize = kwargs.get("discretize", False) power_off_drag = self.power_off_drag_7d @@ -2842,6 +3218,7 @@ def to_dict(self, **kwargs): rocket_dict = { "radius": self.radius, + "length": self._length, "mass": self.mass, "I_11_without_motor": self.I_11_without_motor, "I_22_without_motor": self.I_22_without_motor, @@ -2853,6 +3230,7 @@ def to_dict(self, **kwargs): "power_on_drag": power_on_drag, "center_of_mass_without_motor": self.center_of_mass_without_motor, "coordinate_system_orientation": self.coordinate_system_orientation, + "stability_phase": self.stability_phase, "motor": self.motor, "motor_position": self.motor_position, "aerodynamic_surfaces": self.aerodynamic_surfaces, @@ -2864,26 +3242,28 @@ def to_dict(self, **kwargs): } if kwargs.get("include_outputs", False): - thrust_to_weight = self.thrust_to_weight aerodynamic_center = self.aerodynamic_center - # The zero-incidence design surface (Mach, time), a 2-D slice of the - # angle-of-attack-aware stability_margin, for output inspection. - stability_margin = Function( - lambda mach, time: self.stability_margin.get_value_opt(0.0, mach, time), - inputs=["Mach", "Time (s)"], - outputs="Stability Margin (c)", - ) - center_of_mass = self.center_of_mass - motor_center_of_mass_position = self.motor_center_of_mass_position - reduced_mass = self.reduced_mass - total_mass = self.total_mass - total_mass_flow_rate = self.total_mass_flow_rate - center_of_propellant_position = self.center_of_propellant_position - - if discretize: - thrust_to_weight = thrust_to_weight.set_discrete_based_on_model( - self.motor.thrust, mutate_self=False + stability_margin = self.stability_margin + # Functions of time while the motor burns + timed = { + name: getattr(self, name) + for name in ( + "thrust_to_weight", + "center_of_mass", + "motor_center_of_mass_position", + "reduced_mass", + "total_mass", + "total_mass_flow_rate", + "center_of_propellant_position", ) + } + if discretize: + timed = { + name: function.set_discrete_based_on_model( + self.motor.thrust, mutate_self=False + ) + for name, function in timed.items() + } aerodynamic_center = aerodynamic_center.set_discrete( 0, 4, 25, mutate_self=False ) @@ -2893,45 +3273,15 @@ def to_dict(self, **kwargs): (10, 10), mutate_self=False, ) - center_of_mass = center_of_mass.set_discrete_based_on_model( - self.motor.thrust, mutate_self=False - ) - motor_center_of_mass_position = ( - motor_center_of_mass_position.set_discrete_based_on_model( - self.motor.thrust, mutate_self=False - ) - ) - reduced_mass = reduced_mass.set_discrete_based_on_model( - self.motor.thrust, mutate_self=False - ) - total_mass = total_mass.set_discrete_based_on_model( - self.motor.thrust, mutate_self=False - ) - total_mass_flow_rate = total_mass_flow_rate.set_discrete_based_on_model( - self.motor.thrust, mutate_self=False - ) - center_of_propellant_position = ( - center_of_propellant_position.set_discrete_based_on_model( - self.motor.thrust, mutate_self=False - ) - ) + rocket_dict.update(timed) rocket_dict["area"] = self.area rocket_dict["center_of_dry_mass_position"] = ( self.center_of_dry_mass_position ) - rocket_dict["center_of_mass_without_motor"] = ( - self.center_of_mass_without_motor - ) - rocket_dict["motor_center_of_mass_position"] = motor_center_of_mass_position rocket_dict["motor_center_of_dry_mass_position"] = ( self.motor_center_of_dry_mass_position ) - rocket_dict["center_of_mass"] = center_of_mass - rocket_dict["reduced_mass"] = reduced_mass - rocket_dict["total_mass"] = total_mass - rocket_dict["total_mass_flow_rate"] = total_mass_flow_rate - rocket_dict["thrust_to_weight"] = thrust_to_weight rocket_dict["cp_eccentricity_x"] = self.cp_eccentricity_x rocket_dict["cp_eccentricity_y"] = self.cp_eccentricity_y rocket_dict["thrust_eccentricity_x"] = self.thrust_eccentricity_x @@ -2942,12 +3292,24 @@ def to_dict(self, **kwargs): rocket_dict["nozzle_position"] = self.nozzle_position rocket_dict["nozzle_to_cdm"] = self.nozzle_to_cdm rocket_dict["nozzle_gyration_tensor"] = self.nozzle_gyration_tensor - rocket_dict["center_of_propellant_position"] = center_of_propellant_position return rocket_dict @classmethod def from_dict(cls, data): + """Rebuild a rocket saved with :meth:`to_dict`. + + Parameters + ---------- + data : dict + The dictionary returned by :meth:`to_dict`. + + Returns + ------- + Rocket + The rocket, with its motor, surfaces, rail buttons, parachutes, + sensors, air brakes and controllers. + """ rocket = cls( radius=data["radius"], mass=data["mass"], @@ -2963,7 +3325,9 @@ def from_dict(cls, data): power_on_drag=data["power_on_drag"], center_of_mass_without_motor=data["center_of_mass_without_motor"], coordinate_system_orientation=data["coordinate_system_orientation"], + length=data.get("length"), ) + rocket.stability_phase = data.get("stability_phase", "power_off") if (motor := data["motor"]) is not None: rocket.add_motor( diff --git a/rocketpy/simulation/events/event.py b/rocketpy/simulation/events/event.py index 72d39c22f..9ec1e54dd 100644 --- a/rocketpy/simulation/events/event.py +++ b/rocketpy/simulation/events/event.py @@ -17,7 +17,7 @@ class Event: - """A rule that runs an action during a flight when a condition is met. + """Event helper with trigger/callback execution and exact-time support. An ``Event`` is the main way RocketPy reacts to conditions during a flight. It pairs a ``trigger`` predicate with a ``callback`` action: at diff --git a/rocketpy/simulation/flight.py b/rocketpy/simulation/flight.py index 06ce57eaf..062df1261 100644 --- a/rocketpy/simulation/flight.py +++ b/rocketpy/simulation/flight.py @@ -433,12 +433,29 @@ class Flight: # pylint: disable=too-many-instance-attributes, too-many-public-m of frequency in Hz. Flight.static_margin : Function Rocket's static margin during flight in calibers. + Flight.static_margin_yaw : Function + Rocket's static margin in the yaw plane during flight, in calibers. + Equals ``static_margin`` for an axisymmetric rocket. Flight.stability_margin : Function - Rocket's stability margin during flight, in calibers. + Rocket's stability margin during flight, in calibers. + Flight.stability_margin_yaw : Function + Rocket's stability margin in the yaw plane during flight, in calibers. + Equals ``stability_margin`` for an axisymmetric rocket built from nose + cones, fins and tails. Flight.initial_stability_margin : float Rocket's initial stability margin in calibers. Flight.out_of_rail_stability_margin : float Rocket's stability margin in calibers when it leaves the rail. + Flight.initial_stability_margin_yaw : float + Rocket's initial stability margin in the yaw plane, in calibers. + Flight.out_of_rail_stability_margin_yaw : float + Rocket's stability margin in the yaw plane when it leaves the rail, in + calibers. + Flight.max_stability_margin_yaw, Flight.min_stability_margin_yaw : float + Largest and smallest stability margin in the yaw plane, in calibers, + reached at ``max_stability_margin_yaw_time`` and + ``min_stability_margin_yaw_time``. The yaw values equal the pitch ones + for an axisymmetric rocket linear in the angle of attack. Flight.stream_velocity_x : Function Freestream velocity x (East) component, in m/s, as a function of time. @@ -664,6 +681,7 @@ class for more details. Default is None. self.custom_events = [] if custom_events is None else custom_events # Flight initialization + self.rocket._refresh_aerodynamics() self.__init_events() self.__init_solution_monitors() self.__init_equations_of_motion() @@ -2019,6 +2037,52 @@ def out_of_rail_stability_margin(self): """ return self.stability_margin.get_value_opt(self.out_of_rail_time) + @cached_property + def max_stability_margin_yaw_time(self): + """Time of maximum yaw-plane stability margin.""" + index = np.argmax(self.stability_margin_yaw[:, 1]) + return self.stability_margin_yaw[index, 0] + + @cached_property + def max_stability_margin_yaw(self): + """Maximum yaw-plane stability margin.""" + return self.stability_margin_yaw.get_value_opt( + self.max_stability_margin_yaw_time + ) + + @cached_property + def min_stability_margin_yaw_time(self): + """Time of minimum yaw-plane stability margin.""" + index = np.argmin(self.stability_margin_yaw[:, 1]) + return self.stability_margin_yaw[index, 0] + + @cached_property + def min_stability_margin_yaw(self): + """Minimum yaw-plane stability margin.""" + return self.stability_margin_yaw.get_value_opt( + self.min_stability_margin_yaw_time + ) + + @property + def initial_stability_margin_yaw(self): + """Yaw-plane stability margin at time 0. + + Returns + ------- + float + """ + return self.stability_margin_yaw.get_value_opt(self.time[0]) + + @property + def out_of_rail_stability_margin_yaw(self): + """Yaw-plane stability margin at the time the rocket leaves the rail. + + Returns + ------- + float + """ + return self.stability_margin_yaw.get_value_opt(self.out_of_rail_time) + # Reynolds Number @funcify_method("Time (s)", "Reynolds Number", "spline", "zero") def reynolds_number(self): @@ -2327,174 +2391,159 @@ def attitude_frequency_response(self): @cached_property def static_margin(self): - """Static margin of the rocket (linear, zero-airspeed reference).""" + """Static margin of the rocket, in calibers, as a function of time (s): + the distance from the center of mass to the center of pressure at zero + airspeed, divided by the rocket's diameter. Same as + :attr:`rocketpy.Rocket.static_margin`. + """ return self.rocket.static_margin - def _incidence(self, time): - """Total angle of attack, in radians (unsigned), used as the incidence - for the stability quantities. This is exact for a crosswind in the pitch - plane and a close approximation for combined pitch-and-yaw incidence.""" - return abs(np.radians(self.angle_of_attack.get_value_opt(time))) + @cached_property + def static_margin_yaw(self): + """Static margin of the rocket in the yaw plane, in calibers, as a + function of time (s). Equals :attr:`static_margin` for an axisymmetric + rocket. + """ + return self.rocket.static_margin_yaw + + def _stability_state(self): + """Angle of attack and sideslip angle, in radians over ``self.time``, + at which the margin, restoring slope and damping of *both* planes are + read at each instant. + + The linearization of one plane depends on the angle in the other when + a surface is nonlinear in the angle, so both planes are read at the + same state. For an axisymmetric rocket that state is taken in the + plane of the wind, ``(total angle of attack, 0)``: the pitch plane then + describes a motion in the wind plane (tangent slope) and the yaw plane + a motion across it (secant slope, the classic ``Cm/CN`` center of + pressure). Otherwise the state is the rocket's actual partial angles + ``(alpha, beta)``. For a rocket linear in the angle the state does not + matter. + + While the rail holds the rocket its incidence is not a free-flight + state (at ignition the wind alone sets it, near 90 degrees), so on the + rail both angles are taken as zero and the margin is the conventional + zero-angle value. + """ + if self.rocket.is_axisymmetric: + alpha = np.radians(self.angle_of_attack.y_array) + beta = np.zeros_like(alpha) + else: + alpha = np.radians(self.partial_angle_of_attack.y_array) + beta = np.radians(self.angle_of_sideslip.y_array) + on_rail = self.time < self.out_of_rail_time + alpha = np.where(on_rail, 0.0, alpha) + beta = np.where(on_rail, 0.0, beta) + return alpha, beta + + def _margin_along_flight(self, plane): + """Stability margin of ``plane`` at every instant, at the flight's + stability state, Mach number and time.""" + # Imported here: the rocket package cannot be imported while this + # module loads (circular import through the controllers) + # pylint: disable-next=import-outside-toplevel + from ..rocket._helpers import stability_margin_and_slope + + alpha, beta = self._stability_state() + return np.array( + [ + stability_margin_and_slope( + self.rocket, a, b, self.mach_number.get_value_opt(t), t, plane + )[0] + for a, b, t in zip(alpha, beta, self.time) + ] + ) @funcify_method("Time (s)", "Stability Margin (c)", "linear", "zero") def stability_margin(self): - """Pitch-plane stability margin along the flight, in calibers. - - This samples the rocket's own :attr:`Rocket.stability_margin` along the - flight's realized angle of attack, Mach number and time. It therefore - accounts for the center-of-mass shift as propellant burns, the variation - of the aerodynamic center with Mach number, and, when a surface is - nonlinear in incidence (e.g. a Galejs body-lift - :class:`rocketpy.GenericSurface`), the migration of the neutral point - with the flight angle of attack. In that last case the margin oscillates - as the angle of attack oscillates; for a rocket built only from the - linear Barrowman surfaces the angle of attack has no effect and this - reduces to the Mach-and-time margin. - - For non-axisymmetric rockets, this represents the pitch plane. + """Stability margin of the rocket along the flight, in calibers: the + distance from the center of mass to the neutral point, divided by the + rocket's diameter, at the angle of attack, Mach number and time of each + instant. Returns ------- stability : rocketpy.Function Stability margin in calibers as a function of time. """ - rocket_margin = self.rocket.stability_margin - margin = np.array( - [ - rocket_margin.get_value_opt( - self._incidence(t), self.mach_number.get_value_opt(t), t - ) - for t in self.time - ] - ) - return np.column_stack((self.time, margin)) + return np.column_stack((self.time, self._margin_along_flight("pitch"))) @funcify_method("Time (s)", "Stability Margin - Yaw (c)", "linear", "zero") def stability_margin_yaw(self): - """Yaw-plane stability margin along the flight, in calibers. - - Yaw-plane counterpart of :meth:`stability_margin`, sampling the rocket's - :attr:`Rocket.stability_margin_yaw` along the flight. Equals - :meth:`stability_margin` for an axisymmetric rocket. For a - non-axisymmetric rocket (e.g. single-plane canards) it differs, since the - pitch and yaw aerodynamic centers no longer coincide. + """Stability margin of the rocket in the yaw plane along the flight, in + calibers. Equals :meth:`stability_margin` for an axisymmetric rocket + built from nose cones, fins and tails. Returns ------- stability : rocketpy.Function Yaw-plane stability margin in calibers as a function of time. """ - rocket_margin = self.rocket.stability_margin_yaw - margin = np.array( - [ - rocket_margin.get_value_opt( - self._incidence(t), self.mach_number.get_value_opt(t), t - ) - for t in self.time - ] - ) - return np.column_stack((self.time, margin)) - - # Dynamic stability. The equations behind the two methods below (the lateral - # inertia and the linearized oscillator) are documented in - # docs/technical/aerodynamics/center_of_pressure_and_stability.rst. - def _lateral_inertia(self, dry_lateral_inertia, motor_lateral_inertia): - """Lateral moment of inertia about the instantaneous center of mass, as - an array over ``self.time``. Uses the reduced-mass formulation of the - equations of motion: ``I_L = I_dry + I_motor(t) + mu(t) b^2`` with - ``mu`` the dry/propellant reduced mass and ``b`` the (initial) - dry-mass-to-propellant distance.""" - dry_mass = self.rocket.dry_mass - b = ( - -( - self.rocket.center_of_propellant_position.get_value_opt(0) - - self.rocket.center_of_dry_mass_position - ) - * self.rocket._csys - ) + return np.column_stack((self.time, self._margin_along_flight("yaw"))) + + # Dynamic stability. The equations behind the two methods below are documented in + # docs/technical/aerodynamics/center_of_pressure_and_stability.rst + def _lateral_inertia(self, inertia_about_cdm): + """Lateral moment of inertia about the center of mass along the flight, + from the rocket's ``I_11`` or ``I_22``, and its rate of change, as two + arrays over ``self.time``. The rate is negative while propellant is + consumed and enters the jet damping.""" + if not isinstance(inertia_about_cdm, Function): + # A point mass rocket has no inertia, and no attitude to oscillate + return np.zeros(len(self.time)), np.zeros(len(self.time)) + # Imported here: the rocket package cannot be imported while this + # module loads (circular import through the controllers) + # pylint: disable-next=import-outside-toplevel + from ..rocket._helpers import lateral_inertia_and_rate + inertia = np.empty(len(self.time)) + rate = np.empty(len(self.time)) for i, t in enumerate(self.time): - propellant_mass = self.rocket.motor.propellant_mass.get_value_opt(t) - total = propellant_mass + dry_mass - mu = (propellant_mass * dry_mass / total) if total > 0 else 0.0 - inertia[i] = ( - dry_lateral_inertia + motor_lateral_inertia.get_value_opt(t) + mu * b**2 + inertia[i], rate[i] = lateral_inertia_and_rate( + self.rocket, inertia_about_cdm, t ) - return inertia - - def _dynamic_stability(self, plane, lateral_inertia): - """Linearized oscillator coefficients for one plane, as arrays over - ``self.time``: corrective moment coefficient ``C1`` (restoring moment - per radian), damping moment coefficient ``C2`` (aerodynamic + jet), - undamped natural frequency ``omega_n`` and damping ratio ``zeta``. - - ``plane`` is ``"pitch"`` or ``"yaw"``; ``lateral_inertia`` is the array - from :meth:`_lateral_inertia`. The restoring moment ``C1`` is built from - the angle-of-attack-aware margin and force-curve slope taken from the - rocket (:meth:`Rocket._neutral_point_margin_slope`) at the flight angle - of attack, so for a rocket that is nonlinear in incidence the natural - frequency and damping ratio move with the angle of attack. The - aerodynamic damping ``C2`` uses each surface's zero-incidence slope (its - second-order incidence dependence is neglected). - """ - rocket = self.rocket - area = rocket.area - diameter = 2 * rocket.radius - csys = rocket._csys - nozzle_position = rocket.nozzle_position - mass_flow_rate = rocket.motor.total_mass_flow_rate + return inertia, rate + + def _dynamic_stability(self, plane, lateral_inertia, inertia_rate): + """Corrective moment coefficient, damping moment coefficient, natural + frequency and damping ratio of the ``"pitch"`` or ``"yaw"`` plane, as + arrays over ``self.time``.""" + # Imported here: the rocket package cannot be imported while this + # module loads (circular import through the controllers) + # pylint: disable-next=import-outside-toplevel + from ..rocket._helpers import corrective_and_damping_moments corrective = np.empty(len(self.time)) damping = np.empty(len(self.time)) + alphas, betas = self._stability_state() for i, t in enumerate(self.time): - mach = self.mach_number.get_value_opt(t) - dynamic_pressure = self.dynamic_pressure.get_value_opt(t) - speed = self.speed.get_value_opt(t) - density = self.density.get_value_opt(t) - center_of_mass = self.rocket.center_of_mass.get_value_opt(t) - - # Angle-of-attack-aware margin and local restoring-force slope, from - # the rocket, at this instant's incidence, Mach and time. - margin, force_slope = rocket._neutral_point_margin_slope( - self._incidence(t), mach, t, plane + # From the rocket at this instant's state, Mach and time + corrective[i], damping[i] = corrective_and_damping_moments( + self.rocket, + alphas[i], + betas[i], + self.mach_number.get_value_opt(t), + t, + self.free_stream_speed.get_value_opt(t), + self.density.get_value_opt(t), + self.dynamic_pressure.get_value_opt(t), + inertia_rate[i], + plane, ) - # Corrective moment per radian: q A C_Nalpha (x_cm - x_np). - corrective[i] = dynamic_pressure * area * force_slope * margin * diameter - - # Aerodynamic damping: 0.5 rho V A sum_i (A_i/A) C_Nalpha_i arm_i^2. - damping_aero = 0.0 - for surface, position in self.rocket.aerodynamic_surfaces: - slope = surface.cN_alpha.get_value_opt( - 0.0, 0.0, mach, 0.0, 0.0, 0.0, 0.0 - ) - cp_position = ( - position.z - csys * surface.center_of_pressure_z.get_value_opt(mach) - ) - arm = cp_position - center_of_mass - ref_factor = surface.reference_area / area - damping_aero += ref_factor * slope * arm**2 - damping_aero *= 0.5 * density * speed * area - - # Jet (propulsive) damping: mdot (x_nozzle - x_cm)^2. - damping_jet = ( - abs(mass_flow_rate.get_value_opt(t)) - * (nozzle_position - center_of_mass) ** 2 - ) - damping[i] = damping_aero + damping_jet - + # The rocket cannot oscillate while the rail holds it, without inertia + # (a point mass) or when C1 is not positive (no restoring moment). The + # damping ratio also divides by C1, so it is only reported where C1 is + # not negligible. Elsewhere both are reported as 0. positive_corrective = np.clip(corrective, 0.0, None) - # The damping ratio C2 / (2 sqrt(C1 I)) is ill-conditioned when the - # corrective moment C1 is ~0, i.e. at very low airspeed (on the launch - # rail): the propulsive (jet) part of C2 is already at full strength - # while C1 -> 0, so the ratio blows up even though there is no real - # oscillation yet. Only report it where C1 is a meaningful fraction of - # its flight maximum; elsewhere it is left at 0. The natural frequency - # sqrt(C1 / I) has C1 in the numerator, so it stays well-behaved. + free = (self.time >= self.out_of_rail_time) & (lateral_inertia > 0) max_corrective = positive_corrective.max(initial=0.0) - meaningful = positive_corrective > 1e-4 * max_corrective + meaningful = free & (positive_corrective > 1e-4 * max_corrective) with np.errstate(divide="ignore", invalid="ignore"): - natural_frequency = np.sqrt(positive_corrective / lateral_inertia) + natural_frequency = np.where( + free, np.sqrt(positive_corrective / lateral_inertia), 0.0 + ) denominator = 2.0 * np.sqrt(positive_corrective * lateral_inertia) damping_ratio = np.divide( damping, @@ -2504,57 +2553,138 @@ def _dynamic_stability(self, plane, lateral_inertia): ) return corrective, damping, natural_frequency, damping_ratio + @cached_property + def _pitch_dynamics(self): + """Corrective and damping moment coefficients, natural frequency and + damping ratio of the pitch plane over ``self.time``, computed once.""" + return self._dynamic_stability( + "pitch", *self._lateral_inertia(self.rocket.I_11) + ) + + @cached_property + def _yaw_dynamics(self): + """As :attr:`_pitch_dynamics`, for the yaw plane.""" + return self._dynamic_stability("yaw", *self._lateral_inertia(self.rocket.I_22)) + @funcify_method("Time (s)", "Corrective Moment Coefficient (N m/rad)", "linear") def corrective_moment_coefficient(self): """Pitch-plane corrective (restoring) moment coefficient ``C1`` as a function of time -- the aerodynamic restoring moment per radian of angle of attack. Positive for a statically stable rocket.""" - inertia = self._lateral_inertia(self.rocket.dry_I_11, self.rocket.motor.I_11) - corrective, _, _, _ = self._dynamic_stability("pitch", inertia) - return np.column_stack((self.time, corrective)) + return np.column_stack((self.time, self._pitch_dynamics[0])) @funcify_method("Time (s)", "Damping Moment Coefficient (N m s/rad)", "linear") def damping_moment_coefficient(self): """Pitch-plane damping moment coefficient ``C2`` as a function of time -- the moment opposing the pitch rate, summing aerodynamic damping (from - every surface) and propulsive (jet) damping.""" - inertia = self._lateral_inertia(self.rocket.dry_I_11, self.rocket.motor.I_11) - _, damping, _, _ = self._dynamic_stability("pitch", inertia) - return np.column_stack((self.time, damping)) + every surface) and propulsive (jet) damping. The jet damping is the + angular momentum the exhaust carries away, ``|mdot| (x_nozzle - + x_cm)**2``, less what the falling lateral inertia gives back, + ``dI_L/dt``; Thomson's ``mdot (l_n**2 - l_p**2)``.""" + return np.column_stack((self.time, self._pitch_dynamics[1])) @funcify_method("Time (s)", "Pitch Natural Frequency (rad/s)", "linear") def pitch_natural_frequency(self): """Undamped natural frequency of the pitch oscillation, ``omega_n = sqrt(C1 / I_L)``, as a function of time (rad/s).""" - inertia = self._lateral_inertia(self.rocket.dry_I_11, self.rocket.motor.I_11) - _, _, natural_frequency, _ = self._dynamic_stability("pitch", inertia) - return np.column_stack((self.time, natural_frequency)) + return np.column_stack((self.time, self._pitch_dynamics[2])) @funcify_method("Time (s)", "Pitch Damping Ratio", "linear") def pitch_damping_ratio(self): """Damping ratio of the pitch oscillation, ``zeta = C2 / (2 sqrt(C1 I_L))``, as a function of time. ``zeta < 1`` is underdamped (oscillatory), ``zeta > 1`` overdamped.""" - inertia = self._lateral_inertia(self.rocket.dry_I_11, self.rocket.motor.I_11) - _, _, _, damping_ratio = self._dynamic_stability("pitch", inertia) - return np.column_stack((self.time, damping_ratio)) + return np.column_stack((self.time, self._pitch_dynamics[3])) @funcify_method("Time (s)", "Yaw Natural Frequency (rad/s)", "linear") def yaw_natural_frequency(self): """Undamped natural frequency of the yaw oscillation as a function of time (rad/s). Equals :meth:`pitch_natural_frequency` for an axisymmetric - rocket.""" - inertia = self._lateral_inertia(self.rocket.dry_I_22, self.rocket.motor.I_22) - _, _, natural_frequency, _ = self._dynamic_stability("yaw", inertia) - return np.column_stack((self.time, natural_frequency)) + rocket linear in the angle of attack; for an axisymmetric rocket + nonlinear in the angle it is the frequency of a motion across the + wind.""" + return np.column_stack((self.time, self._yaw_dynamics[2])) @funcify_method("Time (s)", "Yaw Damping Ratio", "linear") def yaw_damping_ratio(self): """Damping ratio of the yaw oscillation as a function of time. Equals - :meth:`pitch_damping_ratio` for an axisymmetric rocket.""" - inertia = self._lateral_inertia(self.rocket.dry_I_22, self.rocket.motor.I_22) - _, _, _, damping_ratio = self._dynamic_stability("yaw", inertia) - return np.column_stack((self.time, damping_ratio)) + :meth:`pitch_damping_ratio` for an axisymmetric rocket linear in the + angle of attack.""" + return np.column_stack((self.time, self._yaw_dynamics[3])) + + def disturbance_response(self, time, disturbance=5.0, plane="pitch", duration=None): + """Compute how the rocket would swing back after a sudden disturbance + at one instant of this flight. + + The rocket is tilted by ``disturbance`` away from its flight direction + and released. The result shows how its angle swings back: how fast it + oscillates and how quickly the oscillation dies out. The airspeed, air + density and rocket mass are the ones of this flight at ``time`` and are + held fixed, so this is a snapshot of that instant: during the motor + burn the real conditions change while the rocket swings. It is valid + for small angles. + + Parameters + ---------- + time : float + Instant of the flight, in seconds, at which the disturbance + happens. Must be after the rocket leaves the rail, for example + ``flight.out_of_rail_time``. + disturbance : float, optional + Angle the rocket is tilted by, in degrees. Default is 5. + plane : str, optional + ``"pitch"`` or ``"yaw"``. The two only differ for a rocket that is + not axisymmetric. Default is ``"pitch"``. + duration : float, optional + How long to follow the response, in seconds. By default, long + enough for the oscillation to settle. + + Returns + ------- + rocketpy.Function + Angle of the rocket, in degrees, as a function of the time since + the disturbance, in seconds. Call ``.plot()`` on it to see the + curve; its title shows the natural frequency and damping ratio. + + Raises + ------ + ValueError + If ``time`` is before the rocket leaves the rail, where it cannot + swing, or for a point mass rocket. + + See Also + -------- + rocketpy.Rocket.disturbance_response : The same response at a flight + condition you choose, without running a flight. + + Examples + -------- + >>> response = flight.disturbance_response(flight.out_of_rail_time) # doctest: +SKIP + >>> response.plot() # doctest: +SKIP + """ + # pylint: disable-next=import-outside-toplevel + from ..rocket._helpers import disturbance_response, lateral_inertia_and_rate + + if time < self.out_of_rail_time: + raise ValueError( + f"At {time} s the rocket is still on the rail (it leaves at " + f"{self.out_of_rail_time:.3f} s) and cannot swing." + ) + if plane == "yaw": + dynamics, inertia_about_cdm = self._yaw_dynamics, self.rocket.I_22 + else: + dynamics, inertia_about_cdm = self._pitch_dynamics, self.rocket.I_11 + if not isinstance(inertia_about_cdm, Function): + inertia = 0.0 # a point mass rocket + else: + inertia, _ = lateral_inertia_and_rate(self.rocket, inertia_about_cdm, time) + return disturbance_response( + np.interp(time, self.time, dynamics[0]), + np.interp(time, self.time, dynamics[1]), + inertia, + disturbance, + duration, + ) # Rail Button Forces diff --git a/rocketpy/simulation/helpers/event_commands.py b/rocketpy/simulation/helpers/event_commands.py index fb8c5760f..e26f16c6c 100644 --- a/rocketpy/simulation/helpers/event_commands.py +++ b/rocketpy/simulation/helpers/event_commands.py @@ -50,6 +50,9 @@ def apply_event_commands( apply_disable_commands( flight, event_results, node_index, event, phase, time=t_apply ) + if event.changes_dynamics: + # The callback may have changed a surface + flight.rocket._refresh_aerodynamics() def apply_rollback_command(flight, time, state): diff --git a/rocketpy/simulation/helpers/flight_derivatives.py b/rocketpy/simulation/helpers/flight_derivatives.py index 5e94afbbf..f61a7e420 100644 --- a/rocketpy/simulation/helpers/flight_derivatives.py +++ b/rocketpy/simulation/helpers/flight_derivatives.py @@ -4,58 +4,27 @@ from ...mathutils import Matrix, Vector -def _compute_drag_7d_inputs( +def _compute_drag_area( flight, + time, stream_velocity_body, stream_speed, - stream_mach, + mach, density, dynamic_viscosity, + omega, ): - """Compute drag-model inputs in the order expected by RocketPy drag functions. - - Parameters - ---------- - flight : Flight - Flight object providing rocket geometry. - stream_velocity_body : Vector - Freestream velocity expressed in the body frame. - stream_speed : float - Freestream speed magnitude in m/s. - stream_mach : float - Freestream Mach number. - density : float - Atmospheric density in kg/m^3. - dynamic_viscosity : float - Atmospheric dynamic viscosity in Pa·s. - - Returns - ------- - tuple of float - ``(alpha, beta, mach, reynolds)`` where ``alpha`` and ``beta`` are the - aerodynamic angles, ``mach`` is the supplied Mach number, and ``reynolds`` - is the Reynolds number based on rocket diameter. - """ - aerodynamic_stream_velocity = -stream_velocity_body - alpha = np.arctan2(aerodynamic_stream_velocity[1], aerodynamic_stream_velocity[2]) - beta = np.arctan2(aerodynamic_stream_velocity[0], aerodynamic_stream_velocity[2]) - reynolds = ( - density * stream_speed * (2 * flight.rocket.radius) / dynamic_viscosity - if dynamic_viscosity > 0 - else 0 - ) - return alpha, beta, stream_mach, reynolds - - -def _aerodynamic_drag_force( - flight, time, rho, stream_speed, alpha, beta, mach, reynolds, omega -): - """Total rocket axial aerodynamic (drag) force, including air brakes. + """Drag coefficient times reference area of the rocket, air brakes included, + in squared meters. Selects the power-on/power-off drag curve based on the motor burn state, and then adds (or, when ``override_rocket_drag`` is set, substitutes) the drag of - any deployed air brakes, evaluated through the generic-surface coefficient - machinery. + any deployed air brakes. The force follows from it as + ``0.5 * rho * stream_speed * drag_area`` times the velocity of the air + relative to the rocket: its component along the rocket's axis in a 6-DOF + phase, the whole vector in a 3-DOF one. The rocket's drag curve is its axial + force coefficient: at an angle to the air, the normal force of the nose cone, + fins and tail carries the rest of the drag. Parameters ---------- @@ -63,43 +32,59 @@ def _aerodynamic_drag_force( Flight object providing the rocket. time : float Simulation time, used to select the power-on vs power-off drag curve. - rho : float - Air density. + stream_velocity_body : Vector + Velocity of the air relative to the rocket, in the body frame, in m/s. + The angle of attack and the sideslip angle are read from it. stream_speed : float - Freestream speed magnitude. - alpha, beta, mach, reynolds : float - Standard aerodynamic coefficient inputs at the current state. + Freestream speed magnitude, in m/s. + mach : float + Freestream Mach number. + density : float + Atmospheric density, in kg/m^3. + dynamic_viscosity : float + Atmospheric dynamic viscosity, in Pa·s. With the density it gives the + Reynolds number, based on the rocket diameter. omega : tuple of float - Body angular rates ``(omega1, omega2, omega3)``. + Body angular rates ``(omega1, omega2, omega3)``, in rad/s. They are + passed to the coefficients as the non-dimensional reduced rates + ``omega * L_ref / (2 * V)``. Returns ------- float - The axial (body z) aerodynamic drag force. + The drag coefficient times the reference area. """ rocket = flight.rocket - if time < rocket.motor.burn_out_time: - drag_coefficient = rocket.power_on_drag_7d( - alpha, beta, mach, reynolds, omega[0], omega[1], omega[2] - ) - else: - drag_coefficient = rocket.power_off_drag_7d( - alpha, beta, mach, reynolds, omega[0], omega[1], omega[2] - ) - drag_force = -0.5 * rho * stream_speed**2 * rocket.area * drag_coefficient + # The inputs of the drag coefficients, in the order they expect them + alpha = np.arctan2(-stream_velocity_body[1], -stream_velocity_body[2]) + beta = np.arctan2(-stream_velocity_body[0], -stream_velocity_body[2]) + reynolds = ( + density * stream_speed * (2 * rocket.radius) / dynamic_viscosity + if dynamic_viscosity > 0 + else 0 + ) + reduced_per_length = 1 / (2 * stream_speed) if stream_speed > 0 else 0.0 + reduced = 2 * rocket.radius * reduced_per_length + drag_7d = ( + rocket.power_on_drag_7d + if time < rocket.motor.burn_out_time + else rocket.power_off_drag_7d + ) + drag_area = rocket.area * drag_7d( + alpha, + beta, + mach, + reynolds, + omega[0] * reduced, + omega[1] * reduced, + omega[2] * reduced, + ) # Air brakes are drag-only and may override the rocket drag. for air_brakes in rocket.air_brakes: if air_brakes.deployment_level > 0: - # Air brakes are a (controllable) generic surface, so feed the - # coefficient the non-dimensional reduced rates, like every other - # generic surface (see GenericSurface.compute_forces_and_moments). - reduced = ( - air_brakes.reference_length / (2 * stream_speed) - if stream_speed > 0 - else 0.0 - ) - air_brakes_cd = air_brakes.cD.get_value_opt( + reduced = air_brakes.reference_length * reduced_per_length + air_brakes_area = air_brakes.reference_area * air_brakes.cA.get_value_opt( *air_brakes._coefficient_arguments( alpha, beta, @@ -110,14 +95,11 @@ def _aerodynamic_drag_force( omega[2] * reduced, ) ) - air_brakes_force = ( - -0.5 * rho * stream_speed**2 * air_brakes.reference_area * air_brakes_cd - ) if air_brakes.override_rocket_drag: - drag_force = air_brakes_force # Substitutes rocket drag + drag_area = air_brakes_area # Substitutes rocket drag else: - drag_force += air_brakes_force - return drag_force + drag_area += air_brakes_area + return drag_area def udot_rail1(flight, t, u, post_processing=False): @@ -167,15 +149,16 @@ def udot_rail1(flight, t, u, post_processing=False): Matrix.transformation([e0, e1, e2, e3]).transpose @ free_stream_velocity ) dynamic_viscosity = flight.env.dynamic_viscosity.get_value_opt(z) - alpha, beta, mach, reynolds = _compute_drag_7d_inputs( + drag_area = _compute_drag_area( flight, + t, stream_velocity_body, free_stream_speed, free_stream_mach, rho, dynamic_viscosity, + (0, 0, 0), ) - drag_coeff = flight.rocket.power_on_drag_7d(alpha, beta, mach, reynolds, 0, 0, 0) # Calculate Forces pressure = flight.env.pressure.get_value_opt(z) @@ -184,7 +167,7 @@ def udot_rail1(flight, t, u, post_processing=False): + flight.rocket.motor.pressure_thrust(pressure), 0, ) - R3 = -0.5 * rho * (free_stream_speed**2) * flight.rocket.area * (drag_coeff) + R3 = 0.5 * rho * free_stream_speed * stream_velocity_body[2] * drag_area # Calculate Linear acceleration a3 = (R3 + net_thrust) / total_mass_at_t - ( @@ -352,25 +335,19 @@ def u_dot(flight, t, u, post_processing=False): # Determine Drag Force rho = flight.env.density.get_value_opt(z) dynamic_viscosity = flight.env.dynamic_viscosity.get_value_opt(z) - alpha, beta, mach, reynolds = _compute_drag_7d_inputs( + drag_area = _compute_drag_area( flight, + t, stream_velocity_body, free_stream_speed, free_stream_mach, rho, dynamic_viscosity, - ) - R3 = _aerodynamic_drag_force( - flight, - t, - rho, - free_stream_speed, - alpha, - beta, - mach, - reynolds, (omega1, omega2, omega3), ) + # The drag curve is the axial force coefficient, so the drag follows the + # air moving along the rocket's axis: zero sideways, reversed tail first + R3 = 0.5 * rho * free_stream_speed * stream_velocity_body[2] * drag_area # Off center moment M1 += flight.rocket.cp_eccentricity_y * R3 M2 -= flight.rocket.cp_eccentricity_x * R3 @@ -418,10 +395,20 @@ def u_dot(flight, t, u, post_processing=False): # Off center moment M3 += flight.rocket.cp_eccentricity_x * R2 - flight.rocket.cp_eccentricity_y * R1 + # The aerodynamic moments are taken about the center of dry mass, but the + # angular equations below hold about the system's center of mass, which + # sits ``a`` behind it (toward the propellant) while there is propellant. + # Transfer the lateral forces' moment to that point: M_cm = M_cdm + a z x R. + # The reported M1, M2 stay about the center of dry mass, as in the + # generalized equations. + a = b * mu / rocket_dry_mass # = b * m_prop / m_total + M1_cm = M1 - a * R2 + M2_cm = M2 + a * R1 + # Calculate derivatives # Angular acceleration alpha1 = ( - M1 + M1_cm - ( omega2 * omega3 @@ -437,7 +424,7 @@ def u_dot(flight, t, u, post_processing=False): ( motor_I_11_derivative_at_t + mass_flow_rate_at_t - * (rocket_dry_mass - 1) + * rocket_dry_mass**2 * (b / total_mass_at_t) ** 2 ) - mass_flow_rate_at_t @@ -446,7 +433,7 @@ def u_dot(flight, t, u, post_processing=False): ) ) / (rocket_dry_I_11 + motor_I_11_at_t + mu * b**2) alpha2 = ( - M2 + M2_cm - ( omega1 * omega3 @@ -462,7 +449,7 @@ def u_dot(flight, t, u, post_processing=False): ( motor_I_11_derivative_at_t + mass_flow_rate_at_t - * (rocket_dry_mass - 1) + * rocket_dry_mass**2 * (b / total_mass_at_t) ** 2 ) - mass_flow_rate_at_t @@ -593,28 +580,21 @@ def u_dot_generalized_3dof(flight, t, u, post_processing=False): mach = free_stream_speed / speed_of_sound stream_velocity_body = Kt @ free_stream_velocity dynamic_viscosity = flight.env.dynamic_viscosity.get_value_opt(z) - alpha, beta, mach, reynolds = _compute_drag_7d_inputs( + + # Drag (rocket body drag + air brakes). A 3-DOF phase does not model the + # rocket's attitude, so the drag acts against the velocity of the rocket + # relative to the air instead of along the body axis. + drag_area = _compute_drag_area( flight, + t, stream_velocity_body, free_stream_speed, mach, rho, dynamic_viscosity, - ) - - # Drag computation (rocket body drag + air brakes) - R1, R2 = 0, 0 - R3 = _aerodynamic_drag_force( - flight, - t, - rho, - free_stream_speed, - alpha, - beta, - mach, - reynolds, (omega1, omega2, omega3), ) + R1, R2, R3 = stream_velocity_body * (0.5 * rho * free_stream_speed * drag_area) # Velocity in body frame vb_body = Kt @ v @@ -798,14 +778,20 @@ def u_dot_generalized(flight, t, u, post_processing=False): total_mass = flight.rocket.total_mass.get_value_opt(t) total_mass_dot = flight.rocket.total_mass_flow_rate.get_value_opt(t) total_mass_ddot = flight.rocket.total_mass_flow_rate.differentiate_complex_step(t) - ## CM position vector and time derivatives relative to CDM in body frame + ## CM position vector and time derivatives relative to CDM in body frame. + ## ``com_to_cdm_function`` runs from the center of mass to the center of + ## dry mass, so the position of the center of mass is its negative. Every + ## term linear in r_CM below (the transfer of the forces' moment to the + ## center of mass, the gravity moment, the mass-flow Coriolis terms) needs + ## the position itself. r_CM_z = flight.rocket.com_to_cdm_function - r_CM_t = r_CM_z.get_value_opt(t) + r_CM_t = -r_CM_z.get_value_opt(t) r_CM = Vector([0, 0, r_CM_t]) - r_CM_dot = Vector([0, 0, r_CM_z.differentiate_complex_step(t)]) - r_CM_ddot = Vector([0, 0, r_CM_z.differentiate(t, order=2)]) - ## Nozzle position vector - r_NOZ = Vector([0, 0, flight.rocket.nozzle_to_cdm]) + r_CM_dot = Vector([0, 0, -r_CM_z.differentiate_complex_step(t)]) + r_CM_ddot = Vector([0, 0, -r_CM_z.differentiate(t, order=2)]) + ## Nozzle exit position vector relative to CDM in body frame + ## (``nozzle_to_cdm`` runs from the nozzle exit to the center of dry mass) + r_NOZ = Vector([0, 0, -flight.rocket.nozzle_to_cdm]) ## Nozzle gyration tensor S_nozzle = flight.rocket.nozzle_gyration_tensor ## Inertia tensor @@ -835,14 +821,6 @@ def u_dot_generalized(flight, t, u, post_processing=False): free_stream_mach = free_stream_speed / speed_of_sound stream_velocity_body = Kt @ free_stream_velocity dynamic_viscosity = flight.env.dynamic_viscosity.get_value_opt(z) - alpha, beta, mach, reynolds = _compute_drag_7d_inputs( - flight, - stream_velocity_body, - free_stream_speed, - free_stream_mach, - rho, - dynamic_viscosity, - ) if flight.rocket.motor.burn_start_time < t < flight.rocket.motor.burn_out_time: pressure = flight.env.pressure.get_value_opt(z) @@ -853,17 +831,19 @@ def u_dot_generalized(flight, t, u, post_processing=False): ) else: net_thrust = 0 - R3 = _aerodynamic_drag_force( + drag_area = _compute_drag_area( flight, t, - rho, + stream_velocity_body, free_stream_speed, - alpha, - beta, - mach, - reynolds, + free_stream_mach, + rho, + dynamic_viscosity, (omega1, omega2, omega3), ) + # The drag curve is the axial force coefficient, so the drag follows the + # air moving along the rocket's axis: zero sideways, reversed tail first + R3 = 0.5 * rho * free_stream_speed * stream_velocity_body[2] * drag_area # Get rocket velocity in body frame velocity_in_body_frame = Kt @ v # Calculate lift and moment for each component of the rocket @@ -949,9 +929,12 @@ def u_dot_generalized(flight, t, u, post_processing=False): 0.5 * (omega3 * e0 + omega2 * e1 - omega1 * e2), ] - # Velocity vector derivative + Coriolis acceleration + # Velocity vector derivative + Coriolis acceleration. T20 / m is the + # acceleration of the center of dry mass plus w_dot x r_CM, the part of the + # center of mass' acceleration the angular acceleration gives it about + # the center of dry mass. w_earth = Vector(flight.env.earth_rotation_vector) - v_dot = K @ (T20 / total_mass - (r_CM ^ w_dot)) - 2 * (w_earth ^ v) + v_dot = K @ (T20 / total_mass + (r_CM ^ w_dot)) - 2 * (w_earth ^ v) # Position vector derivative r_dot = [vx, vy, vz] diff --git a/rocketpy/simulation/monte_carlo.py b/rocketpy/simulation/monte_carlo.py index 42a566b7b..73369fe4a 100644 --- a/rocketpy/simulation/monte_carlo.py +++ b/rocketpy/simulation/monte_carlo.py @@ -742,6 +742,14 @@ def __check_export_list(self, export_list): "final_static_margin", "frontal_surface_wind", "initial_static_margin", + "initial_stability_margin", + "initial_stability_margin_yaw", + "out_of_rail_stability_margin", + "out_of_rail_stability_margin_yaw", + "max_stability_margin", + "max_stability_margin_yaw", + "min_stability_margin", + "min_stability_margin_yaw", "lateral_surface_wind", "max_acceleration", "max_acceleration_time", diff --git a/rocketpy/stochastic/stochastic_model.py b/rocketpy/stochastic/stochastic_model.py index 879b61e70..5e3e491d5 100644 --- a/rocketpy/stochastic/stochastic_model.py +++ b/rocketpy/stochastic/stochastic_model.py @@ -414,7 +414,7 @@ def _validate_1d_array_like(self, input_name, input_value): if input_value is not None: error_msg = ( f"`{input_name}` must be a list of path strings, lists " - "with shape (n,2), or Functions." + "with shape (n,2), Functions, AeroCoefficients or functions." ) if not isinstance(input_value, list): @@ -424,7 +424,7 @@ def _validate_1d_array_like(self, input_name, input_value): if isinstance(member, list): if len(np.shape(member)) != 2 or np.shape(member)[1] != 2: raise AssertionError(error_msg) - elif not isinstance(member, (str, Function)): + elif not (isinstance(member, str) or callable(member)): raise AssertionError(error_msg) def _validate_positive_int_list(self, input_name, input_value): diff --git a/rocketpy/stochastic/stochastic_rocket.py b/rocketpy/stochastic/stochastic_rocket.py index f9f1e6c6e..909b006b4 100644 --- a/rocketpy/stochastic/stochastic_rocket.py +++ b/rocketpy/stochastic/stochastic_rocket.py @@ -36,6 +36,9 @@ # TODO: Private methods of this class should be double underscored +# Surfaces with a stochastic version, which is added with the ``add_*`` methods +_DISPERSED_SURFACES = (NoseCone, TrapezoidalFins, EllipticalFins, Tail) + class StochasticRocket(StochasticModel): """A Stochastic Rocket class that inherits from StochasticModel. @@ -75,9 +78,11 @@ class StochasticRocket(StochasticModel): inertia_23 : tuple, list, int, float The inertia of the rocket around the yz axis. power_off_drag : list - The power off drag of the rocket. + The power off drag curves to choose from. By default, the rocket's own + ``power_off_drag_7d``, with every variable it depends on. power_on_drag : list - The power on drag of the rocket. + The power on drag curves to choose from. By default, the rocket's own + ``power_on_drag_7d``. power_off_drag_factor : tuple, list, int, float The power off drag factor of the rocket. power_on_drag_factor : tuple, list, int, float @@ -87,6 +92,14 @@ class StochasticRocket(StochasticModel): coordinate_system_orientation : list[str] The orientation of the coordinate system of the rocket. This attribute can not be a randomized. + + Notes + ----- + The rocket's ``stability_phase`` and ``length`` are kept as they are. + Surfaces with no stochastic version yet, the ones built from their own + coefficients (``GenericSurface`` and its linear and controllable versions), + the free-form fins and the individual fins, are added to every generated + rocket unchanged, at their position. """ def __init__( @@ -133,9 +146,12 @@ def __init__( inertia_23 : int, float, tuple, list, optional The inertia of the rocket around the yz axis. power_off_drag : list, optional - The power off drag of the rocket. + Drag curves to choose from, in any form the ``Rocket`` accepts + (CSV paths, lists of points, Functions, AeroCoefficients or + functions). Default is the rocket's own ``power_off_drag_7d``. power_on_drag : list, optional - The power on drag of the rocket. + Drag curves to choose from, as ``power_off_drag``. Default is the + rocket's own ``power_on_drag_7d``. power_off_drag_factor : int, float, tuple, list, optional The power off drag factor of the rocket. This represents a factor that multiplies the power off drag curve. @@ -146,6 +162,10 @@ def __init__( The center of mass of the rocket without the motor. """ # TODO: mention that these factors are validated differently + # The rocket's ``power_off_drag`` is its Mach-only view; the simulation + # uses the full coefficient, so that is the default. + power_off_drag = power_off_drag or [rocket.power_off_drag_7d] + power_on_drag = power_on_drag or [rocket.power_on_drag_7d] self._validate_1d_array_like("power_off_drag", power_off_drag) self._validate_1d_array_like("power_on_drag", power_on_drag) self.motors = Components() @@ -741,9 +761,11 @@ def create_object(self): coordinate_system_orientation=generated_dict[ "coordinate_system_orientation" ], + length=self.obj._length, ) rocket.power_off_drag_7d *= generated_dict["power_off_drag_factor"] rocket.power_on_drag_7d *= generated_dict["power_on_drag_factor"] + rocket.stability_phase = self.obj.stability_phase if hasattr(self, "cp_eccentricity_x") and hasattr(self, "cp_eccentricity_y"): cp_ecc_x, cp_ecc_y = self._create_eccentricities( @@ -770,6 +792,11 @@ def create_object(self): surface, position_rnd = self._create_surface(component_surface) rocket.add_surfaces(surface, position_rnd) + # Surfaces with no stochastic version are carried over as they are + for surface, position in self.obj.aerodynamic_surfaces: + if not isinstance(surface, _DISPERSED_SURFACES): + rocket.add_surfaces(surface, position) + for air_brake in self.air_brakes: air_brake = self._create_air_brake(air_brake) base_controller = self.air_brake_controller diff --git a/tests/integration/simulation/test_event.py b/tests/integration/simulation/test_event.py index 6ce513ebb..378340d8b 100644 --- a/tests/integration/simulation/test_event.py +++ b/tests/integration/simulation/test_event.py @@ -6,7 +6,6 @@ from rocketpy import Environment, Event, Flight, Rocket, SolidMotor from rocketpy.simulation.events import Commands, exact_time_solvers -from rocketpy.simulation.helpers.dynamics import SIX_DOF_DYNAMICS, _PhaseDynamics from rocketpy.simulation.events.event_builders import ( apogee_callback, apogee_event_exact_time_function, @@ -26,6 +25,7 @@ solve_cubic_hermite, solve_linear, ) +from rocketpy.simulation.helpers.dynamics import SIX_DOF_DYNAMICS, _PhaseDynamics def _callback_return_time(context): diff --git a/tests/integration/simulation/test_flight.py b/tests/integration/simulation/test_flight.py index fd62cbb6f..9680d1f0f 100644 --- a/tests/integration/simulation/test_flight.py +++ b/tests/integration/simulation/test_flight.py @@ -667,7 +667,7 @@ def test_freestream_speed_at_apogee(example_plain_env, calisto): assert np.isclose( test_flight.stream_velocity_x(test_flight.apogee_time), - 0.4641547105489406, + 0.463944387965394, atol=hard_atol, ) assert np.isclose( @@ -679,11 +679,11 @@ def test_freestream_speed_at_apogee(example_plain_env, calisto): ) assert np.isclose( test_flight.free_stream_speed(test_flight.apogee_time), - 0.46415471054894075, + 0.4639443879653988, atol=hard_atol, ) assert np.isclose( - test_flight.apogee_freestream_speed, 0.46415471054894075, atol=hard_atol + test_flight.apogee_freestream_speed, 0.4639443879653988, atol=hard_atol ) diff --git a/tests/integration/simulation/test_flight_3dof.py b/tests/integration/simulation/test_flight_3dof.py index 83e1c10f9..37d034ec6 100644 --- a/tests/integration/simulation/test_flight_3dof.py +++ b/tests/integration/simulation/test_flight_3dof.py @@ -253,13 +253,27 @@ def drag_7d(alpha, beta, mach, reynolds, pitch_rate, yaw_rate, roll_rate): else 0 ) - cd_expected = drag_7d(alpha, beta, mach, reynolds, omega1, omega2, omega3) - r3_expected = -0.5 * rho * free_stream_speed**2 * rocket.area * cd_expected - az_expected = ( - r3_expected - rocket.total_mass.get_value_opt(t) * gravity - ) / rocket.total_mass.get_value_opt(t) + # The drag receives the non-dimensional rates, rate * diameter / (2 * speed) + reduced = 2 * rocket.radius / (2 * free_stream_speed) + cd_expected = drag_7d( + alpha, + beta, + mach, + reynolds, + omega1 * reduced, + omega2 * reduced, + omega3 * reduced, + ) + # A 3-DOF phase does not model the attitude, so the drag acts against the + # velocity relative to the air (here the body and inertial axes coincide) + drag_expected = ( + 0.5 * rho * free_stream_speed * rocket.area * cd_expected * free_stream_velocity + ) + mass = rocket.total_mass.get_value_opt(t) - assert u_dot[5] == pytest.approx(az_expected) + assert u_dot[3] == pytest.approx(drag_expected[0] / mass) + assert u_dot[4] == pytest.approx(drag_expected[1] / mass) + assert u_dot[5] == pytest.approx(drag_expected[2] / mass - gravity) def test_weathercock_zero_gives_fixed_attitude(flight_weathercock_zero): diff --git a/tests/unit/mathutils/test_function.py b/tests/unit/mathutils/test_function.py index e2853f1df..070d6eb42 100644 --- a/tests/unit/mathutils/test_function.py +++ b/tests/unit/mathutils/test_function.py @@ -548,13 +548,22 @@ def test_multivariate_dataset(a, b): ] func = Function(source=source, inputs=["x", "y"], outputs=["z"]) - # Assert interpolation and extrapolation methods - assert func.get_interpolation_method() == "shepard" + # The points cover every combination of x and y: they are read as a grid + assert func.is_regular_grid + assert func.get_interpolation_method() == "linear" assert func.get_extrapolation_method() == "natural" # Assert values assert np.isclose(func(a, b), a + b, atol=1e-6) + # Asking for an interpolation written for scattered points keeps them so + scattered = Function( + source=source, inputs=["x", "y"], outputs=["z"], interpolation="shepard" + ) + assert not scattered.is_regular_grid + assert scattered.get_interpolation_method() == "shepard" + assert np.isclose(scattered(a, b), a + b, atol=1e-6) + @pytest.mark.parametrize( "x,y,z_expected", @@ -1308,6 +1317,12 @@ def test_short_time_fft( assert np.all(frequencies <= sampling_frequency / 2) +def _grid_points(axes, data): + """Write data on a grid as a table of points, one row per node.""" + mesh = np.meshgrid(*axes, indexing="ij") + return np.column_stack([m.ravel() for m in mesh] + [np.ravel(data)]) + + @pytest.fixture def bilinear_grid_2d(): """Return a 2-D regular_grid Function for f(x, y) = 2x + 3y. @@ -1325,10 +1340,7 @@ def bilinear_grid_2d(): X, Y = np.meshgrid(x_axis, y_axis, indexing="ij") data = 2.0 * X + 3.0 * Y return Function( - ([x_axis, y_axis], data), - inputs=["x", "y"], - outputs=["z"], - interpolation="regular_grid", + _grid_points([x_axis, y_axis], data), inputs=["x", "y"], outputs=["z"] ) @@ -1339,7 +1351,8 @@ def test_regular_grid_constructor_sets_metadata(bilinear_grid_2d): are all stored correctly after construction via the ``(axes, grid_data)`` tuple form. """ - assert bilinear_grid_2d.get_interpolation_method() == "regular_grid" + assert bilinear_grid_2d.is_regular_grid + assert bilinear_grid_2d.get_interpolation_method() == "linear" assert bilinear_grid_2d.get_extrapolation_method() == "natural" assert bilinear_grid_2d.get_domain_dim() == 2 assert bilinear_grid_2d.get_inputs() == ["x", "y"] @@ -1413,11 +1426,11 @@ def test_3d_regular_grid_interpolation(x, y, z, expected): X, Y, Z = np.meshgrid(x_axis, y_axis, z_axis, indexing="ij") data = X + 2.0 * Y + 3.0 * Z func = Function( - ([x_axis, y_axis, z_axis], data), + _grid_points([x_axis, y_axis, z_axis], data), inputs=["x", "y", "z"], outputs=["w"], - interpolation="regular_grid", ) + assert func.is_regular_grid result = func(x, y, z) result_opt = func.get_value_opt(x, y, z) @@ -1461,12 +1474,12 @@ def test_regular_grid_extrapolation(extrapolation, x_out, y_out, expected): X, Y = np.meshgrid(x_axis, y_axis, indexing="ij") data = 2.0 * X + 3.0 * Y func = Function( - ([x_axis, y_axis], data), + _grid_points([x_axis, y_axis], data), inputs=["x", "y"], outputs=["z"], - interpolation="regular_grid", extrapolation=extrapolation, ) + assert func.is_regular_grid result = func(x_out, y_out) @@ -1515,17 +1528,21 @@ def test_regular_grid_sorts_unsorted_axes(): x_grid, y_grid = np.meshgrid(x_axis, y_axis, indexing="ij") data = 2.0 * x_grid + 3.0 * y_grid - ascending = Function( - ([x_axis, y_axis], data), interpolation="regular_grid", extrapolation="natural" - ) - # First axis descending, data reversed along that axis to describe the SAME - # surface. It must be normalized to ascending and yield identical values. - descending = Function( - ([x_axis[::-1], y_axis], data[::-1, :]), - interpolation="regular_grid", - extrapolation="natural", - ) - assert np.all(np.diff(descending._grid_axes[0]) > 0) + # The (axes, values) form is deprecated but must keep working + with pytest.warns(DeprecationWarning, match="regular_grid"): + ascending = Function( + ([x_axis, y_axis], data), + interpolation="regular_grid", + extrapolation="natural", + ) + # First axis descending, data reversed along that axis to describe the + # SAME surface. It must be read in ascending order with the same values. + descending = Function( + ([x_axis[::-1], y_axis], data[::-1, :]), + interpolation="regular_grid", + extrapolation="natural", + ) + assert np.all(np.diff(descending._grid.axes[0]) > 0) assert np.isclose(descending(1.5, 0.5), ascending(1.5, 0.5)) @@ -1539,24 +1556,6 @@ def test_regular_grid_repeated_axis_coordinate_raises(): ) -def test_from_regular_grid_csv_falls_back_when_too_few_points(tmp_path): - """A smooth grid method (e.g. cubic) needs enough points per axis; when the - grid is too coarse, from_regular_grid_csv warns and falls back to linear.""" - filename = tmp_path / "coarse_grid.csv" - # 2x2 grid: too few points for cubic (which needs 4 per axis). - filename.write_text("mach,alpha,cL\n0,0,0\n0,1,1\n1,0,1\n1,1,2\n", encoding="utf-8") - with pytest.warns(UserWarning, match="falling back to 'linear'"): - func = Function.from_regular_grid_csv( - str(filename), - ["mach", "alpha"], - "cL", - extrapolation="constant", - interpolation="cubic", - ) - assert func is not None - assert getattr(func, "_grid_method", "linear") == "linear" - - def test_regular_grid_caches_domain_bounds(bilinear_grid_2d): """The N-D hot path caches per-dimension domain bounds at source time.""" assert np.allclose(bilinear_grid_2d._domain_min, [0.0, 0.0]) @@ -1568,27 +1567,7 @@ def test_regular_grid_dict_round_trip(bilinear_grid_2d): from the (axes, grid_data) structure rather than the flat scatter source.""" restored = Function.from_dict(bilinear_grid_2d.to_dict()) - assert restored.get_interpolation_method() == "regular_grid" + assert restored.is_regular_grid assert restored.get_domain_dim() == 2 for x, y in [(0.5, 1.5), (1.25, 0.75), (2.0, 2.0)]: assert np.isclose(restored(x, y), bilinear_grid_2d(x, y)) - - -def test_regular_grid_dict_round_trip_preserves_method(tmp_path): - """A non-default grid method (e.g. pchip) survives to_dict/from_dict.""" - filename = tmp_path / "grid.csv" - rows = ["x,y,z"] - for x in (0, 1, 2, 3): - for y in (0, 1, 2, 3): - rows.append(f"{x},{y},{x + 10 * y**2}") - filename.write_text("\n".join(rows) + "\n", encoding="utf-8") - - original = Function.from_regular_grid_csv( - str(filename), ["x", "y"], "z", extrapolation="constant", interpolation="akima" - ) - assert original._grid_method == "pchip" - - restored = Function.from_dict(original.to_dict()) - assert restored.get_interpolation_method() == "regular_grid" - assert getattr(restored, "_grid_method", "linear") == "pchip" - assert np.isclose(restored(0.5, 1.5), original(0.5, 1.5)) diff --git a/tests/unit/mathutils/test_regular_grid.py b/tests/unit/mathutils/test_regular_grid.py new file mode 100644 index 000000000..aaba598b5 --- /dev/null +++ b/tests/unit/mathutils/test_regular_grid.py @@ -0,0 +1,342 @@ +"""Tests of the regular-grid interpolation: the ``_RegularGrid`` helper on its +own, and the way ``Function`` finds a grid in its data and uses it.""" + +import warnings + +import numpy as np +import pytest +from scipy.interpolate import RegularGridInterpolator + +from rocketpy import Function +from rocketpy.mathutils._regular_grid import _RegularGrid +from rocketpy.units import convert_units + + +def _points(axes, values): + """Write values on a grid as a table of points, one row per node.""" + mesh = np.meshgrid(*axes, indexing="ij") + return np.column_stack([m.ravel() for m in mesh] + [np.ravel(values)]) + + +@pytest.fixture +def plane_points(): + """``z = x + 10 * y`` on a 4x4 grid, as a table of points.""" + axis = np.linspace(0.0, 3.0, 4) + x_grid, y_grid = np.meshgrid(axis, axis, indexing="ij") + return _points([axis, axis], x_grid + 10 * y_grid) + + +# ----------------------------------------------------------------- _RegularGrid + + +def test_from_points_reads_rows_in_any_order(): + axes = [np.array([0.0, 1.0, 2.0]), np.array([-1.0, 0.0]), np.array([5.0, 7.0])] + values = np.arange(12.0).reshape(3, 2, 2) + rows = _points(axes, values) + shuffled = rows[np.random.default_rng(0).permutation(len(rows))] + + grid = _RegularGrid.from_points(shuffled) + + assert grid is not None + for found, expected in zip(grid.axes, axes): + assert np.array_equal(found, expected) + assert np.array_equal(grid.values, values) + + +@pytest.mark.parametrize( + "points", + [ + _points([[0.0, 1.0, 2.0], [0.0, 1.0]], np.arange(6.0))[:-1], # a node missing + np.vstack( + [ + _points([[0.0, 1.0], [0.0, 1.0]], np.arange(4.0))[:-1], + [[0.0, 0.0, 9.0]], # a node repeated in place of another + ] + ), + np.array([[0.0, 5.0, 1.0], [1.0, 5.0, 2.0], [2.0, 5.0, 3.0]]), # y never varies + np.array([[0.0, 1.0], [1.0, 2.0], [2.0, 4.0]]), # a single variable + np.array( + [[0.0, 0.0, 1.0], [1.0, np.nan, 2.0], [0.0, 1.0, 3.0], [1.0, 1.0, 4.0]] + ), + ], +) +def test_from_points_rejects_what_is_not_a_grid(points): + assert _RegularGrid.from_points(points) is None + + +@pytest.mark.parametrize("method", ["shepard", "rbf"]) +def test_from_points_leaves_scattered_methods_alone(plane_points, method): + assert _RegularGrid.from_points(plane_points, method) is None + + +@pytest.mark.parametrize( + "requested, expected", + [(None, "linear"), ("linear", "linear"), ("spline", "cubic"), ("akima", "pchip")], +) +def test_method_names_are_mapped_onto_the_grid(plane_points, requested, expected): + assert _RegularGrid.from_points(plane_points, requested).method == expected + + +@pytest.mark.parametrize("method", ["akima", "spline", "pchip", "cubic"]) +def test_a_coarse_grid_falls_back_to_linear(method): + """The smooth methods need four points per axis. SciPy raises below that.""" + axis = np.linspace(0.0, 2.0, 3) + points = _points([axis, axis], np.zeros((3, 3))) + with pytest.warns(UserWarning, match="falling back to 'linear'"): + grid = _RegularGrid.from_points(points, method, name="cN") + assert grid.method == "linear" + + +def test_an_unknown_method_says_so(plane_points): + with pytest.warns(UserWarning, match="not supported for the regular grid"): + grid = _RegularGrid.from_points(plane_points, "splien") + assert grid.method == "linear" + + +@pytest.mark.parametrize("extrapolation", ["constant", "natural", "zero"]) +@pytest.mark.parametrize("sizes", [(5, 2), (4, 3, 6), (3, 2, 4, 3)]) +def test_single_point_lookup_matches_scipy(sizes, extrapolation): + """The single-point evaluation must give SciPy's values inside the grid, on + its lines and edges, and outside it for every extrapolation.""" + rng = np.random.default_rng(len(sizes)) + axes = [np.sort(rng.uniform(-1, 1, size)) for size in sizes] + values = rng.normal(size=sizes) + grid = _RegularGrid(axes, values, "linear", extrapolation) + reference = RegularGridInterpolator( + axes, values, bounds_error=False, fill_value=None + ) + + points = rng.uniform(-1.6, 1.6, size=(200, len(sizes))) + on_the_grid = [ + [axis[0] for axis in axes], + [axis[-1] for axis in axes], + [axis[len(axis) // 2] for axis in axes], + ] + points = np.vstack([points, on_the_grid]) + + for point in points: + inside = all(axis[0] <= x <= axis[-1] for axis, x in zip(axes, point)) + if extrapolation == "zero" and not inside: + expected = 0.0 + elif extrapolation == "constant": + clamped = [np.clip(x, axis[0], axis[-1]) for axis, x in zip(axes, point)] + expected = reference(clamped)[0] + else: + expected = reference(point)[0] + assert grid.evaluate(*point) == pytest.approx(expected, rel=1e-10, abs=1e-10) + + columns = [points[:, i] for i in range(len(sizes))] + one_by_one = [grid.evaluate(*point) for point in points] + assert grid.evaluate(*columns) == pytest.approx(one_by_one) + + +def test_points_from_axes_accepts_any_axis_order(): + rows = _RegularGrid.points_from_axes( + [[2.0, 0.0, 1.0], [0.0, 1.0]], [[4.0, 5.0], [0.0, 1.0], [2.0, 3.0]] + ) + grid = _RegularGrid.from_points(rows) + assert np.array_equal(grid.values, [[0.0, 1.0], [2.0, 3.0], [4.0, 5.0]]) + + +# --------------------------------------------------------------------- Function + + +def test_function_finds_the_grid_in_points_and_csv(plane_points, tmp_path): + from_points = Function(plane_points[::-1], ["x", "y"], "z") + + filename = tmp_path / "plane.csv" + np.savetxt(filename, plane_points, delimiter=",", header="x,y,z", comments="") + from_csv = Function(str(filename)) + + for function in (from_points, from_csv): + assert function.is_regular_grid + assert function.get_interpolation_method() == "linear" + assert function(1.5, 0.25) == pytest.approx(4.0) + assert function.get_value_opt(1.5, 0.25) == pytest.approx(4.0) + + +def test_function_keeps_the_order_of_the_given_points(plane_points): + rows = plane_points[::-1] + assert np.array_equal(Function(rows, ["x", "y"], "z").get_source(), rows) + + +def test_incomplete_grid_stays_scattered(plane_points): + function = Function(plane_points[:-1], ["x", "y"], "z") + assert not function.is_regular_grid + assert function.get_interpolation_method() == "shepard" + + +def test_one_input_is_never_a_grid(): + function = Function([[0.0, 1.0], [1.0, 2.0], [2.0, 4.0]]) + assert not function.is_regular_grid + assert function.get_interpolation_method() == "spline" + + +@pytest.mark.parametrize("method", ["shepard", "rbf"]) +def test_scattered_interpolation_is_an_escape_hatch(plane_points, method): + function = Function(plane_points, ["x", "y"], "z", interpolation=method) + assert not function.is_regular_grid + assert function.get_interpolation_method() == method + + +def test_changing_the_method_reads_the_data_again(plane_points): + function = Function(plane_points, ["x", "y"], "z") + + function.set_interpolation("shepard") + assert not function.is_regular_grid + assert function(1.5, 0.25) != pytest.approx(4.0, abs=1e-9) + + function.set_interpolation("akima") + assert function.is_regular_grid + assert function.get_interpolation_method() == "pchip" + assert function(1.5, 0.25) == pytest.approx(4.0) + + +@pytest.mark.parametrize( + "extrapolation, expected", [("zero", 0.0), ("constant", 33.0), ("natural", 44.0)] +) +def test_changing_the_extrapolation_reaches_the_grid( + plane_points, extrapolation, expected +): + function = Function(plane_points, ["x", "y"], "z") + function.set_extrapolation(extrapolation) + assert function.get_value_opt(4.0, 4.0) == pytest.approx(expected) + assert function(4.0, 4.0) == pytest.approx(expected) + + +def test_arithmetic_keeps_the_grid(plane_points): + grid = Function(plane_points, ["x", "y"], "z", interpolation="akima") + value = grid.get_value_opt(1.5, 0.25) + results = [ + (grid * 2, 2 * value), + (2 * grid, 2 * value), + (grid + 1, value + 1), + (1 - grid, 1 - value), + (grid / 2, value / 2), + (-grid, -value), + (grid + grid, 2 * value), + ] + for result, expected in results: + assert result.is_regular_grid + assert result.get_interpolation_method() == "pchip" + assert result.get_value_opt(1.5, 0.25) == pytest.approx(expected) + + +def test_copying_a_function_keeps_its_grid_and_method(plane_points): + grid = Function(plane_points, ["x", "y"], "z", interpolation="akima") + copy = Function(grid) + assert copy.is_regular_grid + assert copy.get_interpolation_method() == "pchip" + assert copy(1.5, 0.25) == pytest.approx(grid(1.5, 0.25)) + + +def test_a_new_source_is_read_on_its_own_terms(plane_points): + function = Function(plane_points, ["x", "y"], "z") + function.set_source( + np.array([[0.0, 0.0, 1.0], [1.0, 0.0, 2.0], [0.0, 1.0, 3.0], [2.0, 2.0, 9.0]]) + ) + assert not function.is_regular_grid + assert function(0.0, 0.0) == pytest.approx(1.0) + + +def test_discretizing_two_inputs_gives_a_grid_that_survives_saving(): + function = Function(lambda x, y: x + 10 * y, ["x", "y"], "z") + function.set_discrete(lower=[0, 0], upper=[3, 3], samples=6) + assert function.is_regular_grid + assert function(1.5, 0.25) == pytest.approx(4.0) + + restored = Function.from_dict(function.to_dict()) + assert restored.is_regular_grid + assert restored(1.5, 0.25) == pytest.approx(4.0) + + +def test_save_and_load_keep_the_grid_and_its_method(plane_points): + original = Function(plane_points, ["x", "y"], "z", interpolation="akima") + restored = Function.from_dict(original.to_dict()) + assert restored.is_regular_grid + assert restored.get_interpolation_method() == "pchip" + assert restored(0.5, 1.5) == pytest.approx(original(0.5, 1.5)) + + +def test_a_unit_conversion_keeps_the_grid(plane_points): + grid = Function(plane_points, ["x (m)", "y"], "z", interpolation="akima") + converted = convert_units(grid, "m", "ft", axis=0) + assert converted.is_regular_grid + assert converted.get_interpolation_method() == "pchip" + + +def test_deep_copy_evaluates_its_own_grid(plane_points): + from copy import deepcopy # pylint: disable=import-outside-toplevel + + original = Function(plane_points, ["x", "y"], "z") + copy = deepcopy(original) + copy.set_extrapolation("zero") + assert copy.get_value_opt(9.0, 9.0) == 0.0 + assert original.get_value_opt(9.0, 9.0) == pytest.approx(99.0) + + +# ------------------------------------------------------------------ deprecations + + +def test_axes_and_values_with_the_old_flag_still_work(): + axis = np.array([0.0, 1.0, 2.0]) + x_grid, y_grid = np.meshgrid(axis, axis, indexing="ij") + with pytest.warns(DeprecationWarning, match="regular_grid"): + function = Function( + ([axis, axis], 2 * x_grid + 3 * y_grid), + ["x", "y"], + "z", + interpolation="regular_grid", + ) + assert function.is_regular_grid + assert function.get_interpolation_method() == "linear" + assert function(0.5, 1.5) == pytest.approx(5.5) + + +def test_from_regular_grid_csv_is_deprecated_but_works(plane_points, tmp_path): + grid_file = tmp_path / "grid.csv" + np.savetxt(grid_file, plane_points, delimiter=",", header="x,y,z", comments="") + scattered_file = tmp_path / "scattered.csv" + np.savetxt( + scattered_file, plane_points[:-1], delimiter=",", header="x,y,z", comments="" + ) + + with pytest.warns(DeprecationWarning): + function = Function.from_regular_grid_csv( + str(grid_file), ["x", "y"], "z", "constant" + ) + assert function.is_regular_grid + assert function(1.5, 0.25) == pytest.approx(4.0) + + with pytest.warns(DeprecationWarning): + assert ( + Function.from_regular_grid_csv( + str(scattered_file), ["x", "y"], "z", "constant" + ) + is None + ) + + +@pytest.mark.parametrize("as_pair", [True, False]) +def test_files_saved_with_the_old_flag_still_load(plane_points, as_pair): + axis = np.linspace(0.0, 3.0, 4) + source = ( + [[axis.tolist(), axis.tolist()], plane_points[:, -1].reshape(4, 4).tolist()] + if as_pair + else plane_points.tolist() + ) + saved = { + "source": source, + "title": None, + "inputs": ["x", "y"], + "outputs": ["z"], + "interpolation": "regular_grid", + "extrapolation": "natural", + "grid_method": "pchip", + } + with warnings.catch_warnings(): + warnings.simplefilter("error") + function = Function.from_dict(saved) + assert function.is_regular_grid + assert function.get_interpolation_method() == "pchip" + assert function(1.5, 0.25) == pytest.approx(4.0) diff --git a/tests/unit/rocket/aero_surface/test_aero_coefficient.py b/tests/unit/rocket/aero_surface/test_aero_coefficient.py index e795c02ac..5d20a644e 100644 --- a/tests/unit/rocket/aero_surface/test_aero_coefficient.py +++ b/tests/unit/rocket/aero_surface/test_aero_coefficient.py @@ -1,5 +1,7 @@ """Unit tests for the AeroCoefficient minimal-dimension coefficient store.""" +import functools + import pytest from rocketpy import Function @@ -17,7 +19,6 @@ def test_constant_coefficient_is_zero_flagged(): zero = AeroCoefficient(0, (), name="cD") assert zero.is_zero is True - assert zero.is_zero_coefficient is True assert zero(0.1, 0.2, 0.3, 0, 0, 0, 0) == 0.0 const = AeroCoefficient(0.7, (), name="cD") @@ -61,11 +62,6 @@ def test_unknown_dependency_raises(): AeroCoefficient(lambda x: x, ("bogus",), name="cL") -def test_dom_dim_matches_full_arity(): - coeff = AeroCoefficient(0, (), name="cD") - assert coeff.__dom_dim__ == len(IV) - - def test_repr_constant_and_function(): assert "0.5" in repr(AeroCoefficient(0.5, (), name="cD")) function_repr = repr(AeroCoefficient(lambda mach: mach, ("mach",), name="cL")) @@ -120,7 +116,7 @@ def test_from_input_named_subset_callable(): def test_from_input_rejects_unmappable_callable(): - with pytest.raises(ValueError, match="callable must accept"): + with pytest.raises(ValueError, match="Cannot tell which variables"): AeroCoefficient(lambda x, y, z: x, name="cL") @@ -142,11 +138,26 @@ def test_from_input_1d_function_infers_mach(): def test_from_input_function_with_bad_dimension_raises(): - f = Function(lambda a, b: a + b, ["alpha", "beta"], "cL") + f = Function(lambda a, b: a + b, ["x", "y"], "cL") with pytest.raises(ValueError, match="must have 7 input arguments"): AeroCoefficient(f, name="cL") +def test_from_input_function_with_named_inputs(): + """A Function whose inputs are named after the variables depends on them.""" + f = Function(lambda m, a: 10 * m + a, ["mach", "alpha"], "cL") + coeff = AeroCoefficient(f, name="cL") + assert coeff.depends_on == ("mach", "alpha") + assert coeff(0.2, 0, 0.5, 0, 0, 0, 0) == pytest.approx(5.2) + + +def test_evaluator_takes_only_the_given_variables(): + coeff = AeroCoefficient(lambda mach, alpha: 10 * mach + alpha, name="cL") + evaluate = coeff.evaluator(["alpha", "beta", "mach"]) + assert evaluate(0.2, 99, 0.5) == pytest.approx(5.2) + assert AeroCoefficient(0.3).evaluator(["alpha"])(99) == 0.3 + + # -- constructor inference: CSV path ----------------------------------------------------- @@ -201,13 +212,97 @@ def test_to_dict_from_dict_preserves_axes(): # -- _infer_single_var fallbacks ---------------------------------------------- -def test_infer_single_var_unmatched_label_defaults_to_first(): +def test_infer_single_var_unmatched_label_gives_none(): f = Function(lambda gamma: gamma, "gamma", "cD") - assert AeroCoefficient._infer_single_var(f, IV) == IV[0] + assert AeroCoefficient._infer_single_var(f, IV) is None -def test_infer_single_var_missing_inputs_defaults_to_first(): +def test_infer_single_var_missing_inputs_gives_none(): class NoInputs: pass - assert AeroCoefficient._infer_single_var(NoInputs(), IV) == IV[0] + assert AeroCoefficient._infer_single_var(NoInputs(), IV) is None + + +@pytest.mark.parametrize( + "label, expected", + [ + ("mach", "mach"), + ("Mach Number", "mach"), + ("Pitch Rate", "pitch_rate"), + ("Angle of attack alpha (rad)", "alpha"), + ("Alphabet soup", None), + ("machine", None), + ("time (s)", None), + ], +) +def test_infer_single_var_matches_whole_words_only(label, expected): + f = Function([[0, 0.4], [1, 0.6]], label, "cD") + assert AeroCoefficient._infer_single_var(f, IV) == expected + + +def _model_with_a_constant(alpha, mach, slope=2.0): + return slope * alpha * (1 + mach) + + +def _seven_arguments_and_a_constant(a, b, m, re, q, r, p, gain=1.0): # pylint: disable=unused-argument + return gain * (a + m) + + +@pytest.mark.parametrize( + "function, depends_on, expected", + [ + (_model_with_a_constant, ("alpha", "mach"), 0.4), + (functools.partial(_model_with_a_constant, slope=3.0), ("alpha", "mach"), 0.6), + (lambda alpha, *, slope=2.0: slope * alpha, ("alpha",), 0.2), + (lambda alpha, **options: 2 * alpha, ("alpha",), 0.2), + (_seven_arguments_and_a_constant, tuple(IV), 1.1), + ( + (lambda x, y, slope=2.0: slope * x * (1 + y), ["alpha", "mach"]), + ("alpha", "mach"), + 0.4, + ), + ], +) +def test_function_arguments_with_a_default_are_not_variables( + function, depends_on, expected +): + """A function may carry constants of its own as arguments with a default + value; only the arguments it must be given count as variables.""" + coeff = AeroCoefficient(function, name="cN") + assert coeff.depends_on == depends_on + assert coeff(0.1, 0, 1.0, 0, 0, 0, 0) == pytest.approx(expected) + + +@pytest.mark.parametrize("function", [lambda *args: 2 * args[0], lambda M: 0.5]) +def test_function_that_names_no_variable_explains_what_to_do(function): + with pytest.raises(ValueError, match="Cannot tell which variables"): + AeroCoefficient(function, name="cN") + + +def test_slope_matches_the_analytic_derivative(): + coefficient = AeroCoefficient(lambda alpha, mach: 2 * alpha * (1 + mach) + alpha**3) + slope = coefficient.slope("alpha", "mach", at={"alpha": 0.1}) + assert slope(0.5) == pytest.approx(2 * 1.5 + 3 * 0.1**2, rel=1e-6) + assert coefficient.slope("alpha").get_value_opt(0) == pytest.approx(2.0) + + +@pytest.mark.parametrize( + "call", + [ + lambda c: c.slope("alfa", "mach"), + lambda c: c.slope("alpha", "mahc"), + lambda c: c.slope("alpha", "mach", at={"bta": 1.0}), + lambda c: c.slice("alpha", at={"bta": 1.0}), + ], +) +def test_slice_and_slope_reject_unknown_names_when_built(call): + coefficient = AeroCoefficient(lambda alpha, mach: alpha * mach) + with pytest.raises(ValueError, match="no independent variable"): + call(coefficient) + + +def test_slice_rejects_a_repeated_name(): + coefficient = AeroCoefficient(lambda alpha, mach: alpha * mach) + with pytest.raises(ValueError, match="more than once"): + coefficient.slice("alpha", "alpha") diff --git a/tests/unit/rocket/aero_surface/test_aero_surfaces.py b/tests/unit/rocket/aero_surface/test_aero_surfaces.py index f264ce40b..d7159633e 100644 --- a/tests/unit/rocket/aero_surface/test_aero_surfaces.py +++ b/tests/unit/rocket/aero_surface/test_aero_surfaces.py @@ -1,5 +1,6 @@ from unittest.mock import patch +import numpy as np import pytest from rocketpy import NoseCone @@ -135,3 +136,29 @@ def test_tail_bottom_radius_setter(calisto_tail): def test_tail_top_radius_setter(calisto_tail): calisto_tail.top_radius = 0.1 assert calisto_tail.top_radius == 0.1 + + +@pytest.mark.parametrize( + "surface_fixture, attribute, value", + [ + ("calisto_nose_cone", "length", 0.8), + ("calisto_tail", "bottom_radius", 0.03), + ("calisto_trapezoidal_fins", "n", 3), + ], +) +def test_setter_rebuilds_the_coefficients(surface_fixture, attribute, value, request): + """A setter updates the slope and center of pressure a rocket reads.""" + surface = request.getfixturevalue(surface_fixture) + before = (surface.cN_alpha.slice("mach")(0.3), surface.aerodynamic_center(0.3)) + setattr(surface, attribute, value) + after = (surface.cN_alpha.slice("mach")(0.3), surface.aerodynamic_center(0.3)) + assert after != pytest.approx(before) + assert surface._version == 1 + + +@pytest.mark.parametrize("surface_fixture", ["calisto_nose_cone", "calisto_tail"]) +def test_rocket_radius_setter_updates_the_reference_area(surface_fixture, request): + surface = request.getfixturevalue(surface_fixture) + surface.rocket_radius = 0.1 + assert surface.reference_area == pytest.approx(np.pi * 0.1**2) + assert surface.reference_length == pytest.approx(0.2) diff --git a/tests/unit/rocket/aero_surface/test_barrowman_generic_equivalence.py b/tests/unit/rocket/aero_surface/test_barrowman_generic_equivalence.py index ebc00c72a..57a0777c1 100644 --- a/tests/unit/rocket/aero_surface/test_barrowman_generic_equivalence.py +++ b/tests/unit/rocket/aero_surface/test_barrowman_generic_equivalence.py @@ -2,10 +2,11 @@ After the refactor, every aerodynamic surface (Barrowman or generic) exposes the coefficient derivatives ``cN_alpha``/``cY_beta`` and the -``center_of_pressure_z`` accessor used by the rocket's center-of-pressure / -stability-margin computation. (Barrowman surfaces still compute their flight -forces with the classic geometric method; the derivatives feed only the -stability diagnostics.) These tests pin the properties the refactor guarantees. +``aerodynamic_center`` accessor used by the rocket's center-of-pressure / +stability-margin computation. Barrowman surfaces compute their flight forces +with the classic geometric method, and their public ``cN``/``cY``/``cA``/``cl`` +coefficients are that same method written as coefficients. These tests pin the +properties the refactor guarantees. """ import warnings @@ -13,12 +14,21 @@ import numpy as np import pytest -from rocketpy import LinearGenericSurface, NoseCone, Tail, TrapezoidalFins +from rocketpy import ( + LinearGenericSurface, + NoseCone, + Tail, + TrapezoidalFin, + TrapezoidalFins, +) +from rocketpy.mathutils import Vector def test_barrowman_derived_cp_matches_geometric_cp(): - """The derived ``center_of_pressure_z`` diagnostic must reproduce the - geometric cp of each Barrowman surface.""" + """The derived ``aerodynamic_center`` diagnostic must reproduce the + geometric cp of each Barrowman surface. It is given along the body z-axis + (positive toward the nose), while ``cpz`` is measured from the nose toward + the tail, hence the sign.""" nose = NoseCone( length=0.55829, kind="vonkarman", base_radius=0.0635, rocket_radius=0.0635 ) @@ -33,9 +43,9 @@ def test_barrowman_derived_cp_matches_geometric_cp(): for mach in (0.0, 0.5, 0.9): assert ( pytest.approx( - surface.center_of_pressure_z.get_value_opt(mach), rel=1e-6, abs=1e-9 + surface.aerodynamic_center.get_value_opt(mach), rel=1e-6, abs=1e-9 ) - == surface.cpz + == -surface.cpz ) # The normal-force slope derivative must equal the Barrowman clalpha. assert pytest.approx( @@ -162,3 +172,87 @@ def test_barrowman_surface_uses_geometric_compute_path(): nose.compute_forces_and_moments.__func__ is _BarrowmanSurface.compute_forces_and_moments ) + + +# Airflow relative to the surface, in the body frame (m/s): small and large +# angles, both planes at once, no crossflow and tail-first flow. +_STREAM_VELOCITIES = [ + (3.0, -2.0, -100.0), + (20.0, 35.0, -80.0), + (-50.0, 10.0, -60.0), + (5.0, 0.0, -100.0), + (0.0, 0.0, -100.0), + (30.0, 30.0, 40.0), +] + + +def _barrowman_surfaces(): + return [ + NoseCone( + length=0.55829, kind="vonkarman", base_radius=0.0635, rocket_radius=0.0635 + ), + Tail( + top_radius=0.0635, bottom_radius=0.0435, length=0.060, rocket_radius=0.0635 + ), + TrapezoidalFins( + n=4, + span=0.100, + root_chord=0.120, + tip_chord=0.040, + rocket_radius=0.0635, + cant_angle=1.0, + ), + TrapezoidalFin( + angular_position=30, + span=0.100, + root_chord=0.120, + tip_chord=0.040, + rocket_radius=0.0635, + cant_angle=2.0, + ), + ] + + +@pytest.mark.parametrize("stream_velocity", _STREAM_VELOCITIES) +def test_public_coefficients_are_what_flies(stream_velocity): + """The forces used in the simulation (the fast classic Barrowman computation) + must equal the surface's public ``cN``, ``cY`` and ``cA`` coefficients times + the dynamic pressure and the reference area, so that what a user reads or + plots is what flies.""" + rho, mach = 1.2, 0.3 + stream = Vector(stream_velocity) + speed = abs(stream) + # The coefficients take the angles of the rocket's velocity relative to the air + alpha = np.arctan2(-stream[1], -stream[2]) + beta = np.arctan2(-stream[0], -stream[2]) + args = (alpha, beta, mach, 0.0, 0.0, 0.0, 0.0) + + for surface in _barrowman_surfaces(): + r1, r2, r3, *_ = surface.compute_forces_and_moments( + stream, speed, mach, rho, Vector([0, 0, 0]), (0, 0, 0) + ) + scale = 0.5 * rho * speed**2 * surface.reference_area + assert r1 == pytest.approx(scale * surface.cY(*args), rel=1e-12, abs=1e-9) + assert r2 == pytest.approx(-scale * surface.cN(*args), rel=1e-12, abs=1e-9) + assert r3 == pytest.approx(-scale * surface.cA(*args), rel=1e-12, abs=1e-9) + + +def test_public_roll_coefficient_is_what_flies(): + """A fin set's roll moment in the simulation must equal its public ``cl`` + (cant forcing plus roll damping) times the dynamic pressure, the reference + area and the reference length; an individual fin's ``cl`` is its roll + damping.""" + rho, mach, speed, roll = 1.2, 0.3, 100.0, 6.0 + stream = Vector([0.0, 0.0, -speed]) # no crossflow: only the roll moment is left + nose, _, fins, fin = _barrowman_surfaces() + + for surface in (nose, fins, fin): + moment = surface.compute_forces_and_moments( + stream, speed, mach, rho, Vector([0, 0, 0]), (0, 0, roll) + )[5] + reduced_roll = roll * surface.reference_length / (2 * speed) + scale = 0.5 * rho * speed**2 * surface.reference_area * surface.reference_length + expected = scale * surface.cl(0.0, 0.0, mach, 0.0, 0.0, 0.0, reduced_roll) + assert moment == pytest.approx(expected, rel=1e-12, abs=1e-12) + assert nose.cl.is_zero + assert fins.cl_0(0, 0, mach, 0, 0, 0, 0) != 0 diff --git a/tests/unit/rocket/aero_surface/test_controllable_generic_surface.py b/tests/unit/rocket/aero_surface/test_controllable_generic_surface.py index 8e893b14c..5503934e1 100644 --- a/tests/unit/rocket/aero_surface/test_controllable_generic_surface.py +++ b/tests/unit/rocket/aero_surface/test_controllable_generic_surface.py @@ -138,3 +138,44 @@ def test_active_during_callable_dropped_when_pickling_disabled(): active_during=lambda t, flight: t < 1.0, ) assert surface.to_dict(allow_pickle=False)["active_during"] == "always" + + +def test_controls_and_coefficients_round_trip_through_dict(): + """Saving and loading keeps the control names and the coefficient values, + including for coefficients given in the wind frame.""" + surface = ControllableGenericSurface( + reference_area=0.01, + reference_length=0.1, + coefficients={ + "cL": lambda alpha, canard: 2 * alpha + 0.5 * canard, + "cD": 0.3, + }, + controls=("canard", "elevon"), + force_convention="wind", + ) + surface.set_control("canard", 0.2) + + restored = ControllableGenericSurface.from_dict(surface.to_dict()) + restored.set_control("canard", 0.2) + + assert restored.control_variables == ["canard", "elevon"] + args = (0.05, 0.02, 0.3, 0.0, 0.0, 0.0, 0.0, 0.2, 0.0) + for name in ("cN", "cY", "cA"): + assert getattr(restored, name)(*args) == pytest.approx( + getattr(surface, name)(*args) + ) + + +def test_controllable_surface_from_csv_reads_the_control_column(tmp_path): + csv_file = tmp_path / "canard.csv" + rows = [f"{d},{m},{1.5 * d * (1 + m)}" for d in (-0.2, 0.0, 0.2) for m in (0, 1, 2)] + csv_file.write_text("canard,mach,cN\n" + "\n".join(rows) + "\n") + + surface = ControllableGenericSurface.from_csv( + str(csv_file), 1.0, 1.0, controls=("canard",) + ) + surface.set_control("canard", 0.1) + + assert surface.cN.depends_on == ("canard", "mach") + args = surface._coefficient_arguments(0.0, 0.0, 1.0, 0, 0, 0, 0) + assert surface.cN(*args) == pytest.approx(0.3) diff --git a/tests/unit/rocket/aero_surface/test_generic_surfaces.py b/tests/unit/rocket/aero_surface/test_generic_surfaces.py index 5bbe81395..b44560173 100644 --- a/tests/unit/rocket/aero_surface/test_generic_surfaces.py +++ b/tests/unit/rocket/aero_surface/test_generic_surfaces.py @@ -1,6 +1,8 @@ import json +import warnings from types import SimpleNamespace +import numpy as np import pytest from rocketpy import Function, GenericSurface, LinearGenericSurface @@ -105,10 +107,11 @@ def test_csv_independent_variables_accept_any_order(tmp_path): csv_function = generic_surface.cN.function assert generic_surface.cN(1, 0, 2, 0, 0, 0, 0) == pytest.approx(12) - assert csv_function.get_interpolation_method() == "regular_grid" + assert csv_function.is_regular_grid -POINTS = [[0, 0], [1, 1], [2, 4], [3, 9]] +# A one-input table, given with the name of its variable +POINTS = ([[0, 0], [1, 1], [2, 4], [3, 9]], ["mach"]) def test_interpolation_extrapolation_scalar_applies_to_all(): @@ -146,7 +149,9 @@ def test_interpolation_extrapolation_per_coefficient_dict(): def test_prebuilt_function_interpolation_left_unchanged(): """A pre-built Function keeps its own interpolation/extrapolation when none is requested, and is copied (not mutated) when they are overridden.""" - source = Function(POINTS, interpolation="spline", extrapolation="zero") + source = Function( + POINTS[0], "mach", "cN", interpolation="spline", extrapolation="zero" + ) unchanged = GenericSurface(REFERENCE_AREA, REFERENCE_LENGTH, {"cN": source}) assert unchanged.cN.function.get_interpolation_method() == "spline" @@ -206,9 +211,9 @@ def test_grid_csv_interpolation_maps_to_scipy_method( ) function = gs.cN.function # The Function stays a regular grid (not clobbered to shepard) ... - assert function.get_interpolation_method() == "regular_grid" - # ... with the mapped scipy method threaded through. - assert getattr(function, "_grid_method", "linear") == expected_grid_method + assert function.is_regular_grid + # ... interpolated with the mapped grid method. + assert function.get_interpolation_method() == expected_grid_method def test_grid_csv_cubic_differs_from_linear(tmp_path): @@ -472,3 +477,907 @@ def test_generic_surface_preset_active_during_round_trips(): REFERENCE_AREA, REFERENCE_LENGTH, {"cN": 0}, active_during="power_on" ) assert _rpy_round_trip(gs).active_during == "power_on" + + +# Directions of the rocket's velocity relative to the air, in the body frame: +# small and large combined angles, tail-first flight and flow along one axis. +_FLOW_DIRECTIONS = [ + (0.0, 0.1, 1.0), + (0.1, 0.0, 1.0), + (0.1, 0.1, 1.0), + (0.6, 0.6, 1.0), + (-2.0, 3.0, 0.4), + (0.0, 0.1, -1.0), + (0.3, -0.2, -1.0), + (1.0, 0.0, 0.0), + (0.0, 1.0, 0.0), +] + + +def _wind_input_force(coefficients, direction): + """Body-frame force of a unit-area surface in a unit dynamic pressure flow.""" + surface = GenericSurface(1.0, 1.0, coefficients) + velocity = Vector(direction) + velocity = velocity / abs(velocity) + force = surface.compute_forces_and_moments( + -velocity, + 1.0, + 0.1, + 2.0, + Vector([0, 0, 0]), + (0, 0, 0), + Function(1.0), + Function(1.0), + 0, + )[:3] + return Vector(force), velocity + + +@pytest.mark.parametrize("direction", _FLOW_DIRECTIONS) +def test_wind_frame_drag_opposes_the_velocity(direction): + """A drag coefficient given in the wind frame must give a force pointing + exactly against the velocity, at any combination of angle of attack and + sideslip, including tail-first flight.""" + force, velocity = _wind_input_force({"cD": 1.0}, direction) + assert list(force) == pytest.approx(list(-velocity), abs=1e-12) + + +@pytest.mark.parametrize("direction", _FLOW_DIRECTIONS) +def test_wind_frame_lift_and_side_force_are_perpendicular_to_velocity(direction): + """Lift and side force must be unit forces perpendicular to the velocity and + to each other, with the lift lying in the body y-z plane.""" + lift, velocity = _wind_input_force({"cL": 1.0}, direction) + side, _ = _wind_input_force({"cQ": 1.0}, direction) + assert abs(lift) == pytest.approx(1.0) + assert abs(side) == pytest.approx(1.0) + assert lift @ velocity == pytest.approx(0.0, abs=1e-12) + assert side @ velocity == pytest.approx(0.0, abs=1e-12) + assert lift @ side == pytest.approx(0.0, abs=1e-12) + assert lift[0] == pytest.approx(0.0, abs=1e-12) + + +@pytest.mark.parametrize( + "alpha, beta", [(0.1, 0.0), (0.0, 0.2), (0.3, 0.2), (2.8, 3.0)] +) +def test_wind_frame_views_recover_the_wind_input(alpha, beta): + """The ``cL``/``cD``/``cQ`` views of a surface built from wind-frame + coefficients must give those coefficients back.""" + surface = GenericSurface( + 1.0, + 1.0, + { + "cL": lambda alpha, mach: 2 * alpha, + "cD": 0.4, + "cQ": lambda beta: -1.5 * beta, + }, + ) + args = (alpha, beta, 0.5, 0, 0, 0, 0) + assert surface.cL(*args) == pytest.approx(2 * alpha) + assert surface.cD(*args) == pytest.approx(0.4) + assert surface.cQ(*args) == pytest.approx(-1.5 * beta) + + +def test_wind_input_depends_only_on_what_it_uses(): + """A wind-frame input converted to the body frame depends on the angle of + attack and sideslip plus whatever the input itself uses, so a constant drag + does not make the surface look up the Reynolds number.""" + surface = GenericSurface(1.0, 1.0, {"cD": 0.5}) + assert surface.cA.depends_on == ("alpha", "beta") + assert surface._needs_reynolds is False + + surface = GenericSurface( + 1.0, + 1.0, + { + "cD": ([[0, 0.4], [1, 0.6]], ["mach"]), + "cL": lambda alpha, mach: 2 * alpha, + }, + ) + assert surface.cN.depends_on == ("alpha", "beta", "mach") + + surface = GenericSurface(1.0, 1.0, {"cD": lambda reynolds: 1e-7 * reynolds}) + assert surface._needs_reynolds is True + + +def test_wind_input_honors_interpolation_and_extrapolation(): + """The interpolation and extrapolation given under a wind-frame coefficient's + name must reach its table.""" + table = Function([[0.0, 0.4], [1.0, 0.6]], "mach", "cD", extrapolation="constant") + held = GenericSurface(1.0, 1.0, {"cD": table}) + zeroed = GenericSurface(1.0, 1.0, {"cD": table}, extrapolation={"cD": "zero"}) + outside = (0.0, 0.0, 2.0, 0, 0, 0, 0) # beyond the table + assert held.cA(*outside) == pytest.approx(0.6) + assert zeroed.cA(*outside) == pytest.approx(0.0) + + +def test_wind_views_are_built_once_and_follow_the_coefficients(): + surface = GenericSurface(1.0, 1.0, {"cN": lambda alpha: 2 * alpha, "cA": 0.5}) + view = surface.cD + assert surface.cD is view + surface.cA = surface._as_coefficient(0.8, "cA") + assert surface.cD is not view + assert surface.cD(0, 0, 0, 0, 0, 0, 0) == pytest.approx(0.8) + + +def test_one_input_table_needs_its_variable_named(tmp_path): + """A one-input table must say which variable it is: a Mach curve must never + be read against the angle of attack by default.""" + points = [[0, 0.4], [1, 0.6]] + csv_file = tmp_path / "curve.csv" + csv_file.write_text("0.0,0.4\n1.0,0.6\n") + + for source in (points, str(csv_file), Function(points)): + with pytest.raises(ValueError, match="Cannot tell which variable"): + GenericSurface(1.0, 1.0, {"cA": source}) + + for source in (points, str(csv_file), Function(points)): + surface = GenericSurface(1.0, 1.0, {"cA": (source, ["mach"])}) + assert surface.cA.depends_on == ("mach",) + assert surface.cA(0.3, 0.0, 0.5, 0, 0, 0, 0) == pytest.approx(0.5) + + +def test_coefficient_given_with_the_names_of_its_variables(): + """``(source, variables)`` names the inputs of a table or of a function, in + order, for any number of inputs.""" + points = [[a, m, 2 * a + m] for a in (-0.1, 0.0, 0.1) for m in (0.0, 1.0)] + surface = GenericSurface( + 1.0, + 1.0, + { + "cN": (points, ["alpha", "mach"]), + "cm": (lambda x, y: x - 3 * y, ["mach", "alpha"]), + "cA": (0.5, []), + }, + ) + args = (0.1, 0.0, 1.0, 0, 0, 0, 0) + assert surface.cN.depends_on == ("alpha", "mach") + assert surface.cN(*args) == pytest.approx(1.2) + assert surface.cm.depends_on == ("mach", "alpha") + assert surface.cm(*args) == pytest.approx(0.7) + assert surface.cA(*args) == 0.5 + + with pytest.raises(ValueError, match="1 input.*2 variable name"): + GenericSurface(1.0, 1.0, {"cA": ([[0, 0.4], [1, 0.6]], ["alpha", "mach"])}) + with pytest.raises(ValueError, match="unknown variable"): + GenericSurface(1.0, 1.0, {"cA": ([[0, 0.4], [1, 0.6]], ["speed"])}) + + +def _grid_table(): + """A smooth coefficient sampled on an alpha-Mach grid, as rows and as a block + of values.""" + alphas = np.radians(np.arange(-10, 11, 2.0)) + machs = np.arange(0, 3.01, 0.25) + alpha_grid, mach_grid = np.meshgrid(alphas, machs, indexing="ij") + values = (2 + 0.5 * mach_grid) * np.sin(alpha_grid) + rows = np.column_stack([alpha_grid.ravel(), mach_grid.ravel(), values.ravel()]) + return alphas, machs, values, rows + + +def test_the_same_grid_gives_the_same_coefficient_in_every_form(tmp_path): + """A table on a regular grid must be read the same way from a CSV file, a + list, a numpy array (in any row order) and as axes with a block of values.""" + alphas, machs, values, rows = _grid_table() + csv_file = tmp_path / "cN.csv" + np.savetxt(csv_file, rows, delimiter=",", header="alpha,mach,cN", comments="") + shuffled = np.random.default_rng(1).permutation(rows) + sources = [ + str(csv_file), + (rows.tolist(), ["alpha", "mach"]), + (rows, ["alpha", "mach"]), + (shuffled, ["alpha", "mach"]), + ({"alpha": alphas, "mach": machs}, values), + ({"mach": machs, "alpha": alphas}, values.T), + ] + results = [] + for source in sources: + coefficient = GenericSurface(1.0, 1.0, {"cN": source}).cN + assert coefficient.function.is_regular_grid + results.append(coefficient(0.05, 0.0, 0.6, 0, 0, 0, 0)) + assert results == pytest.approx([results[0]] * len(results)) + assert results[0] == pytest.approx((2 + 0.5 * 0.6) * np.sin(0.05), rel=2e-3) + + +def test_incomplete_grid_and_one_variable_tables_stay_ordinary_tables(): + _, machs, _, rows = _grid_table() + scattered = GenericSurface(1.0, 1.0, {"cN": (rows[:-1], ["alpha", "mach"])}).cN + assert scattered.function.get_interpolation_method() == "linear" + + for source in ( + (np.column_stack([machs, 0.4 + 0.1 * machs]), ["mach"]), + ({"mach": machs}, 0.4 + 0.1 * machs), + ): + curve = GenericSurface(1.0, 1.0, {"cA": source}).cA + assert curve.depends_on == ("mach",) + assert curve.function.get_interpolation_method() == "linear" + assert curve(0.0, 0.0, 0.6, 0, 0, 0, 0) == pytest.approx(0.46) + + +def test_grid_values_must_match_their_axes(): + alphas, machs, values, _ = _grid_table() + with pytest.raises(ValueError, match="The values of cN have shape"): + GenericSurface(1.0, 1.0, {"cN": ({"alpha": alphas, "mach": machs}, values.T)}) + + +def _degrees_table(): + """``cN = (2 + 0.5 * mach) * alpha`` tabulated against the angle of attack in + degrees and Mach, as rows and as a block of values.""" + degrees = np.arange(-10, 11, 2.0) + machs = np.array([0.0, 1.0, 2.0]) + degree_grid, mach_grid = np.meshgrid(degrees, machs, indexing="ij") + values = (2 + 0.5 * mach_grid) * np.radians(degree_grid) + rows = np.column_stack([degree_grid.ravel(), mach_grid.ravel(), values.ravel()]) + return degrees, machs, values, rows + + +def test_angle_in_degrees_in_every_form(tmp_path): + """A source that names its angle ``alpha_deg`` is read in degrees, whichever + form it is given in, while the coefficient is still called in radians.""" + degrees, machs, values, rows = _degrees_table() + csv_file = tmp_path / "cN.csv" + np.savetxt(csv_file, rows, delimiter=",", header="alpha_deg,mach,cN", comments="") + sources = [ + str(csv_file), + (rows, ["alpha_deg", "mach"]), + ({"alpha_deg": degrees, "mach": machs}, values), + lambda alpha_deg, mach: (2 + 0.5 * mach) * np.radians(alpha_deg), + ] + alpha = np.radians(3.0) + for source in sources: + coefficient = GenericSurface(1.0, 1.0, {"cN": source}).cN + assert coefficient.depends_on == ("alpha", "mach") + assert coefficient.in_degrees == ("alpha",) + assert coefficient(alpha, 0.0, 1.0, 0, 0, 0, 0) == pytest.approx(2.5 * alpha) + + curve = Function(np.column_stack([degrees, np.radians(degrees)]), "beta_deg", "cY") + side = GenericSurface(1.0, 1.0, {"cY": curve}).cY + assert side.depends_on == ("beta",) and side.in_degrees == ("beta",) + assert side(0.0, alpha, 0.0, 0, 0, 0, 0) == pytest.approx(alpha) + + +def test_angle_in_degrees_survives_slope_scaling_and_save_and_load(): + _, _, _, rows = _degrees_table() + coefficient = GenericSurface(1.0, 1.0, {"cN": (rows, ["alpha_deg", "mach"])}).cN + alpha = np.radians(3.0) + args = (alpha, 0.0, 1.0, 0, 0, 0, 0) + + # The slope is per radian, whatever unit the table is in + assert coefficient.slope("alpha", "mach")(1.0) == pytest.approx(2.5) + assert (coefficient * 2)(*args) == pytest.approx(5.0 * alpha) + restored = _rpy_round_trip(coefficient) + assert restored.in_degrees == ("alpha",) + assert restored(*args) == pytest.approx(2.5 * alpha) + + with pytest.raises(ValueError, match="more than once"): + GenericSurface(1.0, 1.0, {"cN": (lambda a, b: a, ["alpha", "alpha_deg"])}) + + +def test_angle_axis_in_degrees_read_as_radians_warns(): + """An angle axis that goes beyond what an angle in radians can be warns that + the table looks like it is in degrees; a table in radians does not.""" + degrees, _, _, rows = _degrees_table() + with pytest.warns(UserWarning, match="looks like degrees"): + GenericSurface(1.0, 1.0, {"cN": (rows, ["alpha", "mach"])}) + with warnings.catch_warnings(): + warnings.simplefilter("error") + in_radians = np.column_stack([np.radians(degrees), np.radians(degrees)]) + GenericSurface(1.0, 1.0, {"cN": (in_radians, ["alpha"])}) + GenericSurface(1.0, 1.0, {"cN": (rows, ["alpha_deg", "mach"])}) + # Other variables may be as large as they like + GenericSurface(1.0, 1.0, {"cA": ([[0, 0.4], [10, 0.6]], ["mach"])}) + + +def _write_multi_coefficient_csv(path, header, only_positive_angles=False): + """One table with three coefficients against the angle of attack in degrees + and Mach: ``cN = (2 + 0.5 mach) alpha``, ``cA = 0.4 + 0.1 mach`` and + ``cm = -3 alpha``.""" + degrees = np.arange(0 if only_positive_angles else -10, 11, 2.0) + machs = np.array([0.0, 1.0, 2.0]) + degree_grid, mach_grid = np.meshgrid(degrees, machs, indexing="ij") + alpha_grid = np.radians(degree_grid) + table = np.column_stack( + [ + degree_grid.ravel(), + mach_grid.ravel(), + ((2 + 0.5 * mach_grid) * alpha_grid).ravel(), + (0.4 + 0.1 * mach_grid).ravel(), + (-3 * alpha_grid).ravel(), + ] + ) + np.savetxt(path, table, delimiter=",", header=header, comments="") + + +def test_from_csv_reads_several_coefficients_from_one_file(tmp_path): + csv_file = tmp_path / "aero.csv" + _write_multi_coefficient_csv(csv_file, "alpha_deg, mach, cN, cA, cm") + + surface = GenericSurface.from_csv(str(csv_file), 1.3, 0.7, name="One file") + + alpha = np.radians(3.0) + args = (alpha, 0.0, 1.0, 0, 0, 0, 0) + assert surface.name == "One file" + assert surface.reference_area == 1.3 and surface.reference_length == 0.7 + assert surface.cN(*args) == pytest.approx(2.5 * alpha) + assert surface.cA(*args) == pytest.approx(0.5) + assert surface.cm(*args) == pytest.approx(-3 * alpha) + assert surface.cY.is_zero + for name in ("cN", "cA", "cm"): + coefficient = getattr(surface, name) + assert coefficient.depends_on == ("alpha", "mach") + assert coefficient.function.is_regular_grid + + +def test_from_csv_translates_and_ignores_columns(tmp_path): + """With ``columns``, a file written by another program loads as it is: the + listed columns are translated and the others are ignored.""" + csv_file = tmp_path / "export.csv" + _write_multi_coefficient_csv(csv_file, "Alpha,Mach,CN,CA Power-Off,CMY") + columns = {"Alpha": "alpha_deg", "Mach": "mach", "CN": "cN", "CA Power-Off": "cA"} + + surface = GenericSurface.from_csv( + str(csv_file), 1.0, 1.0, columns=columns, active_during="power_off" + ) + + alpha = np.radians(3.0) + assert surface.cN(alpha, 0.0, 1.0, 0, 0, 0, 0) == pytest.approx(2.5 * alpha) + assert surface.cm.is_zero # CMY was not listed + assert surface.active_during == "power_off" + + with pytest.raises(ValueError, match="neither a variable"): + GenericSurface.from_csv(str(csv_file), 1.0, 1.0) + with pytest.raises(ValueError, match="not found"): + GenericSurface.from_csv(str(csv_file), 1.0, 1.0, columns={"Beta": "beta"}) + with pytest.raises(ValueError, match="at least one variable column"): + GenericSurface.from_csv(str(csv_file), 1.0, 1.0, columns={"Mach": "mach"}) + + +def test_table_with_only_positive_angles_warns(tmp_path): + """A force table that starts at zero angle, where it is zero, would give no + force at negative angles: warn. A drag table (not zero there) does not.""" + csv_file = tmp_path / "one_sided.csv" + _write_multi_coefficient_csv( + csv_file, "alpha_deg, mach, cN, cA, cm", only_positive_angles=True + ) + with pytest.warns(UserWarning, match="no negative angles") as caught: + GenericSurface.from_csv(str(csv_file), 1.0, 1.0) + warned = {str(warning.message).split()[3] for warning in caught} + assert warned == {"cN", "cm"} + + +def _total_angle_surface(): + """A surface with a nonlinear normal force, a pitch moment, an axial force + and a roll moment, all given against the total angle of attack.""" + return GenericSurface( + 1.0, + 1.0, + { + "cN": lambda alpha_total, mach: ( + (2 + 0.5 * mach) * np.sin(alpha_total) + 1.5 * np.sin(alpha_total) ** 3 + ), + "cm": lambda alpha_total: -3 * np.sin(alpha_total), + "cA": lambda alpha_total: 0.4 + alpha_total**2, + "cl": 0.01, + }, + ) + + +@pytest.mark.parametrize( + "stream_velocity", + [ + (3.0, -2.0, -100.0), + (20.0, 35.0, -80.0), + (-50.0, 10.0, -60.0), + (5.0, 0.0, -100.0), + (0.0, -7.0, -100.0), + (0.0, 0.0, -100.0), + (30.0, 30.0, 40.0), + ], +) +def test_total_angle_convention_gives_the_force_along_the_crossflow(stream_velocity): + """Coefficients against the total angle of attack: the normal force must act + along the crossflow of the air and the pitch moment about the axis across + it, whatever the direction of the wind, tail-first flow included.""" + surface = _total_angle_surface() + mach, rho = 0.5, 2.0 + stream = Vector(stream_velocity) + speed = abs(stream) + r1, r2, r3, m1, m2, m3 = surface.compute_forces_and_moments( + stream, speed, mach, rho, Vector([0, 0, 0]), (0, 0, 0), None, None, 0 + ) + + # Built from the velocity directly, with no partial angles involved + u_x, u_y, u_z = (-component / speed for component in stream_velocity) + crossflow = np.hypot(u_x, u_y) + alpha_total = np.arctan2(crossflow, u_z) + scale = 0.5 * rho * speed**2 + normal = scale * ( + (2 + 0.5 * mach) * np.sin(alpha_total) + 1.5 * np.sin(alpha_total) ** 3 + ) + moment = scale * -3 * np.sin(alpha_total) + direction = (u_x / crossflow, u_y / crossflow) if crossflow else (0.0, 0.0) + + assert r1 == pytest.approx(-normal * direction[0], abs=1e-9 * scale) + assert r2 == pytest.approx(-normal * direction[1], abs=1e-9 * scale) + assert r3 == pytest.approx(-scale * (0.4 + alpha_total**2)) + assert m1 == pytest.approx(moment * direction[1], abs=1e-9 * scale) + assert m2 == pytest.approx(-moment * direction[0], abs=1e-9 * scale) + assert m3 == pytest.approx(scale * 0.01) + + +def test_total_angle_convention_slopes_and_dependencies(): + surface = _total_angle_surface() + assert surface.force_convention == "body" + assert surface.cN.depends_on == ("alpha", "beta", "mach") + assert surface.cm.depends_on == ("alpha", "beta") + assert surface.cn.depends_on == ("alpha", "beta") + assert surface._needs_reynolds is False + # The same slope in both planes, with the side force sign of the body frame + at_mach_1 = (0.0, 0.0, 1.0, 0, 0, 0, 0) + assert surface.cN_alpha(*at_mach_1) == pytest.approx(2.5, rel=1e-6) + assert surface.cY_beta(*at_mach_1) == pytest.approx(-2.5, rel=1e-6) + assert surface.cm_alpha(*at_mach_1) == pytest.approx(-3.0, rel=1e-6) + assert surface.cn_beta(*at_mach_1) == pytest.approx(3.0, rel=1e-6) + + +def test_total_angle_names_and_the_guard(): + """``alpha_total`` may be used by any coefficient. One with a direction acts + in the plane of the wind and is split for you, unless it also takes the + roll angle of the wind, which leaves the split to its source.""" + drag = GenericSurface( + 1.0, 1.0, {"cA": lambda alpha_total_deg: 0.4 + 0.01 * alpha_total_deg} + ) + assert drag.cA.depends_on == ("alpha", "beta") + assert drag.cA(np.radians(3), 0.0, 0, 0, 0, 0, 0) == pytest.approx(0.43) + assert drag.cA(0.0, np.radians(-3), 0, 0, 0, 0, 0) == pytest.approx(0.43) + + by_hand = GenericSurface( + 1.0, 1.0, {"cN": lambda alpha_total, phi: 2 * alpha_total * np.sin(phi)} + ) + assert by_hand.cN(-0.1, 0.0, 0, 0, 0, 0, 0) == pytest.approx(-0.2) + + # Named after the variable alone: no option is needed + split = GenericSurface(1.0, 1.0, {"cN": lambda alpha_total: 2 * alpha_total}) + assert split.force_convention == "body" + assert split.cN(-0.1, 0.0, 0, 0, 0, 0, 0) == pytest.approx(-0.2) + assert split.cY(0.0, 0.1, 0, 0, 0, 0, 0) == pytest.approx(-0.2) + + # There is no side force or yaw moment in the plane of the wind + for name in ("cY", "cn", "cQ"): + with pytest.raises(ValueError, match="no side force or yaw moment"): + GenericSurface(1.0, 1.0, {name: lambda alpha_total: alpha_total}) + # and the part in the other plane comes from the split + with pytest.raises(ValueError, match="cY cannot be given together"): + GenericSurface( + 1.0, 1.0, {"cN": lambda alpha_total: alpha_total, "cY": lambda beta: beta} + ) + # A derivative only multiplies its own angle, so the total angle is just + # another variable it may depend on + linear = LinearGenericSurface( + 1.0, 1.0, {"cN_alpha": lambda alpha_total: 2.0 + alpha_total} + ) + assert linear.cN(0.1, 0.0, 0, 0, 0, 0, 0) == pytest.approx(0.21) + assert linear.cN(-0.1, 0.0, 0, 0, 0, 0, 0) == pytest.approx(-0.21) + assert linear.cY(0.0, 0.1, 0, 0, 0, 0, 0) == pytest.approx(0.0) + + +def test_lift_and_drag_against_the_total_angle(): + """Lift and drag given against the total angle of attack give the same + surface as the normal and axial forces they amount to.""" + + def lift(alpha_total, mach): + return (2 + 0.2 * mach) * alpha_total + + def drag(alpha_total): + return 0.4 + alpha_total**2 + + wind = GenericSurface(1.0, 1.0, {"cL": lift, "cD": drag}) + body = GenericSurface( + 1.0, + 1.0, + { + "cN": lambda alpha_total, mach: ( + lift(alpha_total, mach) * np.cos(alpha_total) + + drag(alpha_total) * np.sin(alpha_total) + ), + "cA": lambda alpha_total, mach: ( + drag(alpha_total) * np.cos(alpha_total) + - lift(alpha_total, mach) * np.sin(alpha_total) + ), + }, + ) + assert wind.force_convention == "wind" + for alpha, beta in ((0.1, 0.0), (0.0, -0.2), (0.15, 0.1), (-0.3, 0.05)): + state = (alpha, beta, 0.6, 0, 0, 0, 0) + for name in ("cN", "cY", "cA", "cL", "cD"): + assert getattr(wind, name)(*state) == pytest.approx( + getattr(body, name)(*state), abs=1e-12 + ) + at_zero = (0.0, 0.0, 0.6, 0, 0, 0, 0) + assert wind.cN_alpha(*at_zero) == pytest.approx(body.cN_alpha(*at_zero), rel=1e-6) + # In the pitch plane the lift read back is the lift given + assert wind.cL(0.2, 0.0, 0.6, 0, 0, 0, 0) == pytest.approx(lift(0.2, 0.6)) + + with pytest.raises(ValueError, match="must be zero there"): + GenericSurface(1.0, 1.0, {"cL": lambda alpha_total: 1.0}) + with pytest.raises(ValueError, match="cQ cannot be given together"): + GenericSurface(1.0, 1.0, {"cL": lift, "cQ": lambda beta: beta}) + + +def test_total_angle_table_from_one_file_and_save_and_load(tmp_path): + """A table against the total angle of attack in degrees, which only has + positive angles, loads from one file and needs no mirroring.""" + degrees = np.arange(0, 21, 2.0) + machs = np.array([0.0, 1.0, 2.0]) + degree_grid, mach_grid = np.meshgrid(degrees, machs, indexing="ij") + table = np.column_stack( + [ + degree_grid.ravel(), + mach_grid.ravel(), + ((2 + 0.5 * mach_grid) * np.radians(degree_grid)).ravel(), + (0.4 + 0.1 * mach_grid).ravel(), + ] + ) + csv_file = tmp_path / "export.csv" + np.savetxt(csv_file, table, delimiter=",", header="Alpha,Mach,CN,CA", comments="") + columns = {"Alpha": "alpha_total_deg", "Mach": "mach", "CN": "cN", "CA": "cA"} + + with warnings.catch_warnings(): + warnings.simplefilter("error") + surface = GenericSurface.from_csv(str(csv_file), 1.0, 1.0, columns=columns) + + angle = np.radians(3.0) + assert surface.cN(-angle, 0.0, 1.0, 0, 0, 0, 0) == pytest.approx(-2.5 * angle) + assert surface.cY(0.0, angle, 1.0, 0, 0, 0, 0) == pytest.approx(-2.5 * angle) + assert surface.cA(-angle, 0.0, 1.0, 0, 0, 0, 0) == pytest.approx(0.5) + + # Saved in the body frame, so it loads back as an ordinary surface + restored = _rpy_round_trip(surface) + assert restored.cN(-angle, 0.0, 1.0, 0, 0, 0, 0) == pytest.approx(-2.5 * angle) + + +def test_total_angle_coefficient_must_vanish_at_zero_angle(): + """A normal force or pitch moment against the total angle of attack has no + direction at zero angle, so it must be zero there. A table that starts + above zero degrees holds its first value down to zero and is refused, as + is a function with an intercept.""" + table = [[angle, mach, 0.05 * angle] for angle in (2, 4, 10, 20) for mach in (0, 1)] + with pytest.raises(ValueError, match="cN is 0.1 at zero total angle"): + GenericSurface( + 1.0, + 1.0, + {"cN": (table, ["alpha_total_deg", "mach"])}, + ) + with pytest.raises(ValueError, match="cm is 0.02 at zero total angle"): + GenericSurface( + 1.0, + 1.0, + { + "cN": lambda alpha_total: 2 * np.sin(alpha_total), + "cm": lambda alpha_total: 0.02 - alpha_total, + }, + ) + + +def test_total_angle_table_from_zero_gives_the_right_slope(): + """The same table with its 0 degree row gives the tabulated slope, and a + body-frame force that is continuous through zero angle.""" + table = [ + [angle, mach, 0.05 * angle] for angle in (0, 2, 4, 10, 20) for mach in (0, 1) + ] + surface = GenericSurface( + 1.0, + 1.0, + {"cN": (table, ["alpha_total_deg", "mach"])}, + ) + assert surface.cN_alpha(0, 0, 0.5, 0, 0, 0, 0) == pytest.approx( + np.degrees(0.05), rel=1e-6 + ) + small = 1e-4 + assert surface.cN(small, 0, 0.5, 0, 0, 0, 0) == pytest.approx( + -surface.cN(-small, 0, 0.5, 0, 0, 0, 0) + ) + assert abs(surface.cN(small, 0, 0.5, 0, 0, 0, 0)) < 1e-2 + + +@pytest.mark.parametrize( + "coefficients, wrong", + [ + ( + {"cN": lambda alpha_total, alpha: alpha_total + alpha}, + "cN is given against alpha_total together with alpha", + ), + ( + { + "cN": lambda alpha_total: alpha_total, + "cm": lambda alpha_total, beta: -alpha_total * beta, + }, + "cm is given against alpha_total together with beta", + ), + ], +) +def test_total_angle_refuses_signed_angles(coefficients, wrong): + """Against the total angle of attack the split supplies the sign, so a + normal force or pitch moment that also reads the signed alpha or beta + would carry it twice.""" + with pytest.raises(ValueError, match=wrong): + GenericSurface(1.0, 1.0, coefficients) + + +def test_total_angle_with_phi_is_split_by_its_source(): + """A force that depends on the roll angle of the wind is given against + ``alpha_total`` and ``phi``, with the split along the crossflow written by + its source; Mach, Reynolds and the rates stay valid.""" + surface = GenericSurface( + 1.0, + 1.0, + { + "cN": lambda alpha_total, phi, mach, reynolds, pitch_rate: ( + (2 + 0.1 * mach) + * np.sin(alpha_total) + * (1 + 0.1 * np.cos(2 * phi)) + * np.sin(phi) + ), + "cA": lambda alpha: 0.4 + alpha**2, + }, + ) + assert surface.cN(0.1, 0, 0.5, 0, 0, 0, 0) == pytest.approx( + -surface.cN(-0.1, 0, 0.5, 0, 0, 0, 0) + ) + assert surface.cN(0.1, 0, 0.5, 0, 0, 0, 0) > 0 + + +# Review of 2026-09-26: input checks, center of pressure, orientation, prints + + +def _area_length(): + return np.pi * 0.0635**2, 2 * 0.0635 + + +def test_a_one_argument_function_is_read_by_its_name(): + """The Mach hint for unnamed one-input sources must not override a name + that is a variable; a name that is not one still means Mach.""" + from rocketpy import AeroCoefficient + + assert AeroCoefficient(lambda alpha: alpha, single_var="mach").depends_on == ( + "alpha", + ) + assert AeroCoefficient(lambda reynolds: 1.0, single_var="mach").depends_on == ( + "reynolds", + ) + assert AeroCoefficient(lambda m: m, single_var="mach").depends_on == ("mach",) + assert AeroCoefficient([[0, 0.4], [1, 0.5]], single_var="mach").depends_on == ( + "mach", + ) + + +def test_csv_header_may_have_spaces_after_the_commas(tmp_path): + path = tmp_path / "cN.csv" + path.write_text( + '"alpha", "mach", "coefficient"\n0,0.3,0\n0.1,0.3,0.2\n0,0.9,0\n0.1,0.9,0.25\n' + ) + surface = GenericSurface(*_area_length(), {"cN": str(path)}) + assert surface.cN(0.1, 0, 0.3, 0, 0, 0, 0) == pytest.approx(0.2) + + +@pytest.mark.parametrize( + "kwargs, error, match", + [ + ({"coefficients": "cN.csv"}, TypeError, "dict"), + ({"coefficients": {"cN": 0}, "center_of_pressure": 0.3}, TypeError, "tuple"), + ({"coefficients": {"cN": lambda alpha, *, mach: alpha}}, ValueError, "cN"), + ({"coefficients": {"cN": lambda alpha: None}}, ValueError, "cN"), + ( + {"coefficients": {"cN": lambda mach, alpha, x, y, z, w, v: mach}}, + ValueError, + "cN", + ), + ( + {"coefficients": {"cN": lambda alpha, mach=0.0: alpha * mach}}, + ValueError, + "mach", + ), + ( + { + "coefficients": {"cN": ([[0, 0], [1, 0.4]], ["mach"])}, + "interpolation": "splin", + }, + ValueError, + "splin", + ), + ({"coefficients": {"cN": (0.4, ["mach"])}}, ValueError, "constant"), + ( + {"coefficients": {"cN": ([[0, 0.4], [0, 0.5], [1, 0.6]], ["mach"])}}, + ValueError, + "same input", + ), + ( + { + "coefficients": { + "cN": ({"alpha": [0, 0.1, 0.2], "mach": [0, 1]}, np.zeros((2, 3))) + } + }, + ValueError, + "shape", + ), + ], +) +def test_bad_inputs_are_rejected_at_construction(kwargs, error, match): + """Inputs that used to be misread or to fail only in flight are refused + when the surface is built, with the coefficient named.""" + with pytest.raises(error, match=match): + GenericSurface(*_area_length(), **kwargs) + + +def test_duplicate_csv_variable_is_rejected(tmp_path): + path = tmp_path / "dup.csv" + path.write_text("alpha,alpha,cN\n0,0,0\n0.1,0.1,0.2\n0.2,0.2,0.4\n") + with pytest.raises(ValueError, match="more than once"): + GenericSurface(*_area_length(), {"cN": str(path)}) + + +def test_a_function_labelled_in_degrees_is_read_in_degrees(): + table = Function([[0, 0], [10, 0.5], [20, 1.0]], inputs="Alpha (deg)", outputs="cN") + surface = GenericSurface(*_area_length(), {"cN": table}) + assert surface.cN(np.radians(10), 0, 0.3, 0, 0, 0, 0) == pytest.approx(0.5) + + +def test_wind_and_total_angle_tables_round_trip_without_pickling(): + area, length = _area_length() + wind = GenericSurface( + area, + length, + { + "cL": ( + [[0, 0, 0], [0.1, 0, 0.2], [0, 1, 0], [0.1, 1, 0.25]], + ["alpha", "mach"], + ), + "cD": 0.4, + }, + ) + total = GenericSurface( + area, + length, + { + "cN": ( + [[0, 0, 0], [10, 0, 0.5], [0, 1, 0], [10, 1, 0.6]], + ["alpha_total_deg", "mach"], + ) + }, + ) + for surface in (wind, total): + data = json.dumps(surface, cls=RocketPyEncoder, allow_pickle=False) + loaded = json.loads(data, cls=RocketPyDecoder) + args = (0.1, 0.05, 0.5, 0, 0, 0, 0) + assert loaded.cN(*args) == pytest.approx(surface.cN(*args)) + assert loaded.cY(*args) == pytest.approx(surface.cY(*args)) + + +def test_setting_the_center_of_pressure_updates_the_rocket(): + from rocketpy import Rocket + + surface = GenericSurface(*_area_length(), {"cN": lambda alpha: 2 * alpha}) + rocket = Rocket( + radius=0.0635, + mass=14.426, + inertia=(6.321, 6.321, 0.034), + power_off_drag=0.5, + power_on_drag=0.5, + center_of_mass_without_motor=0, + ) + rocket.add_surfaces(surface, 0.0) + before = rocket.aerodynamic_center(0.3) + surface.center_of_pressure = (0, 0, 0.3) + assert rocket.aerodynamic_center(0.3) == pytest.approx(before + 0.3) + + +def test_center_of_pressure_given_against_mach(): + """A center of pressure that moves with Mach is folded into the moment, + so it equals a hand-written ``cm``, and ``aerodynamic_center`` reports + it.""" + area, length = _area_length() + xcp = [[0, -0.3], [1, -0.2], [2, -0.1]] + with_xcp = GenericSurface( + area, + length, + {"cN": lambda alpha, mach: (2 + mach) * alpha}, + center_of_pressure=(0, 0, xcp), + ) + by_hand = GenericSurface( + area, + length, + { + "cN": lambda alpha, mach: (2 + mach) * alpha, + "cm": lambda alpha, mach: ( + (2 + mach) + * alpha + * np.interp(mach, [0, 1, 2], [-0.3, -0.2, -0.1]) + / length + ), + }, + ) + state = ( + Vector([0, -10, -100]), + 100.5, + 0.5, + 1.2, + Vector([0, 0, 0]), + (0, 0, 0), + None, + None, + 0, + ) + assert with_xcp.compute_forces_and_moments(*state) == pytest.approx( + by_hand.compute_forces_and_moments(*state) + ) + assert with_xcp.aerodynamic_center(0.5) == pytest.approx(-0.25) + + +def test_linear_surface_hints_and_defaults(): + """A derivative tabulated in one column is against Mach; a value name + instead of a derivative name is pointed to the right class.""" + area, length = _area_length() + surface = LinearGenericSurface( + area, length, {"cN_alpha": [[0, 2], [1, 2.5], [2, 3]]} + ) + assert surface.cN_alpha(0, 0, 1, 0, 0, 0, 0) == pytest.approx(2.5) + with pytest.raises(ValueError, match="cN_alpha"): + LinearGenericSurface(area, length, {"cN": 2.0}) + + +def test_surface_prints_show_the_body_coefficients(capsys): + surface = GenericSurface( + *_area_length(), {"cA": 0.4, "cl": lambda roll_rate: -0.1 * roll_rate} + ) + surface.prints.all() + out = capsys.readouterr().out + assert "cA = 0.4000" in out and "cl = " in out and "cN = 0 (zero)" in out + + +def _converted_surfaces(): + area, length = _area_length() + return { + "wind": GenericSurface( + area, + length, + {"cL": lambda alpha, mach: 2 * alpha, "cD": 0.4, "cQ": lambda beta: -beta}, + force_convention="wind", + ), + "alpha_total": GenericSurface( + area, + length, + {"cN": lambda alpha_total: 2 * np.sin(alpha_total)}, + ), + } + + +@pytest.mark.parametrize("kind", ["wind", "alpha_total"]) +def test_converted_coefficients_reuse_nothing_stale(kind): + """The coefficients of one state are computed together and kept for the + next coefficient; switching between states must give each its own values.""" + surface = _converted_surfaces()[kind] + first = (0.1, 0.05, 0.3, 0, 0.02, -0.01, 0) + second = (-0.2, 0.1, 0.6, 0, -0.01, 0.03, 0) + expected = { + state: [getattr(surface, name)(*state) for name in ("cN", "cY", "cm", "cn")] + for state in (first, second) + } + for state in (first, second, first, second): + for name, value in zip(("cN", "cY", "cm", "cn"), expected[state]): + assert getattr(surface, name)(*state) == value + + +@pytest.mark.parametrize("kind", ["wind", "alpha_total"]) +def test_converted_coefficients_accept_arrays(kind): + surface = _converted_surfaces()[kind] + alphas, betas = np.array([0.1, -0.2, 0.3]), np.array([0.0, 0.05, -0.1]) + for name in ("cN", "cY"): + coefficient = getattr(surface, name) + values = coefficient(alphas, betas, 0.3, 0, 0, 0, 0) + assert values == pytest.approx( + [coefficient(a, b, 0.3, 0, 0, 0, 0) for a, b in zip(alphas, betas)] + ) + + +def test_roll_angle_of_the_wind_is_zero_flying_exactly_tail_first(): + from rocketpy.rocket.aero_surface._helpers import total_angle_and_roll + + assert total_angle_and_roll(np.pi, np.pi) == pytest.approx((np.pi, 0.0)) diff --git a/tests/unit/rocket/aero_surface/test_individual_fins.py b/tests/unit/rocket/aero_surface/test_individual_fins.py index 6db540a8a..fa1d0ef0c 100644 --- a/tests/unit/rocket/aero_surface/test_individual_fins.py +++ b/tests/unit/rocket/aero_surface/test_individual_fins.py @@ -85,15 +85,18 @@ def test_trapezoidal_fin_setters_update_geometry(calisto_trapezoidal_fin): # Arrange fin = calisto_trapezoidal_fin - # Act + # Act and assert fin.tip_chord = 0.05 - fin.sweep_angle = 12.0 - fin.sweep_length = 0.03 - - # Assert np.testing.assert_allclose(fin.tip_chord, 0.05) + + fin.sweep_angle = 12.0 np.testing.assert_allclose(fin.sweep_angle, 12.0) + np.testing.assert_allclose(fin.sweep_length, np.tan(np.radians(12.0)) * fin.span) + + # A length replaces the angle, which no longer describes the fin + fin.sweep_length = 0.03 np.testing.assert_allclose(fin.sweep_length, 0.03) + assert fin.sweep_angle is None def test_individual_fin_rocket_diameter_aliases_are_kept_in_sync( @@ -532,3 +535,139 @@ def test_add_individual_fin_accepts_full_3d_position(position_input): # Assert assert stored_position == Vector([0.02, -0.01, -1.2]) + + +@pytest.mark.parametrize("cant_angle", [0.0, 2.0, 10.0]) +@pytest.mark.parametrize("angular_position", [0.0, 30.0, 90.0, 200.0]) +def test_fin_stability_slopes_are_the_slopes_of_what_flies( + cant_angle, angular_position +): + """The slopes a fin gives the rocket for its aerodynamic center and static + margin must be the slopes of the force it produces in flight, which a cant + angle reduces by its cosine.""" + fin = TrapezoidalFin( + angular_position=angular_position, + span=0.1, + root_chord=0.12, + tip_chord=0.04, + rocket_radius=0.0635, + cant_angle=cant_angle, + ) + mach = 0.3 + at_zero = (0.0, 0.0, mach, 0.0, 0.0, 0.0, 0.0) + + assert fin.cN_alpha(*at_zero) == pytest.approx( + fin.cN.slope("alpha", "mach")(mach), rel=1e-6, abs=1e-9 + ) + assert fin.cY_beta(*at_zero) == pytest.approx( + fin.cY.slope("beta", "mach")(mach), rel=1e-6, abs=1e-9 + ) + + +def test_fin_slopes_follow_a_change_of_cant_angle(): + fin = TrapezoidalFin( + angular_position=90, + span=0.1, + root_chord=0.12, + tip_chord=0.04, + rocket_radius=0.0635, + ) + at_zero = (0.0, 0.0, 0.3, 0.0, 0.0, 0.0, 0.0) + straight = fin.cN_alpha(*at_zero) + fin.cant_angle = 10 + assert fin.cN_alpha(*at_zero) == pytest.approx(straight * np.cos(np.radians(10))) + + +@pytest.mark.parametrize( + "fixture_name", + [ + "calisto_trapezoidal_fins", + "calisto_trapezoidal_fin", + "calisto_free_form_fins", + "calisto_free_form_fin", + "calisto_elliptical_fin", + ], +) +def test_changing_the_geometry_of_a_built_fin_updates_it_once(request, fixture_name): + """A fin is built once by its constructor and updated by its setters: a + wider span must give a larger normal-force slope, and count as one change + for the rockets that hold the fin.""" + fin = request.getfixturevalue(fixture_name) + # The fin's own lift slope: an individual fin at 0 degrees around the body + # has no share of it in the pitch plane, so cN_alpha would not show it + slope_before = fin.clalpha(0.3) + version_before = fin._version + + fin.span = 1.5 * fin.span + + assert fin.clalpha(0.3) > slope_before + assert fin._version == version_before + 1 + + +@pytest.mark.parametrize( + "fixture_name", + ["calisto_trapezoidal_fin", "calisto_free_form_fin", "calisto_elliptical_fin"], +) +def test_individual_fin_keeps_its_rotation_when_built_and_updated( + request, fixture_name +): + """The frame of an individual fin is turned by its angular position. That + rotation must be the one the surface uses, both right after the fin is + built and after its angular position changes.""" + fin = request.getfixturevalue(fixture_name) + assert fin._rotation_surface_to_body == fin._rotation_fin_to_body + + fin.angular_position = fin.angular_position + 90 + + assert fin._rotation_surface_to_body == fin._rotation_fin_to_body + assert fin._rotation_fin_to_body == fin._rotation_body_to_fin.transpose + + +@pytest.mark.parametrize( + "fixture_name", + [ + "calisto_trapezoidal_fins", + "calisto_trapezoidal_fin", + "calisto_free_form_fins", + "calisto_free_form_fin", + "calisto_elliptical_fin", + ], +) +def test_a_fin_canted_later_flies_like_one_built_canted(request, fixture_name): + """A controller may change the cant angle at every step, so the change is + made cheap: nothing is rebuilt, the coefficients read the angle when they + are evaluated. The forces, moments and coefficients must then be exactly + those of a fin built with that cant angle from the start.""" + fin = request.getfixturevalue(fixture_name) + built_canted = type(fin).from_dict( + {**fin.to_dict(), "cant_angle": fin.to_dict()["cant_angle"] + 3.0} + ) + version_before = fin._version + + fin.cant_angle += 3.0 + + assert fin._version == version_before + 1 + stream = Vector([-4.0, 6.0, -70.0]) + call = (stream, abs(stream), 0.2, 1.1, Vector([0.0, 0.0, -1.1]), (0.3, -0.2, 5.0)) + assert fin.compute_forces_and_moments(*call) == pytest.approx( + built_canted.compute_forces_and_moments(*call), rel=1e-12, abs=1e-15 + ) + state = (0.05, -0.02, 0.6, 0.0, 0.0, 0.0, 0.1) + for name in ("cN", "cY", "cA", "cl", "cN_alpha", "cY_beta", "cl_0", "cl_p"): + assert getattr(fin, name)(*state) == pytest.approx( + getattr(built_canted, name)(*state), rel=1e-12, abs=1e-15 + ) + assert fin.roll_parameters[2] == pytest.approx(built_canted.roll_parameters[2]) + if hasattr(fin, "_rotation_fin_to_body"): + assert fin._rotation_surface_to_body == built_canted._rotation_surface_to_body + + +def test_changing_the_cant_angle_rebuilds_nothing(calisto_trapezoidal_fin): + """The cant angle is a control input: setting it must not run the + geometry chain, which is far too slow for a controller step.""" + fin = calisto_trapezoidal_fin + with patch.object( + type(fin), "_update_geometry_chain", side_effect=AssertionError("rebuilt") + ): + fin.cant_angle = 2.0 + assert fin.cant_angle == 2.0 diff --git a/tests/unit/rocket/aero_surface/test_linear_generic_surfaces.py b/tests/unit/rocket/aero_surface/test_linear_generic_surfaces.py index 7bb884220..dbd281db3 100644 --- a/tests/unit/rocket/aero_surface/test_linear_generic_surfaces.py +++ b/tests/unit/rocket/aero_surface/test_linear_generic_surfaces.py @@ -1,3 +1,5 @@ +import math + import pytest from rocketpy import Function, GenericSurface, LinearGenericSurface @@ -17,7 +19,6 @@ {"invalid_name": 0}, {"cN_0": "inexistent_file.csv"}, {"cN_0": Function(lambda x1, x2, x3, x4, x5, x6: 0)}, - {"cN_0": lambda x1: 0}, {"cN_0": {}}, ], ) @@ -33,6 +34,15 @@ def test_invalid_initialization(coefficients): ) +def test_a_one_input_derivative_is_against_mach(): + """A derivative name already fixes the angle or rate, so a one-argument + function or one-column table is read against Mach.""" + surface = LinearGenericSurface( + REFERENCE_AREA, REFERENCE_LENGTH, {"cN_alpha": lambda x1: 2 + x1} + ) + assert surface.cN_alpha(0, 0, 0.5, 0, 0, 0, 0) == pytest.approx(2.5) + + def test_invalid_initialization_from_csv(filename_invalid_coeff_linear_generic_surface): """Checks if linear generic surfaces raises errors when initialized incorrectly from a csv file""" @@ -229,3 +239,78 @@ def test_wind_linear_matches_generic_surface_to_first_order(): assert getattr(linear, coeff).get_value_opt(*args) == pytest.approx( getattr(generic, coeff).get_value_opt(*args), abs=1e-5 ) + + +def test_public_coefficient_includes_the_damping_terms(): + """``surface.cm`` is the whole coefficient, so it follows the rotation rates + as well as the angles; ``cmf`` and ``cmd`` are its two parts.""" + surface = LinearGenericSurface( + reference_area=1.3, + reference_length=0.7, + coefficients={"cm_alpha": -3.0, "cm_q": -50.0, "cm_p": lambda mach: mach}, + ) + alpha, mach, pitch_rate, roll_rate = 0.1, 0.5, 0.02, 0.03 + args = (alpha, 0.0, mach, 0.0, pitch_rate, 0.0, roll_rate) + + forcing = -3.0 * alpha + damping = -50.0 * pitch_rate + mach * roll_rate + assert surface.cmf(*args) == pytest.approx(forcing) + assert surface.cmd(*args) == pytest.approx(damping) + assert surface.cm(*args) == pytest.approx(forcing + damping) + # A coefficient with no derivatives given is zero + assert surface.cn(*args) == 0.0 + + +def test_public_coefficients_give_the_simulated_forces_and_moments(): + """The forces and moments used in the simulation are the public coefficients + times the dynamic pressure, the reference area and the reference length.""" + names = [ + f"{coefficient}_{suffix}" + for coefficient in ("cN", "cY", "cA", "cm", "cn", "cl") + for suffix in ("0", "alpha", "beta", "p", "q", "r") + ] + surface = LinearGenericSurface( + reference_area=1.3, + reference_length=0.7, + coefficients={name: 0.05 * i - 0.4 for i, name in enumerate(names)}, + ) + rho, speed, mach = 1.1, 80.0, 0.4 + alpha, beta, rates = 0.05, -0.02, (0.01, -0.02, 0.03) + args = (alpha, beta, mach, 0, *rates) + force_scale = 0.5 * rho * speed**2 * 1.3 + moment_scale = force_scale * 0.7 + # The flow and the body rates that give these angles and reduced rates + direction = Vector([math.tan(beta), math.tan(alpha), 1.0]) + stream_velocity = direction * (-speed / abs(direction)) + omega = [rate * 2 * speed / 0.7 for rate in rates] + + r1, r2, r3, pitch, yaw, roll = surface.compute_forces_and_moments( + stream_velocity, + speed, + mach, + rho, + Vector([0, 0, 0]), + omega, + Function(1.2), + Function(1.8e-5), + 0.0, + ) + + assert r1 == pytest.approx(force_scale * surface.cY(*args)) + assert r2 == pytest.approx(-force_scale * surface.cN(*args)) + assert r3 == pytest.approx(-force_scale * surface.cA(*args)) + assert pitch == pytest.approx(moment_scale * surface.cm(*args)) + assert yaw == pytest.approx(moment_scale * surface.cn(*args)) + assert roll == pytest.approx(moment_scale * surface.cl(*args)) + + +def test_linear_surface_from_csv(tmp_path): + """The coefficient derivatives of a linear surface load from one file.""" + csv_file = tmp_path / "derivatives.csv" + csv_file.write_text( + "mach,cN_alpha,cm_alpha,cm_q\n0,2,-3,-50\n1,3,-4,-50\n2,4,-5,-50\n" + ) + surface = LinearGenericSurface.from_csv(str(csv_file), 1.0, 1.0) + assert surface.cN_alpha.depends_on == ("mach",) + assert surface.cN(0.1, 0.0, 1.0, 0, 0, 0, 0) == pytest.approx(0.3) + assert surface.cm(0.1, 0.0, 1.0, 0, 0.01, 0, 0) == pytest.approx(-0.4 - 0.5) diff --git a/tests/unit/rocket/aero_surface/test_surface_coefficient_completeness.py b/tests/unit/rocket/aero_surface/test_surface_coefficient_completeness.py index a0c413080..04760fbc9 100644 --- a/tests/unit/rocket/aero_surface/test_surface_coefficient_completeness.py +++ b/tests/unit/rocket/aero_surface/test_surface_coefficient_completeness.py @@ -142,7 +142,7 @@ def test_center_of_pressure_accessors(name): """Every surface exposes the pitch and yaw center-of-pressure accessors used by the rocket's aerodynamic-center computation.""" surface = SURFACES[name] - for attr in ("center_of_pressure_z", "center_of_pressure_z_yaw"): + for attr in ("aerodynamic_center", "aerodynamic_center_yaw"): accessor = getattr(surface, attr, None) assert accessor is not None, f"{name} is missing {attr}" assert np.isfinite(accessor.get_value_opt(0.5)) diff --git a/tests/unit/rocket/aero_surface/test_surface_encoding.py b/tests/unit/rocket/aero_surface/test_surface_encoding.py new file mode 100644 index 000000000..b7949b998 --- /dev/null +++ b/tests/unit/rocket/aero_surface/test_surface_encoding.py @@ -0,0 +1,144 @@ +"""Saving and loading aerodynamic surfaces through the RocketPy encoder.""" + +import json +import math + +import numpy as np +import pytest + +from rocketpy import ( + EllipticalFins, + GenericSurface, + NoseCone, + TrapezoidalFin, + TrapezoidalFins, +) +from rocketpy._encoders import RocketPyDecoder, RocketPyEncoder + +RADIUS = 0.0635 +AIRFOIL = ("data/airfoils/NACA0012-radians.txt", "radians") +STATE = (0.05, 0.02, 0.7, 1e6, 0.0, 0.0, 0.01) + + +def _round_trip(obj, **options): + text = json.dumps(obj, cls=RocketPyEncoder, **options) + return json.loads(text, cls=RocketPyDecoder) + + +@pytest.mark.parametrize("options", [{}, {"include_outputs": True, "discretize": True}]) +def test_fin_set_with_airfoil_table_keeps_its_lift(options): + """A tabulated airfoil is saved as it is: resampling it on save changed the + lift slope of the loaded fins (by 29% for this table).""" + fins = TrapezoidalFins( + n=3, + root_chord=0.12, + tip_chord=0.05, + span=0.1, + rocket_radius=RADIUS, + cant_angle=2, + airfoil=AIRFOIL, + ) + loaded = _round_trip(fins, **options) + assert loaded.clalpha_single_fin(0.3) == pytest.approx( + fins.clalpha_single_fin(0.3), rel=1e-10 + ) + for name in ("cN", "cl"): + assert getattr(loaded, name)(*STATE) == pytest.approx( + getattr(fins, name)(*STATE), rel=1e-10 + ) + + +def test_single_fin_saves_its_airfoil_data_not_the_file_path(): + fin = TrapezoidalFin( + angular_position=30, + root_chord=0.12, + tip_chord=0.05, + span=0.1, + rocket_radius=RADIUS, + airfoil=AIRFOIL, + ) + saved = json.loads(json.dumps(fin, cls=RocketPyEncoder)) + assert isinstance(saved["airfoil"][0], dict) + loaded = _round_trip(fin) + assert loaded.cY(*STATE) == pytest.approx(fin.cY(*STATE), rel=1e-10) + + +def test_saving_with_outputs_does_not_change_the_surface(): + fins = EllipticalFins(n=4, root_chord=0.12, span=0.1, rocket_radius=RADIUS) + before = fins.clalpha(0.7) + fins.to_dict(include_outputs=True, discretize=True) + assert fins.clalpha(0.7) == before + + +def test_bluff_nose_cone_keeps_its_length(): + """The saved length is the one given, so the bluff tip does not shorten the + nose cone again on every load or change of shape.""" + nose = NoseCone( + length=0.5, + kind="ogive", + bluffness=0.3, + base_radius=RADIUS, + rocket_radius=RADIUS, + ) + loaded = _round_trip(nose) + assert loaded.length == pytest.approx(nose.length) + assert loaded.cpz == pytest.approx(nose.cpz) + nose.bluffness = 0.3 + assert nose.length == pytest.approx(loaded.length) + + +def test_sweep_angle_is_restored(): + fins = TrapezoidalFins( + n=4, + root_chord=0.12, + tip_chord=0.05, + span=0.1, + rocket_radius=RADIUS, + sweep_angle=20, + ) + loaded = _round_trip(fins) + assert loaded.sweep_angle == 20 + assert loaded.sweep_length == pytest.approx(fins.sweep_length) + + +def _swept_fin(fin_class, **sweep): + placement = {"n": 4} if fin_class is TrapezoidalFins else {"angular_position": 0} + return fin_class( + root_chord=0.12, + tip_chord=0.06, + span=0.08, + rocket_radius=RADIUS, + **placement, + **sweep, + ) + + +@pytest.mark.parametrize("fin_class", [TrapezoidalFins, TrapezoidalFin]) +def test_a_sweep_length_set_after_an_angle_replaces_it(fin_class): + """Setting the length drops the angle the fin was built with, so the fin + does not reload with the old sweep.""" + fin = _swept_fin(fin_class, sweep_angle=30) + fin.sweep_length = 0.01 + assert fin.sweep_angle is None + loaded = _round_trip(fin) + assert loaded.sweep_length == pytest.approx(0.01) + assert loaded.cN(*STATE) == pytest.approx(fin.cN(*STATE)) + + +@pytest.mark.parametrize("fin_class", [TrapezoidalFins, TrapezoidalFin]) +def test_a_sweep_given_as_an_angle_follows_the_span(fin_class): + fin = _swept_fin(fin_class, sweep_angle=30) + fin.span = 0.12 + assert fin.sweep_length == pytest.approx(np.tan(np.radians(30)) * 0.12) + loaded = _round_trip(fin) + assert loaded.sweep_length == pytest.approx(fin.sweep_length) + assert loaded.cpz == pytest.approx(fin.cpz) + + +def test_function_coefficient_saved_without_pickle_gives_a_clear_error(): + surface = GenericSurface( + math.pi * RADIUS**2, 2 * RADIUS, {"cN": lambda alpha: 2 * alpha} + ) + text = json.dumps(surface, cls=RocketPyEncoder, allow_pickle=False) + with pytest.raises(ValueError, match="allow_pickle=True"): + json.loads(text, cls=RocketPyDecoder) diff --git a/tests/unit/rocket/test_rocket.py b/tests/unit/rocket/test_rocket.py index 02fbed668..9b088fd54 100644 --- a/tests/unit/rocket/test_rocket.py +++ b/tests/unit/rocket/test_rocket.py @@ -1,3 +1,4 @@ +import json import warnings from itertools import product from unittest.mock import patch @@ -5,8 +6,18 @@ import numpy as np import pytest -from rocketpy import Function, NoseCone, Rocket, SolidMotor +from rocketpy import ( + Function, + GenericSurface, + LinearGenericSurface, + NoseCone, + Rocket, + SolidMotor, + TrapezoidalFin, +) +from rocketpy._encoders import RocketPyDecoder, RocketPyEncoder from rocketpy.mathutils.vector_matrix import Vector +from rocketpy.rocket._helpers import summed_force_and_moment from rocketpy.motors.empty_motor import EmptyMotor from rocketpy.motors.motor import Motor @@ -37,6 +48,225 @@ def test_evaluate_static_margin_assert_cp_equals_cm(dimensionless_calisto): assert pytest.approx(rocket.aerodynamic_center(0), 1e-8) == pytest.approx(0, 1e-8) +def test_static_margin_lazy_until_accessed(calisto_motorless): + """Static margin must not be discretized until first access.""" + rocket = calisto_motorless + + with patch.object( + rocket._static_margin, + "set_discrete", + wraps=rocket._static_margin.set_discrete, + ) as mock_set_discrete: + rocket.add_nose(length=0.55829, kind="ogive", position=1.160) + mock_set_discrete.assert_not_called() + + static_margin = rocket.static_margin + assert mock_set_discrete.call_count == 1 + assert isinstance(static_margin, Function) + + # Second access must reuse the cached Function. + _ = rocket.static_margin(0) + assert mock_set_discrete.call_count == 1 + + +def test_static_margin_rebuilds_after_adding_surface(calisto): + """Adding an aero surface invalidates SM; access rebuilds it once.""" + rocket = calisto + margin_before = rocket.static_margin(0) + + with patch.object( + rocket._static_margin, + "set_discrete", + wraps=rocket._static_margin.set_discrete, + ) as mock_set_discrete: + rocket.add_nose(length=0.55829, kind="ogive", position=1.160) + mock_set_discrete.assert_not_called() + + margin_after = rocket.static_margin(0) + assert mock_set_discrete.call_count == 1 + + _ = rocket.static_margin(0) + assert mock_set_discrete.call_count == 1 + + assert margin_after != pytest.approx(margin_before, abs=1e-6) + + +def test_aerodynamic_center_lazy_until_accessed(calisto): + """The center of pressure is only rebuilt when read, and only once.""" + rocket = calisto + _ = rocket.aerodynamic_center(0) + + with patch.object( + rocket, + "evaluate_center_of_pressure", + wraps=rocket.evaluate_center_of_pressure, + ) as mock_evaluate: + rocket.add_nose(length=0.55829, kind="ogive", position=1.160) + mock_evaluate.assert_not_called() + + _ = rocket.aerodynamic_center(0) + _ = rocket.aerodynamic_center(0) + assert mock_evaluate.call_count == 1 + + +def test_add_motor_rebuilds_only_the_margins(calisto_motorless, cesaroni_m1670): + """A motor moves the center of mass, not the center of pressure.""" + rocket = calisto_motorless + rocket.add_nose(length=0.55829, kind="ogive", position=1.160) + margin_before = rocket.static_margin(0) + + with patch.object( + rocket, + "evaluate_center_of_pressure", + wraps=rocket.evaluate_center_of_pressure, + ) as mock_evaluate: + rocket.add_motor(cesaroni_m1670, position=-1.373) + margin_after = rocket.static_margin(0) + mock_evaluate.assert_not_called() + + assert margin_after != pytest.approx(margin_before, abs=1e-6) + + +def test_a_kept_static_margin_follows_a_new_surface(calisto): + """The margin is rebuilt in place, so a kept reference stays current.""" + static_margin = calisto.static_margin + static_margin_yaw = calisto.static_margin_yaw + margin_before = static_margin(0) + + calisto.add_nose(length=0.55829, kind="ogive", position=1.160) + + assert calisto.static_margin is static_margin + assert calisto.static_margin_yaw is static_margin_yaw + assert static_margin(0) != pytest.approx(margin_before, abs=1e-6) + + +def test_a_failed_margin_rebuild_is_tried_again(calisto): + """A rebuild that raises leaves the margins outdated, not half-built.""" + margin_before = calisto.static_margin(0) + calisto.add_nose(length=0.55829, kind="ogive", position=1.160) + with patch.object( + calisto, "evaluate_static_margin", side_effect=RuntimeError("failed") + ): + with pytest.raises(RuntimeError): + _ = calisto.static_margin + # The next read builds them again, this time with the new surface + assert calisto.static_margin(0) != pytest.approx(margin_before, abs=1e-6) + + +def test_evaluate_center_of_pressure_updates_the_margins( + calisto_robust, calisto_trapezoidal_fins +): + """Re-evaluating the center of pressure brings the margins up to date.""" + margin_before = calisto_robust.static_margin(0) + calisto_trapezoidal_fins.tip_chord = 0.080 # changes the fin set in place + calisto_robust.evaluate_center_of_pressure() + assert calisto_robust.static_margin(0) != pytest.approx(margin_before, abs=1e-6) + + +def test_rocket_follows_a_surface_changed_in_place( + calisto_robust, calisto_trapezoidal_fins +): + """No evaluate call is needed after changing a surface already added.""" + rocket, fins = calisto_robust, calisto_trapezoidal_fins + center_before = rocket.aerodynamic_center(0) + margin_before = rocket.static_margin(0) + cp_to_cdm_before = [*rocket.surfaces_cp_to_cdm[fins]] + + fins.tip_chord = 0.080 + + assert rocket.aerodynamic_center(0) != pytest.approx(center_before) + assert rocket.static_margin(0) != pytest.approx(margin_before) + assert [*rocket.surfaces_cp_to_cdm[fins]] != pytest.approx(cp_to_cdm_before) + + +def test_a_shared_surface_updates_every_rocket( + calisto, calisto_nose_to_tail, calisto_nose_cone +): + """A surface added to two rockets updates both when it changes.""" + calisto.add_surfaces(calisto_nose_cone, 1.160) + calisto_nose_to_tail.add_surfaces(calisto_nose_cone, -1.160) + centers_before = [ + calisto.aerodynamic_center(0), + calisto_nose_to_tail.aerodynamic_center(0), + ] + + calisto_nose_cone.length = 0.8 + + centers_after = [ + calisto.aerodynamic_center(0), + calisto_nose_to_tail.aerodynamic_center(0), + ] + assert centers_after[0] != pytest.approx(centers_before[0]) + assert centers_after[1] != pytest.approx(centers_before[1]) + + +def test_cm_eccentricity_moves_the_surfaces_already_added(calisto_robust): + """Setting the center of mass eccentricity after the surfaces were added must + give the same surface lever arms as setting it before.""" + rocket = calisto_robust + before = { + s: [*rocket.surfaces_cp_to_cdm[s]] for s, _ in rocket.aerodynamic_surfaces + } + + rocket.add_cm_eccentricity(0.01, -0.02) + + for surface, _ in rocket.aerodynamic_surfaces: + x, y, z = rocket.surfaces_cp_to_cdm[surface] + assert x == pytest.approx(before[surface][0] - 0.01 * rocket._csys) + assert y == pytest.approx(before[surface][1] + 0.02) + assert z == pytest.approx(before[surface][2]) + + +def test_asymmetry_warning_is_shown_once_per_configuration(calisto): + """A rocket that is not axisymmetric warns when its aerodynamic center is + built, and not again until its surfaces change.""" + surface = GenericSurface( + calisto.area, 2 * calisto.radius, {"cN": lambda alpha: 2 * alpha} + ) + calisto.add_surfaces(surface, -1.0) + with pytest.warns(UserWarning, match="not axisymmetric"): + _ = calisto.aerodynamic_center(0) + with warnings.catch_warnings(): + warnings.simplefilter("error") + _ = calisto.aerodynamic_center(0) + _ = calisto.static_margin(0) + + +def test_a_canted_fin_moves_its_leading_edge(calisto): + """Canting a fin already on the rocket places it as if it were added canted: + the position the user gave is kept, the lever arm follows the cant.""" + import copy + + geometry = {"root_chord": 0.12, "tip_chord": 0.04, "span": 0.1} + fin = TrapezoidalFin(0, rocket_radius=0.0635, **geometry) + canted = TrapezoidalFin(0, rocket_radius=0.0635, cant_angle=5, **geometry) + reference = copy.deepcopy(calisto) + reference.add_surfaces(canted, -1.0) + calisto.add_surfaces(fin, -1.0) + cp_to_cdm_before = [*calisto.surfaces_cp_to_cdm[fin]] + + fin.cant_angle = 5 + _ = calisto.aerodynamic_center(0) + + position = next(p for s, p in calisto.aerodynamic_surfaces if s is fin) + assert [*position] == pytest.approx([0.0, 0.0, -1.0]) + assert [*calisto.surfaces_cp_to_cdm[fin]] != pytest.approx(cp_to_cdm_before) + assert [*calisto.surfaces_cp_to_cdm[fin]] == pytest.approx( + [*reference.surfaces_cp_to_cdm[canted]] + ) + + +def test_a_canted_fin_keeps_its_position_through_save_and_load(calisto): + fin = TrapezoidalFin( + 0, root_chord=0.12, tip_chord=0.04, span=0.1, rocket_radius=0.0635, cant_angle=5 + ) + calisto.add_surfaces(fin, -1.0) + loaded = json.loads(json.dumps(calisto, cls=RocketPyEncoder), cls=RocketPyDecoder) + assert [*loaded.aerodynamic_surfaces[-1].position] == pytest.approx( + [*calisto.aerodynamic_surfaces[-1].position] + ) + + @pytest.mark.parametrize( "k, type_", ([2 / 3, "conical"], [0.46469957130675876, "ogive"], [0.563, "lvhaack"]), @@ -465,7 +695,7 @@ def test_evaluate_nozzle_to_cdm(calisto): def test_evaluate_nozzle_gyration_tensor(calisto): expected_gyration_tensor = np.array( - [[0.3940207, 0, 0], [0, 0.3940207, 0], [0, 0, 0.0005445]] + [[1.5752660, 0, 0], [0, 1.5752660, 0], [0, 0, 0.0005445]] ) atol = 1e-3 * 1e-2 * 1e-2 # Equivalent to 1g * 1cm^2 assert np.allclose( @@ -746,8 +976,8 @@ def test_drag_csv_header_order_independent_for_multivariable_input(tmp_path): assert drag_swapped == pytest.approx(drag_ordered) assert set(rocket_ordered.power_off_drag_7d.depends_on) == {"mach", "reynolds"} assert set(rocket_swapped.power_off_drag_7d.depends_on) == {"mach", "reynolds"} - assert ordered_csv_function.get_interpolation_method() == "regular_grid" - assert swapped_csv_function.get_interpolation_method() == "regular_grid" + assert ordered_csv_function.is_regular_grid + assert swapped_csv_function.is_regular_grid def test_drag_input_types_supported_for_power_on_and_power_off(tmp_path): @@ -833,3 +1063,154 @@ def test_drag_input_types_supported_for_power_on_and_power_off(tmp_path): assert rocket.power_off_drag_7d(*query_point) == pytest.approx(expected) assert rocket.power_on_drag_7d(*query_point) == pytest.approx(expected) + + +# Review of 2026-09-26: drag inputs, full-body helpers, positions, length + + +def _bare_rocket(**kwargs): + kwargs.setdefault("power_off_drag", 0.5) + kwargs.setdefault("power_on_drag", 0.5) + return Rocket( + radius=0.0635, + mass=14.426, + inertia=(6.321, 6.321, 0.034), + center_of_mass_without_motor=0, + **kwargs, + ) + + +def test_a_one_argument_drag_function_is_read_by_its_name(): + rocket = _bare_rocket(power_off_drag=lambda alpha: 0.4 + alpha**2) + assert rocket.power_off_drag_7d.depends_on == ("alpha",) + assert rocket.power_off_drag_7d(0.5, 0, 0.3, 0, 0, 0, 0) == pytest.approx(0.65) + assert _bare_rocket( + power_off_drag=lambda m: 0.4 + m + ).power_off_drag_7d.depends_on == ("mach",) + + +def test_drag_can_be_assigned_after_construction(): + rocket = _bare_rocket() + rocket.power_off_drag = lambda mach: 0.9 + mach + assert rocket.power_off_drag_7d(0, 0, 0.5, 0, 0, 0, 0) == pytest.approx(1.4) + assert rocket.power_off_drag(0.5) == pytest.approx(1.4) + + +def test_length_can_be_given(calisto_robust): + rocket = _bare_rocket(length=3.0) + assert rocket.length == 3.0 + loaded = json.loads(json.dumps(rocket, cls=RocketPyEncoder), cls=RocketPyDecoder) + assert loaded.length == 3.0 + assert calisto_robust.length == pytest.approx(2.533, abs=1e-3) + + +def test_prints_list_the_lift_slope_of_every_surface(calisto_robust, capsys): + calisto_robust.prints.all() + out = capsys.readouterr().out + section = out[out.index("Lift Coefficient Derivatives") :][:400] + for surface, _ in calisto_robust.aerodynamic_surfaces: + assert f"{surface.name} Lift Coefficient Derivative" in section + + +def test_plots_survive_a_rocket_without_a_length(): + """No surfaces yet, or a point-like full-body model: the percent-of-length + axis is skipped instead of crashing.""" + _bare_rocket().plots.static_margin() + rocket = _bare_rocket() + surface = LinearGenericSurface( + rocket.area, 2 * rocket.radius, {"cN_alpha": 2, "cY_beta": -2} + ) + rocket.add_full_body_aerodynamics(surface, position=0.0) + rocket.plots.static_margin() + + +def test_the_same_fin_added_twice_keeps_both_positions(): + rocket = _bare_rocket() + rocket.add_nose(length=0.55829, kind="vonkarman", position=1.278) + fin = TrapezoidalFin(0, 0.12, 0.04, 0.1, 0.0635) + rocket.add_surfaces([fin, fin], [-1.168, -0.5]) + rocket._refresh_aerodynamics() + assert [p.z for s, p in rocket.aerodynamic_surfaces if s is fin] == [-1.168, -0.5] + + +def test_set_position_accepts_a_number(calisto_robust): + surface = calisto_robust.aerodynamic_surfaces[0][0] + calisto_robust.aerodynamic_surfaces.set_position(surface, 1.0) + calisto_robust.aerodynamic_center(0.3) + assert calisto_robust.aerodynamic_surfaces.get_positions()[0].z == 1.0 + + +def test_axisymmetric_linear_surface_fills_in_the_yaw_plane(): + """Pitch-only derivatives with ``axisymmetric=True`` give the same rocket + in yaw, also from wind-frame derivatives and after saving and loading; + without it a warning says the rocket has no side force.""" + rocket = _bare_rocket() + area, diameter = rocket.area, 2 * rocket.radius + pitch_only = {"cN_alpha": 3.0, "cm_alpha": -1.0, "cN_q": 4.0, "cm_q": -50.0} + linear = LinearGenericSurface(area, diameter, pitch_only, axisymmetric=True) + by_hand = LinearGenericSurface( + area, + diameter, + {**pitch_only, "cY_beta": -3.0, "cn_beta": 1.0, "cY_r": 4.0, "cn_r": -50.0}, + ) + state = (0.07, -0.04, 0.5, 0, 0.01, 0.02, 0.003) + for name in ("cN", "cY", "cm", "cn"): + assert getattr(linear, name)(*state) == getattr(by_hand, name)(*state) + + with warnings.catch_warnings(): + warnings.simplefilter("error") + rocket.add_full_body_aerodynamics(linear, position=0.0) + assert rocket.aerodynamic_center(0.3) == pytest.approx( + rocket.aerodynamic_center_yaw(0.3) + ) + assert rocket.is_axisymmetric + + wind = LinearGenericSurface( + area, diameter, {"cL_alpha": 2.5, "cD_0": 0.5}, axisymmetric=True + ) + at_zero = (0.0, 0.0, 0.5, 0, 0, 0, 0) + assert wind.cN_alpha(*at_zero) == pytest.approx(3.0) + assert wind.cY_beta(*at_zero) == pytest.approx(-3.0) + + loaded = json.loads(json.dumps(linear, cls=RocketPyEncoder), cls=RocketPyDecoder) + assert sorted(loaded.to_dict()["coefficients"]) == sorted(pitch_only) + assert loaded.cY(*state) == linear.cY(*state) + + with pytest.warns(UserWarning, match="side force"): + _bare_rocket().add_full_body_aerodynamics( + LinearGenericSurface(area, diameter, {"cN_alpha": 3.0}), position=0.0 + ) + + +def test_axisymmetric_linear_surface_refuses_what_singles_out_a_plane(): + """With ``axisymmetric=True`` the yaw plane comes from the pitch plane, so + a yaw derivative, or a pitch derivative that reads the angle of one plane, + is refused; the total angle of attack is the same in every plane.""" + with pytest.raises(ValueError, match="cY_beta cannot be given"): + LinearGenericSurface( + 1.0, 1.0, {"cN_alpha": 2.0, "cY_beta": -2.0}, axisymmetric=True + ) + with pytest.raises(ValueError, match="cQ_beta cannot be given"): + LinearGenericSurface( + 1.0, 1.0, {"cL_alpha": 2.0, "cQ_beta": -2.0}, axisymmetric=True + ) + for name in ("cN_0", "cm_0", "cN_p", "cm_p"): + with pytest.raises(ValueError, match=f"{name} cannot be given"): + LinearGenericSurface( + 1.0, 1.0, {"cN_alpha": 2.0, name: 0.1}, axisymmetric=True + ) + for angle, slope in ( + ("alpha", lambda alpha: 2.0 - alpha**2), + ("beta", lambda beta: 2.0 - beta**2), + ): + with pytest.raises(ValueError, match=f"cN_alpha depends on {angle}"): + LinearGenericSurface(1.0, 1.0, {"cN_alpha": slope}, axisymmetric=True) + + surface = LinearGenericSurface( + 1.0, + 1.0, + {"cN_alpha": lambda alpha_total: 9 - 40 * alpha_total**2}, + axisymmetric=True, + ) + assert surface.cN(0.2, 0, 0, 0, 0, 0, 0) == pytest.approx(1.48) + assert surface.cY(0, 0.2, 0, 0, 0, 0, 0) == pytest.approx(-1.48) diff --git a/tests/unit/rocket/test_stability_rework.py b/tests/unit/rocket/test_stability_rework.py index 8c27b07d1..917fd4cd5 100644 --- a/tests/unit/rocket/test_stability_rework.py +++ b/tests/unit/rocket/test_stability_rework.py @@ -2,10 +2,36 @@ cp_position alias, the reconstructed nonlinear center of pressure, and the aggregate aerodynamic coefficients.""" +import json +import math +import warnings +from unittest.mock import patch + import numpy as np import pytest -from rocketpy import Function, GenericSurface, LinearGenericSurface, Rocket +from rocketpy import ( + ControllableGenericSurface, + Function, + GenericSurface, + LinearGenericSurface, + PointMassRocket, + Rocket, +) +from rocketpy._encoders import RocketPyDecoder, RocketPyEncoder +from rocketpy.rocket.aero_surface.aero_coefficient import AeroCoefficient +from rocketpy.rocket._helpers import ( + aerodynamic_damping, + corrective_and_damping_moments, + lateral_inertia_and_rate, + stability_margin_and_slope, + stability_surfaces, + damping_derivative, + is_incidence_linear, + neutral_point_and_slope, + summed_force_and_moment, +) +from rocketpy.rocket.aero_surface.fins.trapezoidal_fin import TrapezoidalFin def _full_body_surface(rocket, coefficients, name="Full Body Aerodynamics", **kwargs): @@ -63,10 +89,59 @@ def test_length_extends_to_nozzle_past_surfaces(calisto_robust, cesaroni_m1670): assert rocket.length == pytest.approx(1.160 - (-2.0), abs=1e-9) -def test_length_requires_a_surface(calisto): - """A rocket with no aerodynamic surfaces has no defined length.""" - with pytest.raises(ValueError, match="at least one aerodynamic surface"): - _ = calisto.length +def test_length_needs_both_ends(calisto, calisto_nose_cone, calisto_tail): + """The length is only measured when a nose cone marks the front and a + tail, a fin set or a motor nozzle marks the back. A generic surface marks + neither. Otherwise it is ``None``, unless it was given.""" + bare = Rocket( + radius=0.0635, + mass=14.426, + inertia=(6.321, 6.321, 0.034), + power_off_drag=0.5, + power_on_drag=0.5, + center_of_mass_without_motor=0, + ) + assert bare.length is None + bare.add_surfaces( + GenericSurface(bare.area, 2 * bare.radius, {"cN": lambda alpha: 2 * alpha}), + (0, 0, 0), + ) + assert bare.length is None + bare.add_surfaces(calisto_tail, -1.313) + assert bare.length is None # no nose cone + bare.add_surfaces(calisto_nose_cone, 1.160) + assert bare.length == pytest.approx(1.160 - (-1.373), abs=1e-9) + + # A motor and a generic surface alone: the nozzle is known, the nose is not + calisto.add_surfaces( + GenericSurface( + calisto.area, 2 * calisto.radius, {"cN": lambda alpha: 2 * alpha} + ), + (0, 0, 0), + ) + assert calisto.length is None + # The nose cone and the motor nozzle are enough + calisto.add_surfaces(calisto_nose_cone, 1.160) + assert calisto.length == pytest.approx(1.160 - calisto.nozzle_position) + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + calisto.prints.rocket_aerodynamics_quantities() + + +def test_given_length_is_used_without_surfaces(capsys): + """A length given at construction is reported even when the rocket has no + aerodynamic surface to measure it from.""" + rocket = Rocket( + radius=0.0635, + mass=14.426, + inertia=(6.321, 6.321, 0.034), + power_off_drag=0.5, + power_on_drag=0.5, + center_of_mass_without_motor=0, + length=2.5, + ) + rocket.prints.rocket_aerodynamics_quantities() + assert "Rocket Length: 2.500 m" in capsys.readouterr().out def test_axisymmetric_rocket_planes_coincide(calisto_robust): @@ -328,3 +403,1166 @@ def test_add_full_body_aerodynamics_overwrite_warns_on_later_add(calisto_robust) later = _full_body_surface(rocket, {"cN": 1.0}) with pytest.warns(UserWarning, match="stacks on|summed on top"): rocket.add_full_body_aerodynamics(later) + + +@pytest.mark.parametrize("orientation", ["tail_to_nose", "nose_to_tail"]) +@pytest.mark.parametrize("cp_z", [-0.3, 0.3]) +def test_generic_surface_cp_offset_matches_flight_neutral_point(orientation, cp_z): + """A generic surface's ``center_of_pressure`` must move the aerodynamic + center to the same point the flight applies the force at (the neutral + point), in both planes and for both coordinate system orientations.""" + rocket = Rocket( + radius=0.05, + mass=10, + inertia=(5, 5, 0.02), + power_off_drag=0.5, + power_on_drag=0.5, + center_of_mass_without_motor=0, + coordinate_system_orientation=orientation, + ) + surface = LinearGenericSurface( + reference_area=rocket.area, + reference_length=2 * rocket.radius, + coefficients={"cN_alpha": 2.0, "cY_beta": -2.0}, + center_of_pressure=(0, 0, cp_z), + ) + rocket.add_surfaces(surface, 0.1) + + # The center of pressure is given along the body z-axis (toward the nose) + csys = 1 if orientation == "tail_to_nose" else -1 + expected = 0.1 + csys * cp_z + assert rocket.aerodynamic_center(0.3) == pytest.approx(expected) + assert rocket.aerodynamic_center_yaw(0.3) == pytest.approx(expected) + assert rocket.neutral_point(0.0, 0.3) == pytest.approx(expected) + assert rocket.neutral_point_yaw(0.0, 0.3) == pytest.approx(expected) + + +def _rocket_with(surface, position): + """A small rocket of built-in surfaces plus one generic surface.""" + rocket = Rocket( + radius=0.0635, + mass=14.426, + inertia=(6.321, 6.321, 0.034), + power_off_drag=0.5, + power_on_drag=0.5, + center_of_mass_without_motor=0, + coordinate_system_orientation="tail_to_nose", + ) + rocket.add_nose(length=0.55829, kind="vonkarman", position=1.278) + rocket.add_trapezoidal_fins( + n=4, span=0.100, root_chord=0.120, tip_chord=0.040, position=-1.04956 + ) + rocket.add_surfaces(surface, position) + return rocket + + +def _margin_at_the_true_state(rocket, alpha, beta, plane): + """Margin of one plane with both flow angles at their real values.""" + neutral_point, _ = neutral_point_and_slope(rocket, alpha, beta, 0.3, plane) + center_of_mass = rocket.center_of_mass.get_value_opt(0.0) + return rocket._csys * (center_of_mass - neutral_point) / (2 * rocket.radius) + + +@pytest.mark.parametrize( + "surface, position", + [ + # A pure couple: a moment slope with no force slope + ( + LinearGenericSurface(math.pi * 0.0635**2, 0.127, {"cm_alpha": -1.5}), + 0.5, + ), + ( + LinearGenericSurface(math.pi * 0.0635**2, 0.127, {"cn_beta": 1.5}), + 0.5, + ), + # An axial force that changes with the angle, at a sideways offset + ( + LinearGenericSurface( + math.pi * 0.0635**2, + 0.127, + {"cN_alpha": 1.0, "cA_alpha": 3.0}, + center_of_pressure=(0, 0.05, 0), + ), + (0, 0, 0.5), + ), + ( + LinearGenericSurface( + math.pi * 0.0635**2, + 0.127, + {"cY_beta": -1.0, "cA_beta": 3.0}, + center_of_pressure=(0.05, 0, 0), + ), + (0, 0, 0.5), + ), + # A canted individual fin: part of its force acts along the axis + ( + TrapezoidalFin( + angular_position=30, + span=0.06, + root_chord=0.08, + tip_chord=0.04, + rocket_radius=0.0635, + cant_angle=3, + ), + 0.9, + ), + ], +) +def test_aerodynamic_center_counts_the_whole_moment_of_each_surface(surface, position): + """The aerodynamic center is the neutral point at zero angle, taken from the + rocket's summed forces and moments, even for a surface whose moment is not + its force slope times an arm along the axis.""" + rocket = _rocket_with(surface, position) + for mach in (0.0, 0.3, 0.9): + for plane, center in ( + ("pitch", rocket.aerodynamic_center), + ("yaw", rocket.aerodynamic_center_yaw), + ): + expected, _ = neutral_point_and_slope(rocket, 0.0, 0.0, mach, plane) + assert center.get_value_opt(mach) == pytest.approx(expected, abs=1e-6) + + +def test_margin_of_a_non_axisymmetric_rocket_follows_the_angle_of_its_plane(): + """A rocket that is not axisymmetric is analysed one plane at a time: its + pitch margin goes with the angle of attack and its yaw margin with the + sideslip angle. Reading both at the total angle of attack is wrong: in a + pure sideslip it reports the pitch plane of this rocket as unstable while + it is stable.""" + area, diameter = math.pi * 0.0635**2, 2 * 0.0635 + canards = GenericSurface( + area, + diameter, + {"cN": lambda alpha: 3.0 * alpha + 60.0 * alpha**3, "cY": 0}, + force_convention="body", + ) + rocket = _rocket_with(canards, 0.8) + assert not rocket.is_axisymmetric + + alpha, beta = 0.0, math.radians(10.0) # a pure sideslip + reference = _margin_at_the_true_state(rocket, alpha, beta, "pitch") + at_its_own_angle = stability_margin_and_slope(rocket, alpha, 0.0, 0.3, 0.0)[0] + at_the_total_angle = stability_margin_and_slope(rocket, beta, 0.0, 0.3, 0.0)[0] + + assert at_its_own_angle == pytest.approx(reference, abs=0.05) + assert reference > 1.0 > 0.0 > at_the_total_angle + + # the yaw plane of this rocket is linear, whatever the sideslip + yaw_reference = _margin_at_the_true_state(rocket, alpha, beta, "yaw") + yaw_margin = stability_margin_and_slope(rocket, 0.0, beta, 0.3, 0.0, "yaw")[0] + assert yaw_margin == pytest.approx(yaw_reference, abs=1e-6) + + +def test_margin_of_an_axisymmetric_rocket_follows_the_total_angle(): + """An axisymmetric rocket behaves the same in every plane, so it has one + margin, taken in the plane of the wind at the total angle of attack: the + same value whether the wind comes as an angle of attack or as a sideslip.""" + area, diameter = math.pi * 0.0635**2, 2 * 0.0635 + body_lift = GenericSurface( + area, + diameter, + {"cN": lambda alpha_total: 12.0 * math.sin(alpha_total) ** 2}, + ) + rocket = _rocket_with(body_lift, 0.3) + assert rocket.is_axisymmetric and not rocket.is_incidence_linear + + angle = math.radians(8.0) + in_the_wind_plane = stability_margin_and_slope(rocket, angle, 0.0, 0.3, 0.0)[0] + assert in_the_wind_plane == pytest.approx( + _margin_at_the_true_state(rocket, angle, 0.0, "pitch"), abs=1e-6 + ) + assert in_the_wind_plane == pytest.approx( + _margin_at_the_true_state(rocket, 0.0, angle, "yaw"), abs=1e-6 + ) + across = stability_margin_and_slope(rocket, 0.0, angle, 0.3, 0.0, "yaw")[0] + assert across == pytest.approx(in_the_wind_plane, abs=1e-6) + + +def test_restoring_slope_is_positive_in_both_planes(): + """The corrective moment ``q A slope margin d`` is a stiffness: positive for + a stable rocket in either plane. The slope handed to it is therefore + positive in yaw too, where a restoring side force has a negative + ``dCY/dbeta``, and an axisymmetric rocket gives the same value in both + planes, whether or not one of its surfaces is nonlinear.""" + area, diameter = math.pi * 0.0635**2, 2 * 0.0635 + linear = _rocket_with(GenericSurface(area, diameter, {"cN": 0}), 0.3) + body_lift = GenericSurface( + area, + diameter, + {"cN": lambda alpha_total: 12.0 * math.sin(alpha_total) ** 2}, + ) + nonlinear = _rocket_with(body_lift, 0.3) + assert linear.is_incidence_linear and not nonlinear.is_incidence_linear + + for rocket in (linear, nonlinear): + margin, slope = stability_margin_and_slope(rocket, 0.05, 0.0, 0.3, 0.0, "pitch") + margin_yaw, slope_yaw = stability_margin_and_slope( + rocket, 0.0, 0.05, 0.3, 0.0, "yaw" + ) + assert slope > 0 + assert slope_yaw == pytest.approx(slope, rel=1e-6) + assert margin_yaw == pytest.approx(margin, rel=1e-6) + + +def _table_that_stalls(): + """``cN = 2 alpha`` up to 12 degrees, flat beyond, as a table of points.""" + limit = math.radians(12) + return [ + [angle, 2.0 * max(-limit, min(limit, angle))] + for angle in np.radians(np.arange(-20, 21, 1.0)) + ] + + +@pytest.mark.parametrize( + "coefficients, force_convention, linear", + [ + ({"cN": lambda alpha: 2 * alpha, "cY": lambda beta: -2 * beta}, "body", True), + ( + { + "cN": lambda alpha, mach: (2 + mach) * alpha, + "cm": lambda alpha: alpha / 3, + }, + "body", + True, + ), + ({"cN": lambda alpha: 3 * alpha + 60 * alpha**3}, "body", False), + # Nonlinear only past the 5 degrees the check used to stop at + ( + {"cN": lambda alpha: 2 * alpha + 400 * max(abs(alpha) - 0.14, 0) ** 2}, + "body", + False, + ), + ({"cN": (_table_that_stalls(), ["alpha"])}, "body", False), + # Nonlinear only at high Mach + ( + { + "cN": lambda alpha, mach: ( + 2 * alpha + (50 * alpha**3 if mach > 1.5 else 0) + ) + }, + "body", + False, + ), + # The force is linear but the moment is not + ( + { + "cN": lambda alpha: 2 * alpha, + "cm": lambda alpha: alpha / 5 + 8 * alpha**3, + }, + "body", + False, + ), + ], +) +def test_is_incidence_linear_reads_the_whole_range( + coefficients, force_convention, linear +): + """The rocket is linear only when its neutral point stays put over the angles + and Mach numbers a flight sees, not just at a single angle and Mach.""" + area, diameter = math.pi * 0.0635**2, 2 * 0.0635 + surface = GenericSurface( + area, diameter, coefficients, force_convention=force_convention + ) + rocket = _rocket_with(surface, 0.5) + assert rocket.is_incidence_linear is linear + + # The answer must match the neutral point found from the flight forces + moved = 0.0 + for mach in (0.3, 2.0): + at_zero = neutral_point_and_slope(rocket, 0.0, 0.0, mach, "pitch")[0] + for degrees in (3, 9, 14): + angle = math.radians(degrees) + point = neutral_point_and_slope(rocket, angle, 0.0, mach, "pitch")[0] + moved = max(moved, abs(point - at_zero)) + assert (moved <= 1e-6) == linear + + +def test_a_single_nonlinear_surface_cannot_move_the_neutral_point(): + """A rocket whose whole lift comes from one surface with a fixed center of + pressure has a neutral point that cannot move, however nonlinear its force. + It keeps the fast, linear path.""" + area, diameter = math.pi * 0.0635**2, 2 * 0.0635 + rocket = Rocket( + radius=0.0635, + mass=14.426, + inertia=(6.321, 6.321, 0.034), + power_off_drag=0.5, + power_on_drag=0.5, + center_of_mass_without_motor=0, + coordinate_system_orientation="tail_to_nose", + ) + whole_body = GenericSurface( + area, + diameter, + {"cN": lambda alpha_total: alpha_total + 12 * math.sin(alpha_total) ** 2}, + ) + rocket.add_surfaces(whole_body, -0.4) + assert rocket.is_incidence_linear + + +def test_is_incidence_linear_costs_nothing_for_the_built_in_surfaces(calisto_robust): + """The built-in surfaces are linear whatever their data, so none of their + coefficients is read.""" + with patch.object( + AeroCoefficient, "get_value_opt", side_effect=AssertionError("read") + ): + assert is_incidence_linear(calisto_robust) + + +# Aerodynamic damping (C2 of the flight oscillator) + + +def _linear_damping(rocket, mach, plane): + """The textbook sum 0.5 A sum((A_i / A) slope_i arm_i**2) at zero angle.""" + center_of_mass = rocket.center_of_mass.get_value_opt(0.0) + total = 0.0 + for surface, position in rocket.aerodynamic_surfaces: + if plane == "yaw": + slope = -surface.cY_beta.get_value_opt(0, 0, mach, 0, 0, 0, 0) + cp_z = surface.aerodynamic_center_yaw.get_value_opt(mach) + else: + slope = surface.cN_alpha.get_value_opt(0, 0, mach, 0, 0, 0, 0) + cp_z = surface.aerodynamic_center.get_value_opt(mach) + arm = position.z + rocket._csys * cp_z - center_of_mass + total += surface.reference_area / rocket.area * slope * arm**2 + return 0.5 * rocket.area * total + + +@pytest.mark.parametrize("plane", ["pitch", "yaw"]) +def test_damping_derivative_matches_the_linear_sum(calisto_robust, plane): + """For a rocket of built-in surfaces the rate derivative of the summed + moment about the center of mass equals the textbook slope-times-arm-squared + sum, so the two paths of ``aerodynamic_damping`` agree.""" + rocket = calisto_robust + assert rocket.is_incidence_linear + closed_form = aerodynamic_damping(rocket, 0.0, 0.0, 0.3, 0.0, plane) + assert closed_form == pytest.approx(_linear_damping(rocket, 0.3, plane)) + + reference_z = -rocket.com_to_cdm_function.get_value_opt(0.0) + derivative = damping_derivative(rocket, 0.0, 0.0, 0.3, plane, reference_z) + assert derivative == pytest.approx(closed_form, rel=1e-5) + assert derivative > 0 + + +def test_boat_tail_takes_damping_away(calisto_robust): + """A surface with a negative force slope (a boat tail) reduces the + damping; it must not be counted as if it added to it.""" + rocket = calisto_robust + tail = next(s for s, _ in rocket.aerodynamic_surfaces if "Tail" in type(s).__name__) + assert tail.cN_alpha.get_value_opt(0, 0, 0.3, 0, 0, 0, 0) < 0 + with_tail = aerodynamic_damping(rocket, 0.0, 0.0, 0.3, 0.0, "pitch") + rocket.aerodynamic_surfaces.remove(tail) + without_tail = aerodynamic_damping(rocket, 0.0, 0.0, 0.3, 0.0, "pitch") + assert with_tail < without_tail + + +def test_damping_reads_a_user_rate_coefficient(): + """A generic surface whose pitch moment depends on the pitch rate adds + exactly that damping, ``0.25 A d**2 |cm_q|`` per unit density and speed, + which the zero-angle slope sum alone would miss.""" + area, diameter = math.pi * 0.0635**2, 2 * 0.0635 + cm_q = -50.0 + surface = LinearGenericSurface( + area, diameter, {"cN_alpha": 0.0, "cm_q": cm_q}, name="Rate damping" + ) + rocket = _rocket_with(surface, 0.0) + assert rocket.is_incidence_linear # refreshes the flags + assert rocket._uses_rate_coefficients + without = _linear_damping(rocket, 0.3, "pitch") + expected = without + 0.25 * area * diameter**2 * abs(cm_q) + assert aerodynamic_damping(rocket, 0.0, 0.0, 0.3, 0.0, "pitch") == pytest.approx( + expected, rel=1e-4 + ) + + +def _phase_gated_rocket(): + """A rocket carrying one full-body surface per motor phase, the pair the + documentation recommends, with a different center of pressure in each.""" + rocket = Rocket( + radius=0.0635, + mass=14.426, + inertia=(6.321, 6.321, 0.034), + power_off_drag=0.5, + power_on_drag=0.6, + center_of_mass_without_motor=0, + ) + surfaces = [ + LinearGenericSurface( + rocket.area, + 2 * rocket.radius, + {"cN_alpha": 2.0, "cY_beta": -2.0}, + center_of_pressure=(0, 0, cp_z), + active_during=phase, + ) + for cp_z, phase in ((-0.5, "power_off"), (-0.3, "power_on")) + ] + rocket.add_full_body_aerodynamics(surfaces, position=0.0) + return rocket + + +def test_stability_counts_only_the_surfaces_of_its_phase(): + """A surface active during one motor phase only enters the stability + analysis of that phase: coasting by default, the powered phase when + ``stability_phase`` says so. Summing both would double the lift slope + and average the two centers of pressure.""" + rocket = _phase_gated_rocket() + with pytest.warns(UserWarning, match="power_off"): + assert rocket.aerodynamic_center(0.3) == pytest.approx(-0.5) + assert rocket.total_lift_coeff_der(0.3) == pytest.approx(2.0) + assert rocket.aerodynamic_center_yaw(0.3) == pytest.approx(-0.5) + assert rocket.neutral_point(0.0, 0.3) == pytest.approx(-0.5) + + rocket.stability_phase = "power_on" + with pytest.warns(UserWarning, match="power_on"): + assert rocket.aerodynamic_center(0.3) == pytest.approx(-0.3) + assert rocket.total_lift_coeff_der(0.3) == pytest.approx(2.0) + assert rocket.neutral_point(0.0, 0.3) == pytest.approx(-0.3) + + rocket.stability_phase = "coasting" + with pytest.raises(ValueError, match="stability_phase"): + rocket.aerodynamic_center(0.3) + + +def test_to_coefficients_lumps_each_phase_with_its_own_surfaces(): + """The lumped power-on and power-off sets each come from the surfaces + active in that phase, whatever ``stability_phase`` is set to.""" + rocket = _phase_gated_rocket() + coefficients = rocket.to_coefficients(machs=[0.3, 0.9]) + diameter = 2 * rocket.radius + for phase, cp_z, drag in (("power_off", -0.5, 0.5), ("power_on", -0.3, 0.6)): + phase_set = coefficients[phase] + assert phase_set["cN_alpha"](0.3) == pytest.approx(2.0, rel=1e-6) + # a force of slope 2 at cp_z gives a pitch moment slope of 2 cp_z / d + assert phase_set["cm_alpha"](0.3) == pytest.approx( + 2.0 * cp_z / diameter, rel=1e-6 + ) + assert phase_set["cA_0"](0.3) == pytest.approx(drag) + + +def test_ungated_rocket_has_the_same_stability_in_both_phases(calisto_robust): + """``stability_phase`` changes nothing for a rocket whose surfaces are all + always active, and no phase warning is shown.""" + rocket = calisto_robust + coasting = rocket.aerodynamic_center(0.5) + with warnings.catch_warnings(): + warnings.simplefilter("error") + rocket.stability_phase = "power_on" + assert rocket.aerodynamic_center(0.5) == coasting + assert rocket.to_coefficients(machs=[0.5, 1.0])["power_on"]["cN_alpha"]( + 0.5 + ) == pytest.approx(rocket.total_lift_coeff_der(0.5), rel=1e-6) + + +def _lumped_twin(rocket, force_convention="body"): + """A bare rocket carrying only the coasting surface of ``to_surface``.""" + twin = Rocket( + radius=rocket.radius, + mass=rocket.mass, + inertia=(6.321, 6.321, 0.034), + power_off_drag=0, + power_on_drag=0, + center_of_mass_without_motor=rocket.center_of_mass_without_motor, + ) + power_off, _ = rocket.to_surface( + machs=[0.2, 0.3, 0.4], force_convention=force_convention + ) + twin.add_full_body_aerodynamics( + power_off, position=rocket.center_of_dry_mass_position + ) + return twin + + +def _canted_calisto(): + rocket = Rocket( + radius=0.0635, + mass=14.426, + inertia=(6.321, 6.321, 0.034), + power_off_drag=0.5, + power_on_drag=0.5, + center_of_mass_without_motor=0, + ) + rocket.add_nose(length=0.55829, kind="vonkarman", position=1.278) + rocket.add_trapezoidal_fins( + n=4, + span=0.1, + root_chord=0.12, + tip_chord=0.04, + position=-1.04956, + cant_angle=2.0, + ) + return rocket + + +def test_lumped_coefficients_keep_the_roll_forcing_of_canted_fins(): + """Canted fins roll the rocket at zero angle of attack. The lumped set + keeps that as ``cl_0`` and the lumped rocket rolls the same way.""" + rocket = _canted_calisto() + coefficients = rocket.to_coefficients(machs=[0.3, 0.9])["power_off"] + assert coefficients["cl_0"](0.3) < 0 + assert "cN_beta" not in coefficients # still axisymmetric: no cross terms + twin = _lumped_twin(rocket) + original = summed_force_and_moment(rocket, 0.0, 0.0, 0.3, (0, 0, 0.5)) + lumped = summed_force_and_moment(twin, 0.0, 0.0, 0.3, (0, 0, 0.5)) + assert lumped[5] == pytest.approx(original[5], rel=1e-6) + + +@pytest.mark.parametrize("force_convention", ["body", "wind"]) +def test_lumped_coefficients_keep_the_cross_terms_of_an_asymmetric_rocket( + force_convention, +): + """A single canted fin off the body axes couples the two planes and has a + force and a moment at zero angle. The lumped rocket reproduces the + original's force and moment to first order in the angles and rates, in + either frame.""" + rocket = Rocket( + radius=0.0635, + mass=14.426, + inertia=(6.321, 6.321, 0.034), + power_off_drag=0.5, + power_on_drag=0.5, + center_of_mass_without_motor=0, + ) + rocket.add_nose(length=0.55829, kind="vonkarman", position=1.278) + rocket.add_surfaces( + TrapezoidalFin(45, 0.12, 0.04, 0.1, 0.0635, cant_angle=2.0), -1.04956 + ) + coefficients = rocket.to_coefficients(machs=[0.3, 0.9])["power_off"] + for name in ("cN_beta", "cm_beta", "cn_alpha", "cN_0", "cm_0", "cl_0", "cl_alpha"): + assert name in coefficients + twin = _lumped_twin(rocket, force_convention) + for alpha, beta, omega in ( + (0.0, 0.0, (0, 0, 0)), + (0.01, 0.0, (0, 0, 0)), + (0.0, 0.01, (0, 0, 0)), + (0.01, 0.01, (0.1, 0.1, 0.1)), + ): + original = summed_force_and_moment(rocket, alpha, beta, 0.3, omega, speed=100) + lumped = summed_force_and_moment(twin, alpha, beta, 0.3, omega, speed=100) + # the twin's cA_0 holds the rocket's own drag curve on top of the fin + lumped[2] += 0.5 * 100**2 * rocket.area * 0.5 + np.testing.assert_allclose(lumped, original, rtol=2e-3, atol=1e-6) + + +def test_to_coefficients_accepts_a_single_mach(): + rocket = _canted_calisto() + coefficients = rocket.to_coefficients(machs=[0.3])["power_off"] + assert coefficients["cN_alpha"](0.3) == pytest.approx( + rocket.total_lift_coeff_der(0.3), rel=1e-6 + ) + + +def _source_coefficients(rocket, alpha, mach, reynolds=None): + """``(cN, cm)`` of the rocket's own surfaces, summed directly.""" + rocket.evaluate_surfaces_cp_to_cdm() + forces = summed_force_and_moment( + rocket, alpha, 0.0, mach, (0, 0, 0), reynolds=reynolds + ) + dynamic_pressure_area = 0.5 * rocket.area + return ( + -forces[1] / dynamic_pressure_area, + forces[3] / (dynamic_pressure_area * 2 * rocket.radius), + ) + + +def _reynolds_rocket(): + """A rocket with a surface whose lift grows with the Reynolds number.""" + area, diameter = math.pi * 0.0635**2, 2 * 0.0635 + + def gain(reynolds): + return 1 + math.log10(max(reynolds, 1.0)) + + surface = GenericSurface( + area, + diameter, + { + "cN": lambda alpha, reynolds: alpha * gain(reynolds), + "cY": lambda beta, reynolds: -beta * gain(reynolds), + }, + ) + return _rocket_with(surface, 0.3) + + +@pytest.mark.parametrize("model", ["linear", "table"]) +def test_lumping_follows_the_reynolds_number(model): + """A single Reynolds number sets the value the coefficients are read at; + a list makes them (and the surface built from them) follow it. The + Reynolds number is the rocket's, based on its diameter.""" + rocket = _reynolds_rocket() + machs, angles = [0.2, 0.5, 0.8], np.radians([-10, -5, 0, 5, 10]) + alpha = math.radians(5) + + at_zero = rocket.to_coefficients(machs=machs)["power_off"]["cN_alpha"](0.5) + fixed = rocket.to_coefficients(machs=machs, reynolds=1e6)["power_off"] + assert fixed["cN_alpha"](0.5) == pytest.approx(at_zero + 6.0, rel=1e-6) + swept = rocket.to_coefficients(machs=machs, reynolds=[1e5, 1e6, 1e7]) + assert swept["power_off"]["cN_alpha"](0.5, 1e7) == pytest.approx( + at_zero + 7.0, rel=1e-6 + ) + + power_off, _ = rocket.to_surface( + machs=machs, model=model, angles=angles, reynolds=[1e5, 1e6, 1e7] + ) + for reynolds in (1e5, 1e6, 1e7): + expected = _source_coefficients(rocket, alpha, 0.5, reynolds)[0] + lumped = power_off.cN(alpha, 0, 0.5, reynolds, 0, 0, 0) + assert lumped == pytest.approx(expected, rel=1e-6) + + +def _canard(name): + area, diameter = math.pi * 0.0635**2, 2 * 0.0635 + return ControllableGenericSurface( + area, + diameter, + { + "cN": lambda alpha, deflection: 1.5 * (alpha + deflection), + "cY": lambda beta: -1.5 * beta, + }, + name=name, + ) + + +def test_lumping_keeps_a_control(): + """A control listed in ``controls`` becomes an input of the coefficients, + the surface built from them is controllable and matches the rocket at a + deflection, and the rocket's own control is left where it was.""" + canard = _canard("Canard") + rocket = _rocket_with(canard, 0.8) + canard.set_control("deflection", 0.02) + machs, angles = [0.2, 0.5, 0.8], np.radians([-10, 0, 10]) + controls = {"deflection": np.radians([-10, 0, 10])} + + linear = rocket.to_coefficients(machs=machs, controls=controls)["power_off"] + assert linear["cN_0"](0.5, math.radians(10)) == pytest.approx( + 1.5 * math.radians(10) + ) + assert canard.get_control("deflection") == 0.02 + + power_off, _ = rocket.to_surface( + machs=machs, model="table", angles=angles, controls=controls + ) + assert isinstance(power_off, ControllableGenericSurface) + assert power_off.control_variables == ["deflection"] + alpha, deflection = math.radians(3), math.radians(5) + power_off.set_control("deflection", deflection) + canard.set_control("deflection", deflection) + args = power_off._coefficient_arguments(alpha, 0, 0.5, 0, 0, 0, 0) + expected = _source_coefficients(rocket, alpha, 0.5) + assert power_off.cN.get_value_opt(*args) == pytest.approx(expected[0]) + assert power_off.cm.get_value_opt(*args) == pytest.approx(expected[1]) + + with pytest.raises(ValueError, match="needs model='table'"): + rocket.to_surface(machs=machs, controls=controls) + with pytest.raises(ValueError, match="no control named 'flap'"): + rocket.to_coefficients(machs=machs, controls={"flap": [0, 1]}) + with pytest.raises(ValueError, match="at least two different values"): + rocket.to_coefficients(machs=machs, controls={"deflection": [0.1]}) + + +def test_lumping_keeps_same_named_controls_apart(): + """Two surfaces with a control of the same name give two inputs, named + after their surfaces; asking for the shared name warns and sweeps both.""" + rocket = _rocket_with(_canard("Canard A"), 0.8) + rocket.add_surfaces(_canard("Canard B"), 0.6) + machs = [0.2, 0.5] + names = ["mach", "canard_a_deflection", "canard_b_deflection"] + + with pytest.warns(UserWarning, match="used by 2 surfaces"): + shared = rocket.to_coefficients(machs=machs, controls={"deflection": [0, 0.1]}) + assert shared["power_off"]["cN_0"].__inputs__ == names + + apart = rocket.to_coefficients( + machs=machs, + controls={"canard_a_deflection": [0, 0.1], "canard_b_deflection": [0, 0.2]}, + )["power_off"] + assert apart["cN_0"].__inputs__ == names + assert apart["cN_0"](0.5, 0.1, 0.2) == pytest.approx(1.5 * 0.3) + + +def test_table_rates_can_be_read_at_each_angle(): + """With ``rates="at_each_angle"`` the rate terms are tables over the + angles, and the surface built from them has the damping of the rocket at + an angle, which the zero-angle rate terms miss.""" + rocket = _body_lift_rocket() + machs, angles = [0.2, 0.5, 0.8], np.radians([-10, -5, 0, 5, 10]) + theta = math.radians(10) + + coefficients = rocket.to_coefficients( + machs=machs, model="table", angles=angles, rates="at_each_angle" + )["power_off"] + assert coefficients["cm_q"].__inputs__ == ["alpha", "beta", "mach"] + + def twin_damping(rates): + twin = _table_twin(rocket, angles=angles, rates=rates) + return damping_derivative(twin, theta, 0.0, 0.3) + + expected = damping_derivative(rocket, theta, 0.0, 0.3) + assert twin_damping("at_each_angle") == pytest.approx(expected, rel=1e-4) + assert twin_damping(True) != pytest.approx(expected, rel=1e-2) + + with pytest.raises(ValueError, match="needs model='table'"): + rocket.to_coefficients(machs=machs, rates="at_each_angle") + with pytest.raises(ValueError, match="rates must be"): + rocket.to_coefficients(machs=machs, rates="sometimes") + + +def _table_twin(rocket, **kwargs): + """A bare rocket carrying only the coasting table surface of ``to_surface``.""" + twin = Rocket( + radius=rocket.radius, + mass=rocket.mass, + inertia=(6.321, 6.321, 0.034), + power_off_drag=0, + power_on_drag=0, + center_of_mass_without_motor=rocket.center_of_mass_without_motor, + ) + power_off, _ = rocket.to_surface(model="table", machs=[0.2, 0.3, 0.4], **kwargs) + twin.add_full_body_aerodynamics( + power_off, position=rocket.center_of_dry_mass_position + ) + return twin + + +def _assert_twin_matches(rocket, twin, states, rtol): + """The twin's summed force and moment equal the original's at each + ``(alpha, beta, omega)`` state, once the rocket's own drag, which the + twin carries in its axial coefficient, is put back.""" + for alpha, beta, omega in states: + original = summed_force_and_moment(rocket, alpha, beta, 0.3, omega, speed=100) + lumped = summed_force_and_moment(twin, alpha, beta, 0.3, omega, speed=100) + lumped[2] += 0.5 * 100**2 * rocket.area * rocket.power_off_drag(0.3) + np.testing.assert_allclose( + lumped, original, rtol=rtol, atol=rtol * np.abs(original).max() + ) + + +def test_table_model_keeps_the_curve_of_an_axisymmetric_rocket(): + """An axisymmetric rocket with a body-lift term that is far from linear + lumps to a 2-D table over the total angle of attack. The lumped rocket + matches the original at every angle of the table, from either side and at + combined angles, where the linear model is exact only near zero.""" + rocket = _canted_calisto() + body_lift = GenericSurface( + rocket.area, + 2 * rocket.radius, + {"cN": lambda alpha_total: 12.0 * math.sin(alpha_total) ** 2}, + ) + rocket.add_surfaces(body_lift, 0.3) + assert rocket.is_axisymmetric + angles = np.radians(np.arange(-40, 41, 2)) + coefficients = rocket.to_coefficients( + model="table", machs=[0.2, 0.3, 0.4], angles=angles + ) + tables = coefficients["power_off"] + assert set(tables) >= {"cN", "cA", "cm", "cl", "cm_q", "cl_p"} + assert "cY" not in tables and "cn" not in tables + assert tables["cN"].__inputs__ == ["alpha_total", "mach"] + + twin = _table_twin(rocket, angles=angles) + deg = math.radians + static = [ + (0.0, 0.0, (0, 0, 0)), + (deg(10), 0.0, (0, 0, 0)), + (0.0, deg(-25), (0, 0, 0)), + (deg(20), deg(20), (0, 0, 0)), + (deg(40), 0.0, (0, 0, 0)), + ] + _assert_twin_matches(rocket, twin, static, rtol=1e-3) + # the rate terms are those of the linear model, read at zero angle + _assert_twin_matches(rocket, twin, [(deg(5), deg(3), (0.3, 0.2, 0.5))], rtol=1e-2) + + +def test_table_model_keeps_the_coupling_of_a_lopsided_rocket(): + """A single canted fin off the body axes plus a nonlinear canard in one + plane lump to 3-D tables over both angles that reproduce the original at + combined angles, cross coupling included.""" + rocket = Rocket( + radius=0.0635, + mass=14.426, + inertia=(6.321, 6.321, 0.034), + power_off_drag=0.5, + power_on_drag=0.5, + center_of_mass_without_motor=0, + ) + rocket.add_nose(length=0.55829, kind="vonkarman", position=1.278) + rocket.add_surfaces( + TrapezoidalFin(45, 0.12, 0.04, 0.1, 0.0635, cant_angle=2.0), -1.04956 + ) + canard = GenericSurface( + rocket.area, + 2 * rocket.radius, + {"cN": lambda alpha: 1.5 * math.sin(2 * alpha) + 20 * alpha**3, "cY": 0}, + ) + rocket.add_surfaces(canard, 0.8) + assert not rocket.is_axisymmetric + angles = np.radians(np.arange(-30, 31, 5)) + tables = rocket.to_coefficients( + model="table", machs=[0.2, 0.3, 0.4], angles=angles + )["power_off"] + assert set(tables) >= {"cN", "cY", "cA", "cm", "cn", "cl"} + assert tables["cN"].__inputs__ == ["alpha", "beta", "mach"] + + twin = _table_twin(rocket, angles=angles) + deg = math.radians + static = [ + (0.0, 0.0, (0, 0, 0)), + (deg(10), 0.0, (0, 0, 0)), + (0.0, deg(25), (0, 0, 0)), + (deg(20), deg(-20), (0, 0, 0)), + ] + _assert_twin_matches(rocket, twin, static, rtol=1e-3) + _assert_twin_matches(rocket, twin, [(deg(5), deg(3), (0.3, 0.2, 0.5))], rtol=1e-2) + + +def test_lumping_without_rates_has_no_damping(): + """``rates=False`` leaves the rate terms out of both models: the linear + set has no ``_p``/``_q``/``_r`` keys and the table twin feels nothing + from a pure body rate.""" + rocket = _canted_calisto() + linear = rocket.to_coefficients(machs=[0.3, 0.9], rates=False)["power_off"] + assert not any(name[-2:] in ("_p", "_q", "_r") for name in linear) + assert "cN_alpha" in linear and "cl_0" in linear + twin = _table_twin(rocket, rates=False) + moment = summed_force_and_moment(twin, 0.0, 0.0, 0.3, (1.0, 0, 0), speed=100) + assert moment[3] == 0.0 + + +def test_lumping_rejects_bad_options(): + rocket = _canted_calisto() + with pytest.raises(ValueError, match="model"): + rocket.to_coefficients(model="quadratic") + with pytest.raises(ValueError, match="body-frame"): + rocket.to_coefficients(model="table", force_convention="wind") + with pytest.raises(ValueError, match="at least two"): + rocket.to_coefficients(model="table", machs=[0.3]) + + +def test_table_surface_saves_and_loads(): + """The table surfaces round trip through the RocketPy encoder.""" + rocket = _canted_calisto() + power_off, _ = rocket.to_surface( + model="table", machs=[0.2, 0.3], angles=np.radians([-10, 0, 10]) + ) + loaded = json.loads(json.dumps(power_off, cls=RocketPyEncoder), cls=RocketPyDecoder) + args = (math.radians(6), math.radians(-4), 0.25, 0.0, 0.1, 0.0, 0.2) + for name in ("cN", "cY", "cA", "cm", "cn", "cl"): + assert getattr(loaded, name).get_value_opt(*args) == pytest.approx( + getattr(power_off, name).get_value_opt(*args) + ) + + +def _rocket_of(*surfaces_and_positions): + rocket = Rocket( + radius=0.0635, + mass=14.426, + inertia=(6.321, 6.321, 0.034), + power_off_drag=0.5, + power_on_drag=0.5, + center_of_mass_without_motor=0, + ) + rocket.add_nose(length=0.55829, kind="vonkarman", position=1.278) + for surface, position in surfaces_and_positions: + rocket.add_surfaces(surface, position) + return rocket + + +def _fin(angular_position, cant_angle=0.0): + return TrapezoidalFin( + angular_position, 0.12, 0.04, 0.1, 0.0635, cant_angle=cant_angle + ) + + +def test_a_coupled_rocket_is_not_axisymmetric(): + """A single fin at 45 degrees pushes equally in pitch and yaw with the same + center of pressure in both planes, so the two margins agree. It is still + not axisymmetric: a pitch also yaws it, and along its own plane it does + not push back at all.""" + rocket = _rocket_of((_fin(45), -1.04956)) + with pytest.warns(UserWarning, match="couple"): + assert not rocket.is_axisymmetric + assert rocket.aerodynamic_center(0.3) == pytest.approx( + rocket.aerodynamic_center_yaw(0.3) + ) + + +def test_planes_of_equal_center_but_different_strength_are_not_axisymmetric(): + """Lift added to the pitch plane alone, at the aerodynamic center, leaves + the two centers of pressure equal but the pitch plane stiffer.""" + area, diameter = math.pi * 0.0635**2, 2 * 0.0635 + both = LinearGenericSurface(area, diameter, {"cN_alpha": 4.0, "cY_beta": -4.0}) + pitch_only = LinearGenericSurface(area, diameter, {"cN_alpha": 2.0}) + rocket = Rocket( + radius=0.0635, + mass=14.426, + inertia=(6.321, 6.321, 0.034), + power_off_drag=0.5, + power_on_drag=0.5, + center_of_mass_without_motor=0, + ) + rocket.add_surfaces([both, pitch_only], [-1.0, -1.0]) + with pytest.warns(UserWarning, match="strength"): + assert not rocket.is_axisymmetric + assert rocket.aerodynamic_center(0.3) == pytest.approx( + rocket.aerodynamic_center_yaw(0.3) + ) + + +@pytest.mark.parametrize( + "angular_positions", [(0, 90, 180, 270), (30, 150, 270), (45, 135, 225, 315)] +) +def test_evenly_spaced_individual_fins_are_axisymmetric(angular_positions): + """Three or more evenly spaced fins, canted or not, behave the same in + every plane, whatever their orientation about the axis.""" + rocket = _rocket_of( + *((_fin(angle, cant_angle=1.5), -1.04956) for angle in angular_positions) + ) + with warnings.catch_warnings(): + warnings.simplefilter("error") + assert rocket.is_axisymmetric + + +# Both planes are read at one state (alpha, beta) + + +def _body_lift_rocket(): + """Calisto-like rocket plus a Galejs body-lift term against the total + angle of attack: axisymmetric, nonlinear in the angle.""" + area, diameter = math.pi * 0.0635**2, 2 * 0.0635 + body_lift = GenericSurface( + area, + diameter, + { + "cN": lambda alpha_total: ( + 12.0 * math.sin(alpha_total) ** 2 * math.cos(alpha_total) + ) + }, + ) + return _rocket_with(body_lift, 0.3) + + +def test_center_of_pressure_of_a_linear_rocket_is_the_aerodynamic_center( + calisto_robust, +): + """For a rocket built from nose, fins and tail the point where the force + acts does not move with the angle: it is ``cp_position`` at every angle, + in both planes, and at zero angle where the force itself vanishes.""" + rocket = calisto_robust + for mach in (0.0, 0.3, 0.9): + expected = rocket.cp_position.get_value_opt(mach) + for degrees in (0.0, 2.0, 10.0): + angle = math.radians(degrees) + assert rocket.center_of_pressure(angle, mach) == pytest.approx(expected) + assert rocket.center_of_pressure_yaw(angle, mach) == pytest.approx(expected) + + +def test_center_of_pressure_of_a_nonlinear_rocket_moves_with_the_angle(): + """With a body-lift term the center of pressure ``Cm/CN`` leaves the + aerodynamic center as the angle grows, differs from the in-plane neutral + point, and is the across-the-wind neutral point. Turning the wind into + the yaw plane gives the same point from the yaw accessor.""" + rocket = _body_lift_rocket() + theta, mach = math.radians(10), 0.3 + + center = rocket.center_of_pressure(theta, mach) + assert center == pytest.approx( + rocket.neutral_point_yaw(0.0, mach, alpha=theta), abs=1e-5 + ) + assert abs(center - rocket.neutral_point(theta, mach)) > 0.05 + assert abs(center - rocket.aerodynamic_center.get_value_opt(mach)) > 0.05 + assert rocket.center_of_pressure_yaw(theta, mach) == pytest.approx(center) + # Toward zero angle it returns to the aerodynamic center + assert rocket.center_of_pressure(0.0, mach) == pytest.approx( + rocket.aerodynamic_center.get_value_opt(mach) + ) + assert rocket.center_of_pressure(1e-4, mach) == pytest.approx( + rocket.aerodynamic_center.get_value_opt(mach), abs=1e-3 + ) + + +def test_axisymmetric_nonlinear_yaw_is_read_across_the_wind(): + """With the wind in the pitch plane, state ``(theta, 0)``, the pitch plane + of an axisymmetric rocket nonlinear in the angle gives the in-plane + (tangent) neutral point and the yaw plane the across-the-wind one, which + is the classic center of pressure ``Cm/CN`` at that angle with a restoring + slope ``CN / tan(theta)``. The two margins differ.""" + rocket = _body_lift_rocket() + assert rocket.is_axisymmetric and not rocket.is_incidence_linear + theta, mach = math.radians(10), 0.3 + diameter = 2 * rocket.radius + + r1, r2, _, m1, _, _ = summed_force_and_moment(rocket, theta, 0.0, mach, (0, 0, 0)) + dynamic_pressure_area = 0.5 * rocket.area + cN = -r2 / dynamic_pressure_area + cm = m1 / (dynamic_pressure_area * diameter) + secant_point = rocket.center_of_dry_mass_position + rocket._csys * diameter * ( + cm / cN + ) + + across = rocket.neutral_point_yaw(0.0, mach, alpha=theta) + in_plane = rocket.neutral_point(theta, mach) + assert across == pytest.approx(secant_point, abs=1e-5) + assert abs(in_plane - secant_point) > 0.05 + + pitch_margin, pitch_slope = stability_margin_and_slope( + rocket, theta, 0.0, mach, 0.0, "pitch" + ) + yaw_margin, yaw_slope = stability_margin_and_slope( + rocket, theta, 0.0, mach, 0.0, "yaw" + ) + assert yaw_slope == pytest.approx(cN / math.tan(theta), rel=1e-5) + assert pitch_slope > yaw_slope + assert yaw_margin > pitch_margin + 0.5 + + # Rotating the wind into the yaw plane swaps the roles + assert rocket.neutral_point_yaw(theta, mach) == pytest.approx(in_plane) + assert rocket.neutral_point(0.0, mach, beta=theta) == pytest.approx(across) + + +def test_rocket_level_margins_are_read_at_zero_angle(): + """``stability_margin(mach, time)`` and ``stability_margin_yaw(mach, time)`` + keep their two-argument form and read each plane at zero angle; by symmetry + the two agree for an axisymmetric rocket, at zero angle and away from it.""" + rocket = _body_lift_rocket() + at_zero = stability_margin_and_slope(rocket, 0.0, 0.0, 0.3, 0.0)[0] + assert rocket.stability_margin(0.3, 0.0) == pytest.approx(at_zero, rel=1e-9) + assert rocket.stability_margin_yaw(0.3, 0.0) == pytest.approx(at_zero, rel=1e-6) + for angle in (0.1, 0.2): + assert stability_margin_and_slope(rocket, angle, 0.0, 0.3, 0.0, "pitch")[ + 0 + ] == pytest.approx( + stability_margin_and_slope(rocket, 0.0, angle, 0.3, 0.0, "yaw")[0], + rel=1e-6, + ) + + +def test_non_axisymmetric_nonlinear_plane_depends_on_the_other_angle(): + """Canards in one plane with a lift curve that stalls: the pitch-plane + margin read at the rocket's actual state ``(alpha, beta)`` is what a + direct linearization there gives, not the value at ``(alpha, 0)``.""" + area, diameter = math.pi * 0.0635**2, 2 * 0.0635 + limit = math.radians(12) + table = [ + [angle, 2.0 * max(-limit, min(limit, angle))] + for angle in np.radians(np.arange(-30, 31, 1.0)) + ] + canards = GenericSurface(area, diameter, {"cN": (table, ["alpha"]), "cY": 0}) + rocket = _rocket_with(canards, 0.8) + assert not rocket.is_axisymmetric and not rocket.is_incidence_linear + alpha, beta, mach = math.radians(8), math.radians(8), 0.3 + + margin, slope = stability_margin_and_slope(rocket, alpha, beta, mach, 0.0, "pitch") + point, direct_slope = neutral_point_and_slope(rocket, alpha, beta, mach, "pitch") + expected = ( + rocket._csys * (rocket.center_of_mass.get_value_opt(0.0) - point) / diameter + ) + assert margin == pytest.approx(expected) + assert slope == pytest.approx(direct_slope) + # Past the stall the canards stop adding lift and the margin grows + below = stability_margin_and_slope(rocket, 0.0, 0.0, mach, 0.0, "pitch")[0] + beyond = stability_margin_and_slope( + rocket, math.radians(15), 0.0, mach, 0.0, "pitch" + )[0] + assert beyond > below + 0.5 + + +def test_closed_form_damping_follows_the_stability_phase(): + """A surface active only in the other motor phase is left out of the + closed-form damping sum, as it is out of the aerodynamic center.""" + rocket = _phase_gated_rocket() + off_only = aerodynamic_damping(rocket, 0.0, 0.0, 0.3, 0.0, "pitch") + rocket.stability_phase = "power_on" + on_only = aerodynamic_damping(rocket, 0.0, 0.0, 0.3, 0.0, "pitch") + rocket.stability_phase = "power_off" + # The two surfaces have different centers of pressure, so different arms + assert off_only != pytest.approx(on_only) + for phase, expected in (("power_off", off_only), ("power_on", on_only)): + rocket.stability_phase = phase + surfaces = stability_surfaces(rocket) + arm_sum = sum( + surface.reference_area + / rocket.area + * surface.cN_alpha.get_value_opt(0, 0, 0.3, 0, 0, 0, 0) + * ( + position.z + + rocket._csys * surface.aerodynamic_center.get_value_opt(0.3) + - rocket.center_of_mass.get_value_opt(0.0) + ) + ** 2 + for surface, position in surfaces + ) + assert expected == pytest.approx(0.5 * rocket.area * arm_sum) + + +def _oscillator_rocket(): + """A rocket of built-in surfaces with a generic surface that adds nothing.""" + nothing = LinearGenericSurface(math.pi * 0.0635**2, 0.127, {}) + return _rocket_with(nothing, 0.5) + + +def test_disturbance_response_is_the_damped_oscillation_of_the_rocket(): + """The response starts at the disturbance with no rate, and follows + ``I theta'' + C2 theta' + C1 theta = 0`` with the rocket's coefficients at + the chosen condition.""" + rocket = _oscillator_rocket() + speed, density = 40.0, 1.1 + response = rocket.disturbance_response(speed, disturbance=4.0, density=density) + + inertia, inertia_rate = lateral_inertia_and_rate(rocket, rocket.I_11, 0.0) + corrective, damping = corrective_and_damping_moments( + rocket, + 0.0, + 0.0, + speed / 340.29, + 0.0, + speed, + density, + 0.5 * density * speed**2, + inertia_rate, + ) + natural_frequency = math.sqrt(corrective / inertia) + damping_ratio = damping / (2 * math.sqrt(corrective * inertia)) + assert 0 < damping_ratio < 1 + + time = response.x_array + damped_frequency = natural_frequency * math.sqrt(1 - damping_ratio**2) + decay = damping_ratio * natural_frequency + expected = ( + 4.0 + * np.exp(-decay * time) + * ( + np.cos(damped_frequency * time) + + decay / damped_frequency * np.sin(damped_frequency * time) + ) + ) + assert response.y_array == pytest.approx(expected, abs=1e-9) + assert response.y_array[0] == pytest.approx(4.0) + assert abs(response.y_array[-1]) < 0.1 * 4.0 # it settles + assert f"{natural_frequency:.2f} rad/s" in response.title + + +def test_disturbance_response_grows_without_a_restoring_moment(): + """A rocket with its center of pressure ahead of its center of mass does + not swing back: the angle grows.""" + rocket = Rocket( + radius=0.0635, + mass=14.426, + inertia=(6.321, 6.321, 0.034), + power_off_drag=0.5, + power_on_drag=0.5, + center_of_mass_without_motor=0, + coordinate_system_orientation="tail_to_nose", + ) + rocket.add_nose(length=0.55829, kind="vonkarman", position=1.278) + response = rocket.disturbance_response(speed=30) + assert response.y_array[-1] > 3 * response.y_array[0] + assert "no restoring moment" in response.title + + +def test_disturbance_response_of_a_point_mass_rocket_is_refused(): + rocket = PointMassRocket( + radius=0.05, + mass=5, + center_of_mass_without_motor=0, + power_off_drag=0.4, + power_on_drag=0.4, + ) + with pytest.raises(ValueError, match="point mass"): + rocket.disturbance_response(speed=30) diff --git a/tests/unit/simulation/test_aerodynamic_drag_force.py b/tests/unit/simulation/test_aerodynamic_drag_force.py new file mode 100644 index 000000000..ba65f514b --- /dev/null +++ b/tests/unit/simulation/test_aerodynamic_drag_force.py @@ -0,0 +1,204 @@ +"""Tests of the rocket drag helpers. They use a stand-in for the flight, so no +simulation is run.""" + +from types import SimpleNamespace + +import numpy as np +import pytest + +from rocketpy import Rocket +from rocketpy.mathutils import Vector +from rocketpy.simulation.helpers.flight_derivatives import _compute_drag_area + + +def _flight_stand_in(power_off_drag, power_on_drag, burn_out_time=3.0): + """The part of a flight the drag helper reads: the rocket and its motor's + burn out time.""" + rocket = Rocket( + radius=0.05, + mass=10, + inertia=(5, 5, 0.02), + power_off_drag=power_off_drag, + power_on_drag=power_on_drag, + center_of_mass_without_motor=0, + ) + rocket.motor = SimpleNamespace(burn_out_time=burn_out_time) + return SimpleNamespace(rocket=rocket) + + +def _head_on(speed): + """Air coming straight down the rocket's axis, in the body frame.""" + return Vector([0, 0, -speed]) + + +def test_rocket_drag_receives_reduced_rates(): + """The rocket drag must receive the same non-dimensional rates as a generic + surface, ``rate * diameter / (2 * airspeed)``, not the rates in rad/s.""" + flight = _flight_stand_in( + power_off_drag=lambda pitch_rate, yaw_rate, roll_rate: ( + pitch_rate + 10 * yaw_rate + 100 * roll_rate + ), + power_on_drag=0.0, + ) + speed, omega = 50.0, (0.2, -0.4, 8.0) + reduced = 2 * 0.05 / (2 * speed) + expected_coefficient = reduced * (omega[0] + 10 * omega[1] + 100 * omega[2]) + + drag_area = _compute_drag_area( + flight, 5.0, _head_on(speed), speed, 0.15, 1.2, 0, omega + ) + + assert drag_area == pytest.approx(flight.rocket.area * expected_coefficient) + + +def test_rocket_drag_rates_are_zero_without_airspeed(): + """With no airspeed the reduced rates are zero, and nothing divides by it.""" + flight = _flight_stand_in( + power_off_drag=lambda mach, roll_rate: 0.5 + roll_rate, power_on_drag=0.0 + ) + drag_area = _compute_drag_area( + flight, 5.0, _head_on(0.0), 0.0, 0, 1.2, 0, (0, 0, 8.0) + ) + assert drag_area == pytest.approx(0.5 * flight.rocket.area) + + +def test_rocket_drag_reads_the_flow_from_the_stream(): + """The drag coefficients get the angle of attack and the sideslip angle read + from the velocity of the air in the body frame, and the Reynolds number on + the rocket diameter.""" + flight = _flight_stand_in( + power_off_drag=lambda alpha, beta, mach, reynolds: ( + alpha + 10 * beta + 100 * mach + reynolds / 1e6 + ), + power_on_drag=0.0, + ) + alpha, beta, speed = np.radians(4.0), np.radians(-7.0), 80.0 + direction = np.array([np.tan(beta), np.tan(alpha), 1.0]) + stream = Vector(list(-speed * direction / np.linalg.norm(direction))) + density, viscosity = 1.1, 1.8e-5 + reynolds = density * speed * (2 * 0.05) / viscosity + + drag_area = _compute_drag_area( + flight, 5.0, stream, speed, 0.25, density, viscosity, (0, 0, 0) + ) + + expected = alpha + 10 * beta + 100 * 0.25 + reynolds / 1e6 + assert drag_area == pytest.approx(flight.rocket.area * expected) + + +def test_rocket_drag_curve_follows_the_motor_burn(): + """The power-on curve is used until burn out, the power-off curve after.""" + flight = _flight_stand_in(power_off_drag=0.6, power_on_drag=0.4) + before = _compute_drag_area( + flight, 1.0, _head_on(50.0), 50.0, 0.15, 1.2, 0, (0, 0, 0) + ) + after = _compute_drag_area( + flight, 5.0, _head_on(50.0), 50.0, 0.15, 1.2, 0, (0, 0, 0) + ) + assert before == pytest.approx(0.4 * flight.rocket.area) + assert after == pytest.approx(0.6 * flight.rocket.area) + + +@pytest.mark.parametrize("equations", ["u_dot", "u_dot_generalized"]) +@pytest.mark.parametrize("angle_deg", [0.0, 2.0, 10.0, 60.0, 90.0, 180.0]) +def test_axial_drag_follows_the_air_moving_along_the_axis(angle_deg, equations): + """Flying straight into the air the axial drag is the usual + ``0.5 * rho * V**2 * Cd * A`` toward the tail. It fades to zero with the + rocket sideways to the air, and pushes toward the nose (against the motion) + when the rocket moves tail first. Checked on both 6-DOF equations of motion, + with a stand-in for the flight, so no simulation is run.""" + # pylint: disable=import-outside-toplevel + from rocketpy import Environment, SolidMotor + from rocketpy.simulation.helpers import flight_derivatives + + env = Environment() + env.set_atmospheric_model(type="standard_atmosphere") + rocket = Rocket( + radius=0.05, + mass=10, + inertia=(5, 5, 0.02), + power_off_drag=0.5, + power_on_drag=0.5, + center_of_mass_without_motor=0, + ) + rocket.add_motor( + SolidMotor( + thrust_source=1000, + burn_time=2.0, + dry_mass=1.0, + dry_inertia=(0.1, 0.1, 0.002), + center_of_dry_mass_position=0.3, + nozzle_radius=0.03, + grain_number=1, + grain_density=1800, + grain_outer_radius=0.03, + grain_initial_inner_radius=0.015, + grain_initial_height=0.1, + grain_separation=0.0, + grains_center_of_mass_position=0.3, + nozzle_position=0, + throat_radius=0.01, + ), + position=-0.5, + ) + flight = SimpleNamespace(env=env, rocket=rocket) # all the derivative reads + speed, angle = 80.0, np.radians(angle_deg) + upright = [1.0, 0.0, 0.0, 0.0] # the body axis is the inertial z axis + velocity = [speed * np.sin(angle), 0.0, speed * np.cos(angle)] + state = [0.0, 0.0, 1000.0, *velocity, *upright, 0.0, 0.0, 0.0] + + # After burn out, with no aerodynamic surface, the axial force is the drag + derivative = getattr(flight_derivatives, equations) + axial_force = derivative(flight, 10.0, state, post_processing=True)[8] + + rho = env.density.get_value_opt(1000.0) + full = 0.5 * rho * speed**2 * 0.5 * rocket.area + assert axial_force == pytest.approx(-full * np.cos(angle), abs=1e-9) + + +@pytest.mark.parametrize( + "velocity", + [ + (0.0, 0.0, 80.0), # climbing straight up + (30.0, 0.0, 0.5), # near apogee, moving sideways + (5.0, -3.0, -50.0), # falling with a drift + ], +) +def test_3dof_drag_acts_against_the_velocity(velocity): + """A 3-DOF phase does not model the rocket's attitude (by default it stays at + the launch direction), so the drag must act against the velocity relative to + the air, not along the body axis. Falling, it must slow the rocket down.""" + # pylint: disable=import-outside-toplevel + from rocketpy import Environment, PointMassMotor, PointMassRocket + from rocketpy.simulation.helpers.flight_derivatives import u_dot_generalized_3dof + + env = Environment() + env.set_atmospheric_model(type="standard_atmosphere") + rocket = PointMassRocket( + radius=0.05, + mass=10, + center_of_mass_without_motor=0, + power_off_drag=0.5, + power_on_drag=0.5, + ) + rocket.add_motor( + PointMassMotor( + thrust_source=1000, + dry_mass=1.0, + propellant_initial_mass=1.0, + burn_time=2.0, + ), + position=0, + ) + flight = SimpleNamespace(env=env, rocket=rocket) # all the derivative reads + upright = [1.0, 0.0, 0.0, 0.0] + state = [0.0, 0.0, 1000.0, *velocity, *upright, 0.0, 0.0, 0.0] + + drag = np.array( + u_dot_generalized_3dof(flight, 10.0, state, post_processing=True)[6:9] + ) + + speed = np.linalg.norm(velocity) + rho = env.density.get_value_opt(1000.0) + expected = -0.5 * rho * speed * 0.5 * rocket.area * np.array(velocity) + assert drag == pytest.approx(expected, rel=1e-9, abs=1e-12) diff --git a/tests/unit/simulation/test_event.py b/tests/unit/simulation/test_event.py index 73806b645..3572d1225 100644 --- a/tests/unit/simulation/test_event.py +++ b/tests/unit/simulation/test_event.py @@ -831,7 +831,9 @@ def test_sample_on_a_step_boundary_is_checked_exactly_once(): genuinely coincides with a check. """ # imported here only to keep Flight out of this module's import graph - from rocketpy.simulation.flight import Flight # pylint: disable=import-outside-toplevel + from rocketpy.simulation.flight import ( + Flight, # pylint: disable=import-outside-toplevel + ) checked = [] diff --git a/tests/unit/simulation/test_flight.py b/tests/unit/simulation/test_flight.py index f23578486..3ea7472d9 100644 --- a/tests/unit/simulation/test_flight.py +++ b/tests/unit/simulation/test_flight.py @@ -1,5 +1,6 @@ import json import os +from types import SimpleNamespace from unittest.mock import patch import matplotlib as plt @@ -8,6 +9,7 @@ from scipy import optimize from rocketpy import Components, Flight, Function, LinearGenericSurface, Rocket +from rocketpy.rocket._helpers import aerodynamic_damping from rocketpy.simulation.helpers.flight_derivatives import u_dot, u_dot_generalized plt.rcParams.update({"figure.max_open_warning": 0}) @@ -132,17 +134,19 @@ def test_get_solution_at_time(flight_calisto): rtol=1e-05, atol=1e-08, ) + # This rocket has no aerodynamic surfaces, so nothing turns it: it keeps its + # launch attitude and falls tail first, with the drag braking the fall. assert np.allclose( flight_calisto.get_solution_at_time(flight_calisto.t_final), np.array( [ - 48.43719482805657, - -14.836008075478597, - 985.9858934483618, - -3.4415459237894554e-05, - 0.0007572309307800201, - 11.21695000766671, - -341.1460775169661, + 52.61962020640597, + -15.5982732, + 1165.22136, + -2.68709202e-06, + 0.000755621118, + 27.5202515, + -195.691415, 0.9990482215818578, -0.043619387365336, 0.0, @@ -242,7 +246,7 @@ def test_export_sensor_data(flight_calisto_with_sensors): [ ("t_initial", (0.25886, -0.649623, 0)), ("out_of_rail_time", (0.792028, -1.987634, 0)), - ("apogee_time", (-0.509420, -0.732933, -2.089120e-14)), + ("apogee_time", (-0.652631, -0.734179, 2.701482e-16)), ("t_final", (0, 0, 0)), ], ) @@ -279,9 +283,9 @@ def test_aerodynamic_moments(flight_calisto_custom_wind, flight_time, expected_v @pytest.mark.parametrize( "flight_time, expected_values", [ - ("t_initial", (1.654150, 0.659142, -0.067103)), - ("out_of_rail_time", (5.052628, 2.013361, -1.75370)), - ("apogee_time", (2.321838, -1.613641, -0.962108)), + ("t_initial", (1.654150, 0.659142, 0.002172)), + ("out_of_rail_time", (5.052628, 2.013361, -1.716290)), + ("apogee_time", (2.266519, -2.014795, -0.792216)), ("t_final", (-0.019802, 0.012030, 159.051604)), ], ) @@ -322,7 +326,7 @@ def test_aerodynamic_forces(flight_calisto_custom_wind, flight_time, expected_va ("out_of_rail_time", (0, 2.248540, 25.700928)), ( "apogee_time", - (-14.826350, 15.670022, -0.000264), + (-11.635005, 16.697127, -0.000000), ), ("t_final", (5, 2, -5.660155)), ], @@ -361,7 +365,7 @@ def test_velocities(flight_calisto_custom_wind, flight_time, expected_values): [ ("t_initial", (0, 0, 0)), ("out_of_rail_time", (0, 7.8067, 89.2315)), - ("apogee_time", (0.07649, -0.053530, -9.620037)), + ("apogee_time", (0.072063, -0.061586, -9.613866)), ("t_final", (0, 0, 0.0019548)), ], ) @@ -832,3 +836,262 @@ def test_solid_propulsion_and_generalized_equations_agree_when_rotating( generalized = u_dot_generalized(flight, t, pitching) assert solid[10] == pytest.approx(generalized[10], rel=0.1) assert solid[3:6] == pytest.approx(generalized[3:6], rel=0.05) + + +def test_static_margin_yaw_is_the_one_of_the_rocket(): + """The flight gives both planes of the static margin, as it does for the + stability margin. No simulation is needed to check it.""" + rocket = SimpleNamespace(static_margin="pitch", static_margin_yaw="yaw") + flight = SimpleNamespace(rocket=rocket) + assert Flight.static_margin.func(flight) == "pitch" + assert Flight.static_margin_yaw.func(flight) == "yaw" + + +def test_yaw_stability_margin_values_follow_the_yaw_curve(): + """The single yaw-plane margins (initial, at rail exit, largest, smallest) + are read from ``stability_margin_yaw``, as the pitch ones are read from + ``stability_margin``. No simulation is needed to check it.""" + curve = Function([[0.0, 2.0], [1.0, 3.5], [2.0, 1.2], [3.0, 2.4]], "t", "margin") + flight = SimpleNamespace( + stability_margin_yaw=curve, time=[0.0, 1.0, 2.0, 3.0], out_of_rail_time=1.0 + ) + assert Flight.initial_stability_margin_yaw.fget(flight) == pytest.approx(2.0) + assert Flight.out_of_rail_stability_margin_yaw.fget(flight) == pytest.approx(3.5) + + flight.max_stability_margin_yaw_time = Flight.max_stability_margin_yaw_time.func( + flight + ) + flight.min_stability_margin_yaw_time = Flight.min_stability_margin_yaw_time.func( + flight + ) + assert flight.max_stability_margin_yaw_time == pytest.approx(1.0) + assert flight.min_stability_margin_yaw_time == pytest.approx(2.0) + assert Flight.max_stability_margin_yaw.func(flight) == pytest.approx(3.5) + assert Flight.min_stability_margin_yaw.func(flight) == pytest.approx(1.2) + + +# Moments about the center of mass while the motor burns + + +def _rigid_burning_calisto( + calisto_motorless, calisto_nose_cone, calisto_tail, calisto_trapezoidal_fins +): + """Calisto with a full grain whose motor gives a milli-newton of thrust and + a negligible mass flow: a rigid body whose center of mass sits well behind + the center of dry mass.""" + from rocketpy import SolidMotor # pylint: disable=import-outside-toplevel + + motor = SolidMotor( + thrust_source=1e-3, + burn_time=1000.0, + dry_mass=1.815, + dry_inertia=(0.125, 0.125, 0.002), + nozzle_radius=33 / 1000, + grain_number=5, + grain_density=1815, + grain_outer_radius=33 / 1000, + grain_initial_inner_radius=15 / 1000, + grain_initial_height=120 / 1000, + grain_separation=5 / 1000, + grains_center_of_mass_position=0.397, + center_of_dry_mass_position=0.317, + nozzle_position=0, + throat_radius=11 / 1000, + coordinate_system_orientation="nozzle_to_combustion_chamber", + ) + rocket = calisto_motorless + rocket.add_motor(motor, position=-1.255) + rocket.add_surfaces(calisto_nose_cone, 1.160) + rocket.add_surfaces(calisto_trapezoidal_fins, -1.168) + rocket.add_surfaces(calisto_tail, -1.313) + return rocket + + +def _center_of_mass_inertia(rocket, t): + """Position of the center of mass relative to the center of dry mass in + the body frame, and the inertia tensor about it.""" + from rocketpy.mathutils.vector_matrix import Vector # pylint: disable=import-outside-toplevel + + r_cm = Vector([0, 0, -rocket.com_to_cdm_function.get_value_opt(t)]) + mass = rocket.total_mass.get_value_opt(t) + inertia = ( + rocket.get_inertia_tensor_at_time(t) + - (r_cm.cross_matrix @ -r_cm.cross_matrix) * mass + ) + return r_cm, inertia + + +@pytest.mark.parametrize( + "derivative, tolerance", [(u_dot_generalized, 1e-6), (u_dot, 0.02)] +) +def test_dynamics_take_moments_about_the_center_of_mass( + calisto_motorless, + calisto_nose_cone, + calisto_tail, + calisto_trapezoidal_fins, + example_plain_env, + derivative, + tolerance, +): + """With the center of mass behind the center of dry mass, a lateral force + turns the rocket about the center of mass: ``I_cm w_dot`` must equal the + aerodynamic moment about the center of dry mass transferred there, + ``M + R x r_cm``, not the untransferred moment (legacy) nor the transfer + the other way (generalized, before the fix). + """ + from rocketpy.mathutils.vector_matrix import Vector # pylint: disable=import-outside-toplevel + + rocket = _rigid_burning_calisto( + calisto_motorless, calisto_nose_cone, calisto_tail, calisto_trapezoidal_fins + ) + flight = Flight( + rocket=rocket, + environment=example_plain_env, + rail_length=5.2, + inclination=90, + heading=0, + max_time=0.05, + ) + t = 1.0 + r_cm, inertia_cm = _center_of_mass_inertia(rocket, t) + assert r_cm[2] < -0.1 # the full grain pulls the center of mass aft + + # Vertical, 100 m/s up with 10 m/s sideways: sideslip, no rotation + state = [0, 0, 1500, 10.0, 0, 100.0, 1, 0, 0, 0, 0, 0, 0] + out = derivative(flight, t, state, post_processing=True) + w_dot, forces, moments = Vector(out[3:6]), Vector(out[6:9]), Vector(out[9:12]) + assert abs(moments[1]) > 10 # the fins push back + about_center_of_mass = moments + (forces ^ r_cm) + assert list(inertia_cm @ w_dot) == pytest.approx( + list(about_center_of_mass), rel=tolerance, abs=1e-9 + ) + assert abs(about_center_of_mass[1]) < 0.8 * abs(moments[1]) + + +def test_generalized_dynamics_match_a_rigid_body_when_rotating( + calisto_motorless, + calisto_nose_cone, + calisto_tail, + calisto_trapezoidal_fins, + example_plain_env, +): + """Rotating about all three axes with the center of mass off the center of + dry mass, the angular acceleration is that of a rigid body about its + center of mass, ``I_cm w_dot = M_cm - w x (I_cm w)``, and the acceleration + of the center of dry mass follows from the center of mass' one, + ``a_O = F / m - w_dot x r_cm - w x (w x r_cm)``.""" + from rocketpy.mathutils.vector_matrix import Vector # pylint: disable=import-outside-toplevel + + rocket = _rigid_burning_calisto( + calisto_motorless, calisto_nose_cone, calisto_tail, calisto_trapezoidal_fins + ) + flight = Flight( + rocket=rocket, + environment=example_plain_env, + rail_length=5.2, + inclination=90, + heading=0, + max_time=0.05, + ) + t = 1.0 + r_cm, inertia_cm = _center_of_mass_inertia(rocket, t) + mass = rocket.total_mass.get_value_opt(t) + w = Vector([0.4, -0.3, 2.0]) + state = [0, 0, 1500, 10.0, -5.0, 100.0, 1, 0, 0, 0, *w] + out = u_dot_generalized(flight, t, state, post_processing=True) + a_cdm, w_dot = Vector(out[:3]), Vector(out[3:6]) + forces, moments, thrust = Vector(out[6:9]), Vector(out[9:12]), out[12] + + gravity = Vector([0, 0, -mass * example_plain_env.gravity.get_value_opt(1500)]) + total_force = forces + gravity + Vector([0, 0, thrust]) + moment_cm = moments + (forces ^ r_cm) # gravity and thrust give none + expected_w_dot = inertia_cm.inverse @ (moment_cm - (w ^ (inertia_cm @ w))) + # The milli-newton motor still has a whisper of mass flow, hence the slack + assert list(w_dot) == pytest.approx(list(expected_w_dot), rel=2e-3, abs=1e-4) + + # The attitude is the identity, so body and inertial components coincide; + # the integrator also adds the Earth-rotation Coriolis acceleration + earth = Vector(example_plain_env.earth_rotation_vector) + expected_a_cdm = ( + total_force / mass + - (expected_w_dot ^ r_cm) + - (w ^ (w ^ r_cm)) + - 2 * (earth ^ Vector(state[3:6])) + ) + assert list(a_cdm) == pytest.approx(list(expected_a_cdm), rel=1e-3, abs=1e-4) + + +def test_integrator_damping_matches_the_oscillator(flight_calisto_robust): + """During the burn, the moment the equations of motion set against a pitch + rate equals the oscillator's ``C2`` (aerodynamic plus jet damping in + Thomson's form), once the rate is taken about the same point: the state + rotates about the center of dry mass, ``a`` ahead of the center of mass, + which adds ``C1 a / V`` of angle-of-attack coupling.""" + from rocketpy.mathutils.vector_matrix import Vector # pylint: disable=import-outside-toplevel + + flight = flight_calisto_robust + rocket = flight.rocket + # A solution time about one second into the burn + index = int(np.argmin(np.abs(flight.time - 1.0))) + t = flight.time[index] + r_cm, inertia_cm = _center_of_mass_inertia(rocket, t) + speed = 100.0 + state = [0, 0, 1500, 0, 0, speed, 1, 0, 0, 0, 0, 0, 0] + + def pitch_acceleration(rate): + out = u_dot_generalized(flight, t, [*state[:10], rate, 0, 0]) + return (inertia_cm @ Vector(out[10:13]))[0] + + step = 1e-3 + integrator = -(pitch_acceleration(step) - pitch_acceleration(-step)) / (2 * step) + + density = flight.env.density.get_value_opt(1500) + mach = speed / flight.env.speed_of_sound.get_value_opt(1500) + aero = density * speed * aerodynamic_damping(rocket, 0.0, 0.0, mach, t, "pitch") + inertia, inertia_rate = flight._lateral_inertia(rocket.I_11) + jet = ( + abs(rocket.total_mass_flow_rate.get_value_opt(t)) + * (rocket.nozzle_position - rocket.center_of_mass.get_value_opt(t)) ** 2 + + inertia_rate[index] + ) + assert inertia[index] == pytest.approx(inertia_cm[0][0], rel=1e-6) + assert ( + 0 + < jet + < 0.7 + * abs(rocket.total_mass_flow_rate.get_value_opt(t)) + * (rocket.nozzle_position - rocket.center_of_mass.get_value_opt(t)) ** 2 + ) + # Rotating about the center of dry mass gives every surface an extra + # angle of attack a w / V, felt through the restoring moment C1 + slope = rocket.total_lift_coeff_der.get_value_opt(mach) + margin = rocket.stability_margin.get_value_opt(mach, t) + dynamic_pressure = 0.5 * density * speed**2 + corrective = dynamic_pressure * rocket.area * slope * margin * 2 * rocket.radius + coupling = corrective * abs(r_cm[2]) / speed + assert integrator == pytest.approx(aero + jet + coupling, rel=0.01) + + +def test_disturbance_response_matches_the_rocket_at_the_flight_condition( + flight_calisto_robust, +): + """The response at an instant of the flight is the rocket's own response at + that instant's airspeed, density and Mach number; on the rail there is + none.""" + flight = flight_calisto_robust + t = 4.0 # just after burnout + response = flight.disturbance_response(t) + speed = flight.free_stream_speed.get_value_opt(t) + expected = flight.rocket.disturbance_response( + speed, + time=t, + density=flight.density.get_value_opt(t), + speed_of_sound=speed / flight.mach_number.get_value_opt(t), + duration=response.x_array[-1], + ) + assert response.y_array[0] == pytest.approx(5.0) + # The flight reads the coefficients at its own angle of attack and between + # its time steps, so the two agree closely, not exactly + assert response.y_array == pytest.approx(expected.y_array, abs=0.05) + with pytest.raises(ValueError, match="rail"): + flight.disturbance_response(0.0) diff --git a/tests/unit/stochastic/test_stochastic_rocket.py b/tests/unit/stochastic/test_stochastic_rocket.py index 8306b6039..f900459a1 100644 --- a/tests/unit/stochastic/test_stochastic_rocket.py +++ b/tests/unit/stochastic/test_stochastic_rocket.py @@ -1,3 +1,5 @@ +import pytest + from rocketpy.rocket.rocket import Rocket @@ -23,3 +25,91 @@ class creates a StochasticCalisto object from the randomly generated """ obj = stochastic_calisto.create_object() assert isinstance(obj, Rocket) + + +def test_zero_dispersion_keeps_the_full_drag_and_the_stability_phase( + calisto_robust, cesaroni_m1670 +): + """A drag that depends on more than Mach, the stability phase and a generic + surface on the rocket survive the stochastic mirror unchanged, and the + drag factor scales the whole coefficient.""" + from rocketpy import GenericSurface + from rocketpy.stochastic import StochasticRocket + + rocket = calisto_robust + table = [[a, m, 0.4 + a**2 + 0.1 * m] for a in (-0.3, 0, 0.3) for m in (0, 1, 2)] + rocket.power_off_drag = (table, ["alpha", "mach"]) + rocket.stability_phase = "power_on" + rocket.add_surfaces( + GenericSurface(rocket.area, 2 * rocket.radius, {"cN": lambda alpha: alpha}), 0.5 + ) + state = (0.3, 0, 1.0, 0, 0, 0, 0) + + stochastic = StochasticRocket(rocket=rocket) + stochastic.add_motor(cesaroni_m1670, position=(-1.255, 0)) + created = stochastic.create_object() + assert created.power_off_drag_7d(*state) == rocket.power_off_drag_7d(*state) + assert created.stability_phase == "power_on" + assert any(type(s) is GenericSurface for s, _ in created.aerodynamic_surfaces) + + scaled = StochasticRocket(rocket=rocket, power_off_drag_factor=(1.2, 0)) + scaled.add_motor(cesaroni_m1670, position=(-1.255, 0)) + created = scaled.create_object() + assert created.power_off_drag_7d(*state) == 1.2 * rocket.power_off_drag_7d(*state) + + +def test_surfaces_with_no_stochastic_version_are_carried_over(): + """Free-form fins, individual fins and generic surfaces have no stochastic + version; each generated rocket keeps them, at their position.""" + from rocketpy import ( + FreeFormFins, + GenericSurface, + StochasticNoseCone, + StochasticRocket, + TrapezoidalFin, + ) + + rocket = Rocket( + radius=0.0635, + mass=14.4, + inertia=(6.3, 6.3, 0.034), + power_off_drag=0.5, + power_on_drag=0.5, + center_of_mass_without_motor=0, + coordinate_system_orientation="tail_to_nose", + ) + nose = rocket.add_nose(length=0.5, kind="vonKarman", position=1.2) + rocket.add_surfaces( + FreeFormFins( + n=4, + shape_points=[(0, 0), (0.05, 0.1), (0.12, 0.1), (0.12, 0)], + rocket_radius=0.0635, + ), + -1.0, + ) + for angle in (0, 120, 240): + rocket.add_surfaces( + TrapezoidalFin( + angular_position=angle, + root_chord=0.1, + tip_chord=0.05, + span=0.08, + rocket_radius=0.0635, + ), + -0.9, + ) + rocket.add_surfaces( + GenericSurface(rocket.area, 2 * rocket.radius, {"cN": 0.1}), 0.3 + ) + stochastic = StochasticRocket(rocket) + stochastic.add_nose(StochasticNoseCone(nose), position=(1.2, 0)) + + generated = stochastic.create_object() + + def surfaces(r): + return sorted( + (type(s).__name__, round(float(p.z), 6)) for s, p in r.aerodynamic_surfaces + ) + + assert surfaces(generated) == surfaces(rocket) + assert generated.static_margin(0) == pytest.approx(rocket.static_margin(0)) From 052babdb95d60fac095e8b000c8ad4b7139d9b33 Mon Sep 17 00:00:00 2001 From: MateusStano Date: Sun, 4 Oct 2026 19:39:02 -0300 Subject: [PATCH 14/22] DOC: update stability and generic surface guides - stability guide: simpler center of pressure section, displayed C1 and C2 equations, disturbance response section, parts 6 and 7 removed - generic surface guide: full list of variable names, total angle of attack by variable name, axisymmetric linear surfaces - rocket usage: drag curve at an angle of attack, stability_margin(mach, time) --- docs/notebooks/getting_started.ipynb | 839 +++++++++++++----- .../AeroSurface/AeroSurfacePlots.rst | 2 +- .../AeroSurface/AeroSurfacePrints.rst | 2 +- .../user/center_of_pressure_and_stability.rst | 366 ++++---- .../environment/1-atm-models/forecast.rst | 2 + .../environment/1-atm-models/soundings.rst | 68 -- docs/user/first_simulation.rst | 9 +- docs/user/function.rst | 21 +- docs/user/index.rst | 4 +- docs/user/rocket/generic_surface.rst | 702 ++++++++++++--- docs/user/rocket/rocket_usage.rst | 80 +- 11 files changed, 1507 insertions(+), 588 deletions(-) diff --git a/docs/notebooks/getting_started.ipynb b/docs/notebooks/getting_started.ipynb index 1cfab8f95..745803862 100644 --- a/docs/notebooks/getting_started.ipynb +++ b/docs/notebooks/getting_started.ipynb @@ -207,7 +207,7 @@ "\n", "Launch Site Details\n", "\n", - "Launch Date: 2026-06-15 12:00:00 UTC\n", + "Launch Date: 2026-10-04 12:00:00 UTC\n", "Launch Site Latitude: 32.99025°\n", "Launch Site Longitude: -106.97500°\n", "Reference Datum: SIRGAS2000\n", @@ -220,20 +220,20 @@ "\n", "Atmospheric Model Type: Forecast\n", "Forecast Maximum Height: 5.000 km\n", - "Forecast Time Period: from 2026-06-08 00:00:00 to 2026-06-30 18:00:00 utc\n", + "Forecast Time Period: from 2026-09-26 00:00:00 to 2026-10-19 12:00:00 utc\n", "Forecast Hour Interval: 3 hrs\n", "Forecast Latitude Range: From 90.0° to -90.0°\n", "Forecast Longitude Range: From 0.0° to 359.75°\n", "\n", "Surface Atmospheric Conditions\n", "\n", - "Surface Wind Speed: 2.56 m/s\n", - "Surface Wind Direction: 135.39°\n", - "Surface Wind Heading: 315.39°\n", - "Surface Pressure: 855.76 hPa\n", - "Surface Temperature: 293.31 K\n", - "Surface Air Density: 1.016 kg/m³\n", - "Surface Speed of Sound: 343.33 m/s\n", + "Surface Wind Speed: 2.22 m/s\n", + "Surface Wind Direction: 100.73°\n", + "Surface Wind Heading: 280.73°\n", + "Surface Pressure: 859.39 hPa\n", + "Surface Temperature: 290.71 K\n", + "Surface Air Density: 1.030 kg/m³\n", + "Surface Speed of Sound: 341.80 m/s\n", "\n", "\n", "Earth Model Details\n", @@ -251,18 +251,18 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e8ddd414dc7b4fe79ab1995b4ad89f45", + "model_id": "75db972435644d79b20a626de7e1904c", "version_major": 2, "version_minor": 0 }, - "image/png": 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", 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", 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"model_id": "6f9efdce1a4a4373b6f3120b00b1cf27", + "model_id": "6683a5b87b554c618c879ff9d3415d57", "version_major": 2, "version_minor": 0 }, - "image/png": 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", + "image/png": 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", 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", 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", 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"Geometrical Parameters\n", + "\n", + "Rocket Maximum Radius: 0.0635 m\n", + "Rocket Frontal Area: 0.012668 m2\n", + "\n", + "Rocket Distances\n", + "Rocket Center of Dry Mass - Center of Mass without Motor: 0.105 m\n", + "Rocket Center of Dry Mass - Nozzle Exit: 1.150 m\n", + "Rocket Center of Dry Mass - Center of Propellant Mass: 0.753 m\n", + "Rocket Center of Mass - Rocket Loaded Center of Mass: 0.116 m\n", + "\n", + "\n", + "Aerodynamics Lift Coefficient Derivatives\n", + "\n", + "Nose Cone Lift Coefficient Derivative: 2.000/rad\n", + "Fins Lift Coefficient Derivative: 6.280/rad\n", + "Tail Lift Coefficient Derivative: -1.061/rad\n", + "\n", + "Center of Pressure\n", + "\n", + "Nose Cone Center of Pressure position: 0.999 m\n", + "Fins Center of Pressure position: -1.100 m\n", + "Tail Center of Pressure position: -1.223 m\n", + "\n", + "Stability\n", + "\n", + "Center of Mass position (time=0): -0.221 m\n", + "Center of Pressure position (Mach=0): -0.500 m\n", + "Rocket Length: 2.533 m\n", + "Initial Static Margin (mach=0, time=0): 2.199 c (11.02% of length)\n", + "Final Static Margin (mach=0, time=burn_out): 3.112 c (15.60% of length)\n", + "Rocket Center of Mass (time=0) - Center of Pressure (Mach=0): 0.279 m\n", + "\n", + "\n", + "Parachute Details\n", + "\n", + "Parachute Main with a cd_s of 10.0000 m2\n", + "Ejection signal trigger: 800 m (AGL)\n", + "Ejection system refresh rate: 100.000 Hz\n", + "Time between ejection signal is triggered and the parachute is fully opened: 1.5 s\n", + "\n", + "\n", + "Parachute Details\n", + "\n", + "Parachute Drogue with a cd_s of 1.0000 m2\n", + "Ejection signal trigger: At Apogee\n", + "Ejection system refresh rate: 100.000 Hz\n", + "Time between ejection signal is triggered and the parachute is fully opened: 1.5 s\n", + "\n", + "\n", + "Rocket Drawing\n", + "----------------------------------------\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": 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", 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", 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", 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", 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", 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", 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", 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", 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10.152\n", - "Rail Departure Reynolds Number: 1.862e+05\n", + "Rail Departure Reynolds Number: 1.906e+05\n", "\n", "\n", "Burn out State\n", "\n", "Burn out time: 3.900 s\n", - "Altitude at burn out: 2131.413 m (ASL) | 659.947 m (AGL)\n", - "Rocket speed at burn out: 281.014 m/s\n", - "Freestream velocity at burn out: 280.756 m/s\n", - "Mach Number at burn out: 0.821\n", - "Kinetic energy at burn out: 6.413e+05 J\n", + "Altitude at burn out: 2129.710 m (ASL) | 658.244 m (AGL)\n", + "Rocket speed at burn out: 280.530 m/s\n", + "Freestream velocity at burn out: 280.848 m/s\n", + "Mach Number at burn out: 0.827\n", + "Kinetic energy at burn out: 6.391e+05 J\n", "\n", "\n", "Apogee State\n", "\n", - "Apogee Time: 26.230 s\n", - "Apogee Altitude: 4855.464 m (ASL) | 3383.998 m (AGL)\n", - "Apogee Freestream Speed: 18.698 m/s\n", - "Apogee X position: 144.097 m\n", - "Apogee Y position: 414.834 m\n", - "Apogee Drift: 439.149 m\n", - "Apogee Bearing: 379.155°\n", - "Apogee latitude: 32.9939842°\n", - "Apogee longitude: -106.9734531°\n", + "Apogee Time: 26.059 s\n", + "Apogee Altitude: 4815.712 m (ASL) | 3344.246 m (AGL)\n", + "Apogee Freestream Speed: 25.577 m/s\n", + "Apogee X position: 121.567 m\n", + "Apogee Y position: 559.936 m\n", + "Apogee Drift: 572.981 m\n", + "Apogee Bearing: 372.249°\n", + "Apogee latitude: 32.9952890°\n", + "Apogee longitude: -106.9736947°\n", "\n", "\n", "Parachute Events\n", "\n", "Parachute: Drogue\n", - "\tEjection time: 26.240 s\n", - "\tInflation time: 27.740 s\n", - "\tFreestream speed at inflation: 18.691 m/s\n", - "\tAltitude at inflation: 4855.465 m (ASL) | 3384.000 m (AGL)\n", + "\tEjection time: 26.060 s\n", + "\tInflation time: 27.560 s\n", + "\tFreestream speed at inflation: 29.338 m/s\n", + "\tAltitude at inflation: 4804.850 m (ASL) | 3333.385 m (AGL)\n", "Parachute: Main\n", - "\tEjection time: 161.200 s\n", - "\tInflation time: 162.700 s\n", - "\tFreestream speed at inflation: 18.444 m/s\n", - "\tAltitude at inflation: 2271.369 m (ASL) | 799.903 m (AGL)\n", + "\tEjection time: 158.270 s\n", + "\tInflation time: 159.770 s\n", + "\tFreestream speed at inflation: 18.258 m/s\n", + "\tAltitude at inflation: 2243.987 m (ASL) | 772.521 m (AGL)\n", "\n", "\n", "Impact Conditions\n", "\n", - "Time of impact: 302.190 s\n", - "X impact: 783.041 m\n", - "Y impact: 515.547 m\n", - "Drift: 937.519 m\n", - "Bearing: 416.639°\n", + "Time of impact: 295.560 s\n", + "X impact: -1739.007 m\n", + "Y impact: 328.112 m\n", + "Drift: 1769.690 m\n", + "Bearing: 280.685°\n", "Altitude impact: 1471.466 m (ASL) | -0.000 m (AGL) \n", - "Latitude: 32.9948895°\n", - "Longitude: -106.9666029°\n", - "Vertical velocity at impact: -5.595 m/s\n", + "Latitude: 32.9932030°\n", + "Longitude: -106.9936418°\n", + "Vertical velocity at impact: -5.558 m/s\n", "Number of parachutes triggered until impact: 2\n", "\n", "\n", "Stability Margin\n", "\n", - "Initial Stability Margin: 2.199 c at 0.00 s\n", - "Out of Rail Stability Margin: 2.276 c at 0.37 s\n", - "Maximum Stability Margin: 3.682 c at 3.91 s\n", - "Minimum Stability Margin: 2.199 c at 0.00 s\n", + "Initial Stability Margin: 2.199 c (11.02% of length) at 0.00 s\n", + "Out of Rail Stability Margin: 2.276 c (11.41% of length) at 0.37 s\n", + "Maximum Stability Margin: 3.682 c (18.46% of length) at 3.91 s\n", + "Minimum Stability Margin: 2.199 c (11.02% of length) at 0.00 s\n", + "Out of Rail Stability Margin - yaw: 2.276 c (11.41% of length)\n", + "\n", + "\n", + "Dynamic Stability\n", + "\n", + "Out of Rail (t = 0.37 s): Pitch natural frequency = 0.16 Hz, damping ratio = 0.094\n", + " Yaw natural frequency = 0.16 Hz, damping ratio = 0.094\n", + "Burnout (t = 3.90 s): Pitch natural frequency = 2.44 Hz, damping ratio = 0.059\n", + " Yaw natural frequency = 2.44 Hz, damping ratio = 0.059\n", + "Roll Rate at Burnout: 0.00 Hz\n", "\n", "\n", "Maximum Values\n", "\n", - "Maximum Speed: 286.824 m/s at 3.40 s\n", - "Maximum Mach Number: 0.838 Mach at 3.40 s\n", - "Maximum Reynolds Number: 1.946e+06 at 3.32 s\n", - "Maximum Dynamic Pressure: 3.961e+04 Pa at 3.35 s\n", - "Maximum Total Pressure: 1.278e+05 Pa at 3.30 s\n", - "Maximum Acceleration During Motor Burn: 105.254 m/s² at 0.15 s\n", + "Maximum Speed: 286.515 m/s at 3.38 s\n", + "Maximum Mach Number: 0.843 Mach at 3.40 s\n", + "Maximum Reynolds Number: 1.996e+06 at 3.32 s\n", + "Maximum Dynamic Pressure: 4.028e+04 Pa at 3.34 s\n", + "Maximum Total Pressure: 1.290e+05 Pa at 3.30 s\n", + "Maximum Acceleration During Motor Burn: 105.255 m/s² at 0.15 s\n", "Maximum Gs During Motor Burn: 10.733 g at 0.15 s\n", - "Maximum Acceleration After Motor Burn: 61.576 m/s² at 162.70 s\n", - "Maximum Gs After Motor Burn: 6.279 Gs at 162.70 s\n", + "Maximum Acceleration After Motor Burn: 61.194 m/s² at 159.77 s\n", + "Maximum Gs After Motor Burn: 6.240 Gs at 159.77 s\n", "Maximum Stability Margin: 3.682 c at 3.91 s\n", - "Maximum Aerodynamic Normal Force: 126.083 N at 27.76 s\n", - "Maximum Aerodynamic Axial Force: 211.708 N at 3.36 s\n", - "Maximum Aerodynamic Lift Force: 1488.134 N at 162.70 s\n", - "Maximum Aerodynamic Drag Force: 509.492 N at 162.70 s\n", - "Maximum Upper Rail Button Normal Force: 0.802 N\n", - "Maximum Upper Rail Button Shear Force: 0.785 N\n", - "Maximum Lower Rail Button Normal Force: 1.419 N\n", - "Maximum Lower Rail Button Shear Force: 1.399 N\n", + "Maximum Aerodynamic Normal Force: 270.502 N at 27.57 s\n", + "Maximum Aerodynamic Axial Force: 216.919 N at 3.37 s\n", + "Maximum Aerodynamic Lift Force: 1376.519 N at 159.77 s\n", + "Maximum Aerodynamic Drag Force: 216.921 N at 3.37 s\n", + "Maximum Upper Rail Button Normal Force: 0.181 N\n", + "Maximum Upper Rail Button Shear Force: 0.979 N\n", + "Maximum Lower Rail Button Normal Force: 0.321 N\n", + "Maximum Lower Rail Button Shear Force: 1.731 N\n", "\n", "\n", "\n", @@ -1087,9 +1443,9 @@ "Absolute Error Tolerance: [0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 1e-06, 1e-06, 1e-06, 1e-06, 0.001, 0.001, 0.001]\n", "Allow Event Overshoot: True\n", "Terminate Simulation on Apogee: False\n", - "Number of Time Steps Used: 741\n", - "Number of Derivative Functions Evaluation: 440\n", - "Average Function Evaluations per Time Step: 0.594\n", + "Number of Time Steps Used: 766\n", + "Number of Derivative Functions Evaluation: 403\n", + "Average Function Evaluations per Time Step: 0.526\n", "\n", "\n", "\n", @@ -1100,18 +1456,18 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "27b0c9ed4c6c4e549c054cfde9a9385d", + "model_id": "1a426143094044e19452c4cfd718415e", "version_major": 2, "version_minor": 0 }, - "image/png": 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", + "image/png": 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", + "image/png": 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", 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", 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Consider using `matplotlib.pyplot.close()`.\n", + " plt.figure(figsize=(9, 6))\n" + ] + }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a98281af84a14437bf10b1b09e1a6e1c", + "model_id": "796f0698096d4b84a9a53f41f53f5dc0", "version_major": 2, "version_minor": 0 }, - "image/png": 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", + "image/png": 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", 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", + "image/png": 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", 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", 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", 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", 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", 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", + "image/png": 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", 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", 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", + "image/png": 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Skipping bending moment plots.\n", + "\n", + "Angular Position Plots\n", + "\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "d995add6d9b045bd8c9d8e43f9d76cb6", + "version_major": 2, + "version_minor": 0 + }, + "image/png": 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", + "image/png": 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", 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Skipping bending moment plots.\n", + "\n", + "\n", + "Rail Buttons Forces Plots\n", + "\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "6ea639baf53a4f1ba4ffd57ef28f3578", + "model_id": "991fc15a214f4093aae76bf96735efec", "version_major": 2, "version_minor": 0 }, - "image/png": 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", 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", 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", + "image/png": 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", + "image/png": 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", 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", + "image/png": 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", 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CCHHnzh1haGgopkyZotHu4sWLwsjISD09MzNTODg4iMqVK4uMjAx1u8WLFwsAon79+m/to1KpFDKZTIwaNSrHvOzvy5MnT8RXX30lvL291fOqV68uPv30UyFE7p/v7du3BQBx4MAB9bSlS5cKAMLY2Fg0aNBATJgwQRw+fFhkZWXlmu3YsWMCgFi/fv1b+yCEEL/99psAIFavXi0iIyOFoaGhGD58+DufFxMTIwCIyZMniydPnohHjx6Jw4cPi+rVqwsAYuPGjeq2w4cPFwA0Prvnz58LT09P4eHhoe7HsmXLBAAxa9asHK+nUqk0Xvfnn38WSqVSdOnSRZiamoq9e/eq2+b1OyCEEK1atcp1nX8Td3d30apVK/Hq1Svh6OgofvjhByGEEFeuXBEAxMGDB8Xy5csFAHHy5En183Jb59auXSsAiEOHDqmnWVtbv3WdP3v2bI73N68WLFig8TkcP35cABB3795V5798+bIQQoidO3cKAGLNmjXq57/PdjC7bd++fTWmt2vXThQvXlz9+Ny5cwKA6N+/v0a7r776SgAQ//zzj3ram7Y97u7uGhk2btyYYx16mw9dX+fMmaNed7JlZmaKwMBAYWFhof678uTJkzdmz97OPX36VD3t/PnzwsDAQPTq1StHxm7duuWpT0uWLBEAxMWLF3PMc3d3F87OzsLIyEgEBQWJ1NTUXJexefNmAUDMmTNHPS0rK0s0bNhQABDLly/P8ZymTZuKcuXK5SmjPuMewAL0/PlzAIClpeVb22XPT0lJKfA8hZFl6dKlsLe3h4ODA6pVq4b9+/djzJgxGDlypEa7f//Se/78ORISElC3bl2kpaVpHP74UCVKlMD58+cxYMAAPHv2DIsWLUJISAgcHBzwww8/5Dg0aWRkpLHnwNjYGF988QXi4+Nx+vRpAK8vhChXrhzKli2LhIQE9b/s89wOHDgA4PUJ2yqVCp07d9Zo5+joCB8fH3W7U6dOIT4+HgMGDNA4tNqnTx9YW1u/s4+JiYkQQuTYu/pfISEhuHnzJk6ePKn+77sO/1pbW2vseenbty/27NmDoKAgHDlyBD/88APq1q0LHx8fHDt2LMcysjPl5WKbzz//HM2aNcOQIUPQs2dPeHl5YerUqe98XraJEyfC3t4ejo6OqFu3Lq5evYqZM2eiY8eO6ja7du1CQECARp8sLCzw+eef486dO7hy5QqA13sk7OzsMGTIkByv899TIDIzM9GpUyfs3LkTu3btQtOmTdXz8vod+BiGhobo3Lmz+qKBNWvWwNXV9Y1HHP69zqWnpyMhIUF9isS/D+/a2NjgxIkTePjwYa7Lyf5u7t27N8eh1Hf593mAwOtDvCVLloSbmxvKli0LW1tb9d6b/14A8qH+vXcZeH2KwNOnT9XbuexbFv13GzVq1CgAKPS7GLzP+rpr1y44OjqiW7du6mlyuRxDhw7FixcvcPDgwbe+VlxcHM6dO4c+ffrA1tZWPb1SpUpo0qRJrrdz+u/7+SbZpxa8afuUmJiIV69ewcXFJceev2x79uyBXC7X2NNoYGCAQYMGvfF1ixUrpjMX+RUkFoAFKLuYyi4E3ySvhWJ+5CmMLG3atMG+ffvw559/qs8ZSUtLg4GB5tft8uXLaNeuHaytrWFlZQV7e3v1ifxvO4/ofTg5OSE0NBRxcXGIjo7GvHnzYG9vj++++w5Lly7VaOvs7JzjcGPp0qUB/O+Q4o0bN3D58mXY29tr/Mtul30e240bNyCEgI+PT462V69eVbe7e/cuAMDHx0fjdeVy+XudwPzfYva/qlSpgrJlyyI8PBxr1qyBo6PjWy/O+PPPP9G0adMcJ9s3a9YMe/fuRVJSEg4dOoRBgwbh7t27aN26dY4LQbIz5fW80aVLlyItLQ03btxAWFjYG/8g5Obzzz/Hvn378Mcff2DEiBF4+fJljnNJ7969izJlyuR4bvZhuezP4tatWyhTpkyeLjSYNm0atm3bhk2bNuW4Z2NevwMfKyQkBFeuXMH58+cRHh6Orl27vvE9T0xMxLBhw1CiRAmYmprC3t4enp6eADTXuRkzZuDSpUtwdXVFQEAAJk2ahNu3b6vne3p6YuTIkViyZAns7OzQrFkzLFiwIE/rbYUKFWBjY6NR5NWuXRvA6+9KYGCgxjxXV1e4ubl92Jvz//77/OyC5NmzZwBef/YGBgbw9vbWaOfo6AgbGxv1d6OwvM/6evfuXfj4+OTYvv73e/0m2fPftG4kJCTkOA83+zuTV2/aPjVq1AgDBw7E6tWrMXz48Dfmc3JygpmZmcb0/35W/329DzlfXd/o7qVUWsDa2hpOTk64cOHCW9tduHABJUuWhJWVFYA3/8H87x+091WuXDmcO3cOsbGxb9ygZmf19fX94NdxcXFRn4jbsmVL2NnZYfDgwWjQoIH6nJOkpCTUr18fVlZW+P777+Hl5QUTExOcOXMGX3/9NVQq1Qe/fm5kMhlKly6N0qVLo1WrVvDx8cGaNWvQv3//91qOSqVCxYoVMWvWrFznu7q6qtvJZDLs3r0711uz/Pt+WB/D1tYWMplM/YfsbUJCQhAaGgpLS0t06dIlxx+MbGlpaYiIiFCfM5QbMzMz1K1bF3Xr1oWdnR0mT56M3bt3a5yXk53Jzs4uT32JiIhQnxd28eJFBAYG5ul5wOsCOvs717p1axgaGmLs2LFo0KABqlWrluflvK9mzZphz549mDFjBoKCgjROXi+s70CNGjXg5eWF4cOHIyYm5q17djt37oxjx45h9OjRqFy5MiwsLKBSqdC8eXONda5z586oW7cutm7dir/++gs///wzpk+fji1btqBFixYAgJkzZ6JPnz7Yvn07/vrrLwwdOhTTpk1DZGQkXFxc3pjBwMAAgYGB6nP9jh49qnGPv1q1amHZsmXqcwPbtm370e9Rbu8/kLMw+Zii4WO3z/+V1/VVCnn9cVa8eHEAr7cFb/pO/Prrr3j27BnmzZuHYsWK5csN/p89e5bn7Y4+055vlI5q3bo1YmJi1Ic7/uvw4cO4c+eOxg0xixUrluuJ1Ln9knufDVb2a6xcuTLX+SkpKdi+fTvKli371l9X7+uLL76Al5cXvv32W/UGNyIiAk+fPkVYWBiGDRuG1q1bo3Hjxu88lJkfSpUqhWLFiiEuLk5j+sOHD3P80r1+/ToAqK/K9PLyQmJiIho1aoTGjRvn+Jf9K9rLywtCCHh6eubaLvuwm7u7O4DXe4v+TalUIiYm5p19MTIygpeXV57ahoSEIC4uDtevX39rkfDPP/8gIyND/Yf+XbILrP++n9mZ8nLie1xcHIYMGYKmTZuidevW+Oqrrz5qr8v48eNhaWmJb7/9Vj3N3d091/tJZp9ukP1ZeHl5ITo6Gkql8p2vU7NmTWzbtg3Hjh1Dp06d8OrVK/W8vH4HgI8rPIDX90GMiIhAuXLlULly5VzbPHv2DPv378fYsWMxefJktGvXDk2aNHnjnmYnJyd8+eWX2LZtG2JiYlC8eHFMmTJFo03FihXx7bff4tChQzh8+DAePHiQ65Xz/1WnTh0kJiZix44diI+PV+8BBF4XgLdu3cKuXbvw8uXLPB3+/dj3z93dHSqVKsd6+PjxYyQlJam/G0Du2+fMzMwc3/+PzZTX9dXd3R03btzI8aP5v9/rN+XJnv+mdcPOzu6DLsQCoL5n7Nu2TwYGBli5ciVatGiByZMnY968eTnyxcXF5TjV4ObNm29cZkxMTJ62O/qOBWABGz16NExNTfHFF1/kuNVCYmIiBgwYADMzM40bMHt5eSE5OVljz2FcXFyuN4w2Nzd/51V32Tp27AhfX1/89NNPOW62qlKpMHDgQDx79gwTJ058jx6+m5GREUaNGoWrV6+qL83P/kX+71/gmZmZ+XrPthMnTuR6C5GoqCg8ffo0xyGPV69e4bffftPI89tvv8He3h7+/v4AXu8ZefDgQY4bHwOvr8bLfr327dvD0NAQkydPzrGXQQih/i5Uq1YN9vb2WLRoETIzM9VtwsLC8vy5BgYG5mm0By8vL8yZMwfTpk1DQEDAG9vt2rUL1apVQ4kSJTSm79+//43tgZyHkE6fPg1ra2uUL1/+ndk+++wzqFQqLF26FIsXL4aRkRH69ev3zkPbb2JjY4MvvvgCe/fuxblz5wC83hsdFRWlMUJIamoqFi9eDA8PD/Ve7w4dOiAhIQG//vprjuXmlqdx48ZYt24d9uzZg549e6r/EOf1OwC8Xo8/5rSH/v37Y+LEiZg5c+Yb2+S2zgHIccVxVlZWjiwODg5wdnZW76FNSUnRKHaB18WggYFBrrdD+q/som769OkwMzPTKFoDAgJgZGSkvjVWXgrA99kO5ib7avf/vhfZe/r/ffWtl5cXDh06pNFu8eLFOfYAZhdNH5orr+try5Yt8ejRI6xfv1497dWrV5g/fz4sLCxQv359AFAfQv1vHicnJ1SuXBkrVqzQmHfp0iX89ddfGncCeF/+/v4wNjZ+5/ZJLpdj06ZNqF27NoYPH65x54ZmzZpBqVRqbHNVKhUWLFiQ67KSk5Nx69atHPd+pVwU6iUnemrDhg1CLpcLJycn8e2334qlS5eKCRMmCGdnZ2FsbCw2b96s0T4hIUGYm5uLUqVKiTlz5oipU6cKV1dXUbVq1RxXv7Vs2VKYm5uLmTNnirVr14rIyMi3Zrly5YpwcnISCoVCDBgwQCxZskT88ssv6mXndjUp3vMq4NzapqWlCTs7O1GzZk11H4sVKybc3d3FzJkzxaxZs0SVKlWEn59fjivnPvQq4EGDBgkbGxvx6aefil9//VUsXrxYjBgxQlhbWwsTExON96p+/frC2dlZODg4iCFDhoj58+eLOnXqCABi8eLF6nZZWVmiZcuWQiaTia5du4r58+eLOXPmiAEDBghbW1uNKy6nTZsmAIhatWqJGTNmiNDQUDFmzBjh4+OjcUV09hWwtWvXFvPmzRMjRowQNjY2olSpUu+8ClgIITZt2iQAiOjoaI3p/76q8G3++5m5u7uLSZMm5Whnbm4uKlSoIMaNGyeWLFki5s6dK4KDgwUAUb16daFUKjXaV6hQQfTo0eOd+bOvug0LC1NPW716tQAgFixY8Nbn/vtq3P968OCBMDY2Fl26dBFCvL4qvESJEsLa2lpMmDBBzJ49W1SuXFnIZDKxZcsW9fNevXolgoKCBADRtWtXsWDBAjFjxgzRtGlTsW3btje+7qpVq4RMJhOff/65elpevwMzZswQAMSIESNEeHi4xlX4ucm+CvhtcrsKuF69esLMzEyMHz9eLFy4ULRt21a9zmWvT8+ePVNfVTtr1iyxePFi0blzZ42r5Ldu3SpKliwphg8fLhYuXCjmzZsnqlevLuRyuTh+/PhbcwkhxMuXL4WxsbEAIIKCgnLM9/f3FwCEjY1NjqvMc7sK+E3bwTetA9nvTUxMjHpa7969BQDRuXNnsWDBAvXjtm3bajw3+4r/9u3bi9DQUDFgwADh6ekp7OzsNK4CjouLE4aGhqJmzZoiLCxMrF27Vjx+/PiN78mHrq9paWmiXLlywtjYWIwaNUrMnz9ffVeDf185K4QQvr6+wtHRUSxYsECsXbtWfXXuvn37hJGRkShbtqz4+eefxffffy/s7e1FsWLFxO3bt98747+1bt1aBAYG5pie23c4KSlJ+Pn5CSMjI7F9+3YhxOv1MSAgQBgaGorBgweLX3/9VTRt2lRUrlw5x3ZDiP9tD2/evJnnjPqKBWAhuXDhgujWrZtwcnIScrlcODo6im7duuV6ebwQQvz111+iQoUKwtjYWJQpU0asXr061w3ftWvXRL169YSpqakAkKdbwsTHx4uRI0cKb29voVAohI2NjWjcuPEb/+jkRwEohBCTJk3SKO6OHj0qatasKUxNTYWzs7MYM2aM2Lt3b74VgBcuXBCjR48WVatWFba2tsLIyEg4OTmJTp06iTNnzmi0rV+/vihfvrw4deqUCAwMFCYmJsLd3V38+uuvOZabmZkppk+fLsqXLy8UCoUoVqyY8Pf3F5MnTxbJyckabTdv3izq1KkjzM3Nhbm5uShbtqwYNGhQjmJt4cKFwtPTUygUClGtWjVx6NAhUb9+/TwVgBkZGcLOzk59K5BsH/IH5dKlSwKAiIqKytFu7dq1omvXrsLLy0uYmpoKExMT4evrK8aPH6++1US2q1evCgDi77//futr37t3T1hbW4vg4OAc89q1ayfMzc01/gD919sKQCGE6NOnjzA0NFT/Mbh165bo2LGjsLGxESYmJiIgIEDs3Lkzx/PS0tLE+PHjhaenp3p97dixo7h169ZbX3fhwoUCgPjqq6/U0/LyHXjx4oUICQkRNjY2AsA7bwnzoQXg/fv3Rbt27YSNjY2wtrYWnTp1Eg8fPtRYnzIyMsTo0aOFn5+fsLS0FObm5sLPz08sXLhQvZzbt2+Lvn37Ci8vL2FiYiJsbW1FgwYN3vl5/1tgYKAAIL755psc84YOHSoAiBYtWuSY9z7bwfcpAJVKpZg8ebL6M3d1dRXjxo3TuCWVEK9/BH799dfCzs5OmJmZiWbNmombN2/muA2MEEL8/vvvolSpUsLQ0PCdt4T50AJQCCEeP34sPv30U2FnZyeMjY1FxYoVc709yrFjx4S/v7+6+P73NvTvv/8WtWvXFqampsLKykoEBweLK1eufFDGf9uyZYuQyWQiNjZWY/qbvsOPHj0S3t7ewsTERP1+PXnyRISEhAhLS0thbW0t+vTpI44ePSoAiHXr1mk8v0uXLqJOnTp5zqfPZEJ84DEWIh0SFBSEhISEN97oV9v98MMPWL58OW7cuPHGE97zYsaMGZg1axbi4uI+6hym4cOH49ChQzh9+jSvxiPSY1lZWfD19UXnzp3xww8/5Ntyt23bhnbt2uHIkSPqc0gfPXoET09PrFu3Dm3atMm319JVPAeQSAeMGDECL168wLp16z5qOR4eHpg9e/ZHFW1Pnz7FkiVL8OOPP7L4I9JzhoaG+P7777FgwQK8ePHig5bx8uVLjcdZWVmYP38+rKysNEYZmTNnDipWrMjiL4+4B5AIRX8PIBGRrurfvz9evnyJwMBAZGRkYMuWLTh27BimTp2KcePGSR2vyOJ9AImIiEhrNWzYEDNnzsTOnTuRnp4Ob29vzJ8/H4MHD5Y6WpHGPYBEREREeobnABIRERHpGRaARERERHqGBSARERGRnuFFIB9BpVLh4cOHsLS05O0uiIiIigghBJ4/fw5nZ2cYGOjnvjAWgB/h4cOHcHV1lToGERERfYB79+7BxcVF6hiSYAH4ESwtLQG8/gJZWVlJnCZ/KJVK/PXXX2jatCnkcrnUcQoc+6vb2F/dxv7qtoLsb0pKClxdXdV/x/URC8CPkH3Y18rKSqcKQDMzM1hZWenNBob91V3sr25jf3VbYfRXn0/f0s8D30RERER6jAUgERERkZ5hAUhERESkZ1gAEhEREekZFoBEREREeoYFIBEREZGeYQFIREREpGd0tgAMDQ1FpUqV1PfoCwwMxO7du9XzHz16hJ49e8LR0RHm5uaoWrUqNm/eLGFiIiIiosKhswWgi4sLfvrpJ5w+fRqnTp1Cw4YN0aZNG1y+fBkA0KtXL0RHR2PHjh24ePEi2rdvj86dO+Ps2bMSJyciIiIqWDpbAAYHB6Nly5bw8fFB6dKlMWXKFFhYWCAyMhIAcOzYMQwZMgQBAQEoVaoUvv32W9jY2OD06dMSJyciIiIqWDpbAP5bVlYW1q1bh9TUVAQGBgIAatWqhfXr1yMxMREqlQrr1q1Deno6goKCpA1LREREVMB0eizgixcvIjAwEOnp6bCwsMDWrVvh6+sLANiwYQO6dOmC4sWLw8jICGZmZti6dSu8vb3fuLyMjAxkZGSoH6ekpAB4PV6hUqnM1+xCCEnGKMzuR373R1uxv7qN/dVt7K9uK8j+6st7+DYyIYSQOkRByczMRGxsLJKTk7Fp0yYsWbIEBw8ehK+vL4YMGYKoqChMnToVdnZ22LZtG2bPno3Dhw+jYsWKuS5v0qRJmDx5co7p4eHhMDMzy7fccWnA6puG6FwqC+4W+bZYIiIiApCWloaQkBAkJyfDyspK6jiS0OkC8L8aN24MLy8vjBkzBt7e3rh06RLKly+vMd/b2xuLFi3K9fm57QF0dXVFQkJCvn6BRm68gD8uPIKbrSm2fxkIC0Xh7ahVKpXYt28fmjRpArlcXmivKxX2V7exv7qN/dVtBdnflJQU2NnZ6XUBqNOHgP9LpVIhIyMDaWlpAAADA81TIA0NDaFSqd74fIVCAYVCkWO6XC7P1y/nj20r4fTdJMQmvsQPf0ZjVpfK+bbsvMrvPmk79le3sb+6jf3VbQXRX316/95EZy8CGTduHA4dOoQ7d+7g4sWLGDduHCIiItC9e3eULVsW3t7e+OKLLxAVFYVbt25h5syZ2LdvH9q2bSt1dFibyTG3WxUYyIAtZx9gy5n7UkciIiIiHaKzBWB8fDx69eqFMmXKoFGjRjh58iT27t2r3pW8a9cu2NvbIzg4GJUqVcLKlSuxYsUKtGzZUuroAIDqHrYY1qg0AGDCtku4k5AqcSIiIiLSFTp7CHjp0qVvne/j46P1I38MbuiNo7cSEBWTiKHrzmLTgFowNtLZmp2IiIgKCasJLWZoIMOcLpVhbSrHhfvJmPlXtNSRiIiISAewANRyzjammN6hEgDgt0O3cej6E4kTERERUVHHArAIaF7BET1qugEARm44jyfPM97xDCIiIqI3YwFYRHzbyhelS1gg4UUGRm08D5VKb27fSERERPmMBWARYSI3xK8hVaEwMsCh60+w9EiM1JGIiIioiGIBWISULmGJ74Jfj2U8Y+81XLifJG0gIiIiKpJYABYxIQFuaF7eEcosgaFrz+JFxiupIxEREVERwwKwiJHJZPipQ0U4W5vgztM0fLf9ktSRiIiIqIhhAVgE2ZgZY07X/x8q7swDbD3LoeKIiIgo71gAFlEBnrYY2sgHAPDt1ku4+5RDxREREVHesAAswgY38EaAhy1SM7MwdO1ZZL5SSR2JiIiIigAWgEWYkaEBZnd9PVTc+fvJmLmPQ8URERHRu7EALOJK2phieoeKAIDfDnKoOCIiIno3FoA6oHkFJ3Sv8b+h4hJecKg4IiIiejMWgDpiQut/DRW3gUPFERER0ZuxANQRJnJDzO/2eqi4g9efYNlRDhVHREREuWMBqEPKOFpiQuvXQ8VN33MNF+8nS5yIiIiItBELQB3TvYYbmpUvAWWWwJC1ZzhUHBEREeXAAlDHyGQyTO9QCU7/P1TcxO2XpY5EREREWoYFoA6yMTPGnC6VYSADNp+5j21nH0gdiYiIiLQIC0AdVaNUcQxp+P9DxW3jUHFERET0PywAddiQht6o7lEMLzJecag4IiIiUmMBqMOMDA0wp2sVWJkY4fz9ZMzad13qSERERKQFWADquJI2ppjRsRIAYNHBWzh8g0PFERER6TsWgHqAQ8URERHRv7EA1BPZQ8U9eZ6BrzZyqDgiIiJ9xgJQT/x7qLiIaA4VR0REpM9YAOqRMo6W+PZfQ8VdesCh4oiIiPQRC0A906OGG5r6Zg8VdxapHCqOiIhI77AA1DMymQwzOr4eKi4mIRUTd3CoOCIiIn3DAlAP2ZgZY/b/DxW36fR9bD/HoeKIiIj0CQtAPVWzVHEM/v+h4sZvvYTYp2kSJyIiIqLCwgJQjw3911BxQ9adhTKLQ8URERHpA50tAENDQ1GpUiVYWVnBysoKgYGB2L17t0ab48ePo2HDhjA3N4eVlRXq1auHly9fSpS48GkMFXcviUPFERER6QmdLQBdXFzw008/4fTp0zh16hQaNmyINm3a4PLl1xc9HD9+HM2bN0fTpk0RFRWFkydPYvDgwTAw0Nm3JFclbUwxvcP/hoo7euupxImIiIiooBlJHaCgBAcHazyeMmUKQkNDERkZifLly2PEiBEYOnQoxo4dq25TpkyZwo6pFVpUdEJIDTeEn4jF6E0XMbys1ImIiIioIOnF7q6srCysW7cOqampCAwMRHx8PE6cOAEHBwfUqlULJUqUQP369XHkyBGpo0pmQitf+DhY4MmLTKy5aQAhOFQcERGRrtLZPYAAcPHiRQQGBiI9PR0WFhbYunUrfH19ERkZCQCYNGkSfvnlF1SuXBkrV65Eo0aNcOnSJfj4+OS6vIyMDGRkZKgfp6SkAACUSiWUSmXBd6gAGcmA2Z0qov2iE7iSBCw/GoO+dUpJHavAZX9uRf3zyyv2V7exv7qN/c3/ZeszmdDhXT2ZmZmIjY1FcnIyNm3ahCVLluDgwYNISkpC7dq1MW7cOEydOlXdvlKlSmjVqhWmTZuW6/ImTZqEyZMn55geHh4OMzOzAutHYTr8SIZNMYYwlAmMrJgFF3OpExEREeWvtLQ0hISEIDk5GVZWVlLHkYROF4D/1bhxY3h5eWHs2LEoVaoUVq1ahR49eqjnd+nSBUZGRlizZk2uz89tD6CrqysSEhJ05guUmZmJrr/+g4vPDOBZ3AxbB9aEuUJ3dxQrlUrs27cPTZo0gVwulzpOgWN/dRv7q9vY3/yTkpICOzs7vS4Adfcvey5UKhUyMjLg4eEBZ2dnREdHa8y/fv06WrRo8cbnKxQKKBSKHNPlcrlOrYzdvFSIv26KmKdpmLL7On7u5Cd1pAKna5/hu7C/uo391W3sb/4sU9/pbAE4btw4tGjRAm5ubnj+/DnCw8MRERGBvXv3QiaTYfTo0Zg4cSL8/PxQuXJlrFixAteuXcOmTZukji45czkws2NF9Fx+ChtP30cdHzu0qVxS6lhERESUT3S2AIyPj0evXr0QFxcHa2trVKpUCXv37kWTJk0AAMOHD0d6ejpGjBiBxMRE+Pn5Yd++ffDy8pI4uXao4WmLIQ28Me+fm/h26yVUcS0Gt+K6cZ4jERGRvtPZAnDp0qXvbDN27FiN+wCSpqGNfHDs1lOcuvsMQ9edxcYBgZAb6sWdg4iIiHQa/5rTG70eKq4yrEyMcO5eEmZzqDgiIiKdwAKQ3sqlmBl++v+h4kIP3sLRmwkSJyIiIqKPxQKQ3qllRSd0C3CDEMCI9efw9EXGu59EREREWosFIOXJd6194e1ggfjnGRi96QKHiiMiIirCWABSnpgaG2J+tyowNjLAP9fiEXbsjtSRiIiI6AOxAKQ8K+dkhW9blQMATNt1DZceJEuciIiIiD4EC0B6Lz1ruqNxuRLIzFJh6NqzSM14JXUkIiIiek8sAOm9yGQy/NyxEhytTHA7IRWT/7gsdSQiIiJ6TywA6b0VMzfG7C6VIZMBG07dx47zD6WORERERO+BBSB9kECv4hjSwBsAMH7LRdxLTJM4EREREeUVC0D6YEMb+cDfvRieZ7zC0HVnocxSSR2JiIiI8oAFIH0wI0MDzO1aGZYmRjgbm4Q5f3OoOCIioqKABSB9FJdiZvip/euh4hZG3MIxDhVHRESk9VgA0kdrVckJ3QJcIQQwfP05JKZmSh2JiIiI3oIFIOWL71qX/99QcRvPc6g4IiIiLcYCkPKFqbEh5nV9PVTc/mvxWMGh4oiIiLQWC0DKN77OVhjf8vVQcVN3XcOVhykSJyIiIqLcsACkfNUr8H9DxQ1eewZpmRwqjoiISNuwAKR8pTFU3JNUTN5xRepIRERE9B8sACnf/XuouPWn7mH7uQdSRyIiIqJ/YQFIBSLQqzgG//9QcaM3XsDxW08lTkRERETZWABSgRneuDSalX99PuBnK0/h0oNkqSMRERERWABSATI0kGFu1yqo4WmLFxmv0Gf5Sdx9mip1LCIiIr3HApAKlIncEL/3roZyTlZIeJGBnkujEP88XepYREREeo0FIBU4KxM5VvStDjdbM8QmpqH3spNISVdKHYuIiEhvsQCkQuFgaYJV/QJgZ2GMq3Ep+GzFKaQrs6SORUREpJdYAFKhcS9ujrBPA2ChMMKJmEQMW3cWWSqOGUxERFTYWABSoapQ0hqLe/nD2NAAey8/xrfbLkIIFoFERESFiQUgFbpaXnaY160yDGTA2qh7mPnXdakjERER6RUWgCSJ5hWc8GPbigCAXw/cxPKjMRInIiIi0h8sAEkyITXcMKpJaQDA5D+ucMg4IiKiQsICkCQ1uKE3ege6AwC+2ngeh64/kTgRERGR7mMBSJKSyWSYGFwewX7OUGYJDFh9GufuJUkdi4iISKexACTJGRjIMLOTH+r62CEtMwufLo/CzfgXUsciIiLSWTpbAIaGhqJSpUqwsrKClZUVAgMDsXv37hzthBBo0aIFZDIZtm3bVvhBCQBgbGSA0B7+8HOxxrM0JXovi0Jc8kupYxEREekknS0AXVxc8NNPP+H06dM4deoUGjZsiDZt2uDy5csa7ebMmQOZTCZRSvo3C4URlvWpjlJ25niQ9BK9lkYhKS1T6lhEREQ6R2cLwODgYLRs2RI+Pj4oXbo0pkyZAgsLC0RGRqrbnDt3DjNnzsSyZcskTEr/VtxCgZX9AlDCSoEb8S/QN+wkXmZyyDgiIqL8ZCR1gMKQlZWFjRs3IjU1FYGBgQCAtLQ0hISEYMGCBXB0dMzTcjIyMpCRkaF+nJKSAgBQKpVQKpX5H1wC2f2Qsj8lLORY1qsqui05iTOxSRiw+hRCQypDbpj/v1e0ob+Fif3VbeyvbmN/83/Z+kwmdHgcrosXLyIwMBDp6emwsLBAeHg4WrZsCQD44osvkJWVhSVLlgB4fTXq1q1b0bZt2zcub9KkSZg8eXKO6eHh4TAzMyuQPuiz2ynAwquGUKpkqG6nQoi3CgY8Wk9ERB8peydQcnIyrKyspI4jCa0pAJOSkrB161YcPnwYd+/eRVpaGuzt7VGlShU0a9YMtWrVeu9lZmZmIjY2FsnJydi0aROWLFmCgwcP4ubNmxg1ahTOnj0LCwsLAHkrAHPbA+jq6oqEhASd+QIplUrs27cPTZo0gVwulzoODkQ/wcDwc8hSCfSr7Y6xzcvk6/K1rb8Fjf3VbeyvbmN/809KSgrs7Oz0ugCU/BDww4cP8d1332HNmjVwdnZGQEAAKleuDFNTUyQmJuLAgQP45Zdf4O7ujokTJ6JLly55XraxsTG8vb0BAP7+/jh58iTmzp0LU1NT3Lp1CzY2NhrtO3TogLp16yIiIiLX5SkUCigUihzT5XK5zq2M2tKnphWcMaODCqM2nsfSo3fhYGWKL+p75fvraEt/Cwv7q9vYX93G/ubPMvWd5AVglSpV0Lt3b5w+fRq+vr65tnn58iW2bduGOXPm4N69e/jqq68+6LVUKhUyMjIwefJk9O/fX2NexYoVMXv2bAQHB3/QsqngdPB3wdPUDEzddQ3Tdl+DrbkxOlVzlToWERFRkSV5AXjlyhUUL178rW1MTU3RrVs3dOvWDU+fPs3TcseNG4cWLVrAzc0Nz58/R3h4OCIiIrB37144OjrmeuGHm5sbPD09P6gfVLA+r+eFpy8y8duh2xi75SKKmRmjsW8JqWMREREVSZLfBuZdxd+Hto+Pj0evXr1QpkwZNGrUCCdPnsTevXvRpEmTD4lJWmBsi7LoUNUFWSqBQeFncPJOotSRiIiIiiTJ9wACwPXr15GUlISAgAD1tP379+PHH39Eamoq2rZti2+++ea9lrl06dL3aq8l18LQW8hkMvzUoSKS0jKx/1o8+oWdxIYBgSjrqJ8n8BIREX0oyfcAAsDXX3+NnTt3qh/HxMQgODgYxsbGCAwMxLRp0zBnzhzpApLWkBsa4NeQqqjmXgwp6a/Qa2kU7iWmSR2LiIioSNGKAvDUqVNo0aKF+vGaNWtQunRp7N27F3PnzsWcOXMQFhYmXUDSKqbGhljauzrKlLBE/PMM9FoWhYQXGe9+IhEREQHQkgIwISEBLi4u6scHDhzQuBo3KCgId+7ckSAZaStrMzlW9A1ASRtTxCSk4tPlJ/Ei45XUsYiIiIoErSgAbW1tERcXB+D1rVpOnTqFmjVrqudnZmbyHD3KwdHaBCv7BcDW3BgXHyRjwKrTyHjFcYOJiIjeRSsKwKCgIPzwww+4d+8e5syZA5VKhaCgIPX8K1euwMPDQ7J8pL287C2wvE91mBkb4sjNBIzacB5ZKv5YICIiehutuAp4ypQpaNy4Mdzd3WFoaIh58+bB3NxcPX/VqlVo2LChhAlJm/m52uC3nv7oG3YSOy/EwdbcGJM/KQ+ZjAMHExER5UYrCkAPDw9cu3YNly9fhr29PZydnTXmT548WeMcQaL/qutjj1mdK2PourNYefwu7CwUGNrIR+pYREREWkkrDgHXrVtXPUbvf4s/APDz83vvG0aT/gn2c8ak4PIAgFn7rmN15F2JExEREWknrSgAP/vsMxw7dgz+/v4oV64cvv76axw9epQXftB7613LA0MbegMAJmy/hF0X4yROREREpH20ogDs1asXNm/ejISEBMycORNJSUno1KkTHB0d0bdvX2zbtg0vX76UOiYVESOalEa3ADcIAQxfdw7HbiZIHYmIiEiraEUBmE2hUKBly5b47bff8PDhQ+zYsQNOTk6YMGECihcvjtatW+Po0aNSxyQtJ5PJ8GPbCmhe3hGZWSp8vuo0Lj1IljoWERGR1tCqAvC/atSogSlTpuDixYu4ePEiGjVqpL5fINHbGBrIMKdrZdQsZYsXGa/QZ3kU7iSkSh2LiIhIK2h1AfhvXl5eGDFiBDp27Ch1FCoiTOSG+L1XNZR3tkLCi0z0XHYC8SnpUsciIiKSnFbcBqZUqVJ5anf79u0CTkK6xtJEjrBPA9Bx0THcfZqGXsuisP6LQFibyqWORkREJBmtKADv3LkDd3d3hISEwMHBQeo4pGPsLRVY1bcGOiw6hmuPnuOzlaewsm8ATOSGUkcjIiKShFYUgOvXr8eyZcswa9YstGjRAn379kXLli1hYFBkjlCTlnMrboYVnwagy2/HERWTiCFrzyK0e1WpYxEREUlCKyqsTp06Yffu3bh58yb8/f0xYsQIuLq6YuzYsbhx44bU8UhH+Dpb4ffe1WBsZIB9Vx5j/NZLvNckERHpJa0oALOVLFkS48ePx40bNxAeHo4TJ06gbNmyePbsmdTRSEfULFUc87tVgYEMWH/qHmb9fVPqSERERIVOqwpAAEhPT8fq1asxefJknDhxAp06dYKZmZnUsUiHNCvviKntKgIAFh2KQUScTOJEREREhUtrCsATJ07g888/h6OjI2bNmoX27dvjwYMHWLduHRQKhdTxSMd0DXDD6GZlAABb7xhi+3neX5KIiPSHVlwEUr58ecTHxyMkJAQHDx6En5+f1JFID3wZ5IX4lJdYcTwWY7dcgp2lCYLK8Cp0IiLSfVqxB/Dq1atIT0/HypUr0aBBA9ja2ub6jyg/yWQyfNO8DPztVHilEhi4+gzOxvJ8UyIi0n1asQdw+fLlUkcgPWVgIEOIlwpmNvY4fPMpPg07iU0DAuHtYCl1NCIiogKjFQVg7969pY5AeszIAJjf1Q+9V5zB+XtJ6LU0CpsG1oKzjanU0YiIiAqE5IeAeR820gbmCiMs71MdXvbmeJicjl7LovAsNVPqWERERAVC8gKwfPnyWLduHTIz3/7H9saNGxg4cCB++umnQkpG+sbW3Bgr+9WAk7UJbsa/QN8VJ5GW+UrqWERERPlO8kPA8+fPx9dff40vv/wSTZo0QbVq1eDs7AwTExM8e/YMV65cwZEjR3D58mUMHjwYAwcOlDoy6bCSNqZY2TcAHRcdx9nYJHy55gx+71UNckPJfysRERHlG8kLwEaNGuHUqVM4cuQI1q9fjzVr1uDu3bt4+fIl7OzsUKVKFfTq1Qvdu3dHsWLFpI5LesCnhCWW9amO7ksiERH9BGM2XcDMTn4wMOANo4mISDdIXgBmq1OnDurUqSN1DCIAgL97MYT28MdnK05h69kHsDU3xretykEmYxFIRERFH49rEb1BgzIO+LlTJQDA0iMxmP/PTV60REREOoEFINFbtKvigm9blQMAzNp3Hd/vvAKVikUgEREVbSwAid6hf91S6iJw+dE7GL7+HDJfqSRORURE9OFYABLlQf+6pTCnS2UYGciw4/xD9A07iRcZvEUMEREVTTpbAIaGhqJSpUqwsrKClZUVAgMDsXv3bgBAYmIihgwZgjJlysDU1BRubm4YOnQokpOTJU5N2qxtlZJY1qc6zIwNceRmArotjkTCiwypYxEREb03ya8CTklJyXNbKyurPLd1cXHBTz/9BB8fHwghsGLFCrRp0wZnz56FEAIPHz7EL7/8Al9fX9y9excDBgzAw4cPsWnTpg/pBumJeqXtsfazmvg07CQuPkhGx9BjWNm3BtyKm0kdjYiIKM8kLwBtbGzyfGuNrKysPC83ODhY4/GUKVMQGhqKyMhI9OvXD5s3b1bP8/LywpQpU9CjRw+8evUKRkaSvy2kxfxcbbBpQCB6LYvCnadpaB96DCv6Vkd5Z2upoxEREeWJ5JXOgQMH1P9/584djB07Fn369EFgYCAA4Pjx41ixYgWmTZv2wa+RlZWFjRs3IjU1Vb3c/0pOToaVldVbi7+MjAxkZPzvkF/23kulUgmlUvnB+bRJdj90pT/v8qH9dbVRYP1nAei34jSuPX6Bzr8dx6KQKqhZyrYgYuYbfr66jf3Vbexv/i9bn8mEFt3YrFGjRujfvz+6deumMT08PByLFy9GRETEey3v4sWLCAwMRHp6OiwsLBAeHo6WLVvmaJeQkAB/f3/06NEDU6ZMeePyJk2ahMmTJ+eYHh4eDjMzHgLURy9fAUuiDXEzRQZDmUAvHxUqF9eaVYqIiHKRlpaGkJAQ9c4ffaRVBaCZmRnOnz8PHx8fjenXr19H5cqVkZaW9l7Ly8zMRGxsLJKTk7Fp0yYsWbIEBw8ehK+vr7pNSkoKmjRpAltbW+zYsQNyufyNy8ttD6CrqysSEhJ05gukVCqxb98+NGnS5K3vha7Ij/5mKLMwctNF/HUlHjIZMLFVWXSv4ZbPSfMHP1/dxv7qNvY3/6SkpMDOzk6vC0DJDwH/m6urK37//XfMmDFDY/qSJUvg6ur63sszNjaGt7c3AMDf3x8nT57E3Llz8dtvvwEAnj9/jubNm8PS0hJbt2595xdMoVBAoVDkmC6Xy3VuZdTFPr3Nx/RXLpcjtEc1fLf9EtaciMWkndeQmPYKI5qU1tqh4/j56jb2V7exv/mzTH2nVQXg7Nmz0aFDB+zevRs1atQAAERFReHGjRsaF218KJVKpd6Dl5KSgmbNmkGhUGDHjh0wMTH56OWT/jI0kOHHthXgYGmC2X9fx7x/buLJi0z80KY8jAx19m5LRERURGlVAdiyZUtcv34doaGhuHbtGoDXV/MOGDDgvfcAjhs3Di1atICbmxueP3+O8PBwREREYO/evUhJSUHTpk2RlpaG1atXIyUlRX1Bh729PQwNDfO9b6T7ZDIZhjX2gZ2lMSZsu4S1UbF4+iID87pVgYmc3ykiItIeWlUAAq8PA0+dOvWjlxMfH49evXohLi4O1tbWqFSpEvbu3YsmTZogIiICJ06cAAD1IeJsMTEx8PDw+OjXJ/3VvYY7ipsbY+i6c/jrymP0WhqF33tXg7UpDzkQEZF20LpjU4cPH0aPHj1Qq1YtPHjwAACwatUqHDly5L2Ws3TpUty5cwcZGRmIj4/H33//jSZNmgAAgoKCIITI9R+LP8oPzSs4YWXfAFgqjBB1JxGdFx3H45R0qWMREREB0LICcPPmzWjWrBlMTU1x5swZ9fl6ycnJ+bJXkKgw1SxVHBsGBMLBUoHox8/RfuEx3HryQupYRERE2lUA/vjjj1i0aBF+//13jSt0ateujTNnzkiYjOjDlHOywuaBtVDKzhwPkl6iY+gxnI19JnUsIiLSc1pVAEZHR6NevXo5pltbWyMpKanwAxHlA1dbM2wcEAg/F2s8S1Mi5PcTiIiOlzoWERHpMa0qAB0dHXHz5s0c048cOYJSpUpJkIgofxS3UCD8s5qoV9oeL5VZ6L/iFLacuS91LCIi0lNaVQB+9tlnGDZsGE6cOAGZTIaHDx9izZo1+OqrrzBw4ECp4xF9FHOFEZb0qoZ2VUrilUpg5IbzWHzoltSxiIhID2nVbWDGjh0LlUqFRo0aIS0tDfXq1YNCocBXX32FIUOGSB2P6KMZGxlgZic/FDc3xpIjMZi66xqePM/AuBblYGCgnaOGEBGR7tGqAlAmk2H8+PEYPXo0bt68iRcvXsDX1xcWFhZSRyPKNwYGMnzb2hcOVgpM3XUNvx+OQcKLTMzoWAlyjhpCRESFQCv/2sTGxuLevXuoWLEiLCwsIISQOhJRvvu8nhdmdvKDoYEMW88+QP8Vp5Ca8UrqWEREpAe0qgB8+vQpGjVqhNKlS6Nly5aIi4sDAPTr1w+jRo2SOB1R/uvg74IlvavBVG6Ig9efIGTJCSSmZkodi4iIdJxWFYAjRoyAXC5HbGwszMzM1NO7dOmCPXv2SJiMqOA0KOOA8M9qoJiZHOfvJaFj6DHcf5YmdSwiItJhWlUA/vXXX5g+fTpcXFw0pvv4+ODu3bsSpSIqeFXcimHjgFooaWOK2wmp6BB6DNcepUgdi4iIdJRWFYCpqakae/6yJSYmQqFQSJCIqPB4O1hg88BaKFPCEo9TMtBp0XGcuP1U6lhERKSDtKoArFu3LlauXKl+LJPJoFKpMGPGDDRo0EDCZESFw9HaBBu+CER1j2J4nv4KPZdFYc+lR1LHIiIiHaNVBeCMGTOwePFitGjRApmZmRgzZgwqVKiAQ4cOYfr06VLHIyoU1mZyrOpXA018SyDzlQpfrjmN8BOxUsciIiIdolUFYIUKFXD9+nXUqVMHbdq0QWpqKtq3b4+zZ8/Cy8tL6nhEhcZEbojQ7lXRLcAVKgF8s/Ui5v59g7dEIiKifKFVN4IGAGtra4wfP17qGESSMzI0wNR2FWFvocC8f25i9t/X8eRFOiZ/UgGGHDWEiIg+gtYVgM+ePcPSpUtx9epVAICvry8+/fRT2NraSpyMqPDJZDKMbFoGdpYKTNxxGasjY/H0RSZmd6kME7mh1PGIiKiI0qpDwIcOHYKHhwfmzZuHZ8+e4dmzZ5g3bx48PT1x6NAhqeMRSaZXoAd+7VYVxoYG2H3pEfosj0JKulLqWEREVERpVQE4aNAgdOnSBTExMdiyZQu2bNmC27dvo2vXrhg0aJDU8Ygk1aqSE8L6VoeFwgiRtxPR5bdIxKekSx2LiIiKIK0qAG/evIlRo0bB0PB/h7YMDQ0xcuRI3Lx5U8JkRNqhlpcd1n1eE3YWClyNS0GHRccQk5AqdSwiIipitKoArFq1qvrcv3+7evUq/Pz8JEhEpH0qlLTGloG14F7cDPcSX6Jj6DFcuJ8kdSwiIipCtOoikKFDh2LYsGG4efMmatasCQCIjIzEggUL8NNPP+HChQvqtpUqVZIqJpHk3IqbYdOAWvg0LAqXHqSg6+JI/NbTH3V97KWORkRERYBWFYDdunUDAIwZMybXeTKZDEIIyGQyZGVlFXY8Iq1ib6nAus8DMWDVaRy5mYC+YSfxSyc/tKlcUupoRESk5bSqAIyJiZE6AlGRYqEwwrI+1TFq43n8cf4hhq07h6cvMtG3jqfU0YiISItpVQHo7u4udQSiIsfYyABzu1RGcXNjhB27g+93XsGTFxkY06wMZDLeMJqIiHLSiotArl+/jqioKI1p+/fvR4MGDRAQEICpU6dKlIyoaDAwkGFisC/GNC8DAAiNuIXRmy5AmaWSOBkREWkjrSgAv/76a+zcuVP9OCYmBsHBwTA2NkZgYCCmTZuGOXPmSBeQqAiQyWT4MsgbMzpWgqGBDJtO38cXq07jZSbPlyUiIk1aUQCeOnUKLVq0UD9es2YNSpcujb1792Lu3LmYM2cOwsLCpAtIVIR0ruaK33r4Q2FkgH+uxaP7kkg8S82UOhYREWkRrSgAExIS4OLion584MABBAcHqx8HBQXhzp07EiQjKpoa+5ZA+Gc1YG0qx5nYJHT67TgeJr2UOhYREWkJrSgAbW1tERcXBwBQqVQ4deqU+j6AAJCZmQkhhFTxiIokf3dbbBoQCCdrE9yMf4H2C4/hxuMXUsciIiItoBUFYFBQEH744Qfcu3cPc+bMgUqlQlBQkHr+lStX4OHhIVk+oqLKp4QlNg+sBW8HCzxKSUfXJVG4lSJ1KiIikppWFIBTpkzBtWvX4O7ujq+//hozZsyAubm5ev6qVavQsGFDCRMSFV3ONqbYNCAQVd1skJL+CguuGGL5sbvcq05EpMe04j6AHh4euHr1Ki5fvgx7e3s4OztrzJ88ebLGOYJE9H5szIyxpn9NjNpwFrsuPcbU3dE4HZuEGR39YG0qlzoeEREVMq3YAwgARkZG8PPzy1H8AYCfnx+KFy/+XssLDQ1FpUqVYGVlBSsrKwQGBmL37t3q+enp6Rg0aBCKFy8OCwsLdOjQAY8fP/7ofhBpK1NjQ8zpXAkdPbMgN5Rh7+XHaD3/MC7eT5Y6GhERFTKtKQDzm4uLC3766SecPn0ap06dQsOGDdGmTRtcvnwZADBixAj88ccf2LhxIw4ePIiHDx+iffv2EqcmKlgymQx1HQXWfxYAV1tT3Et8iQ6hx7Dy+B0eEiYi0iM6WwAGBwejZcuW8PHxQenSpTFlyhRYWFggMjISycnJWLp0KWbNmoWGDRvC398fy5cvx7FjxxAZGSl1dKICV7GkNXYOqYtm5UsgM0uF77ZfxuC1Z/E8XSl1NCIiKgRacQ5gQcvKysLGjRuRmpqKwMBAnD59GkqlEo0bN1a3KVu2LNzc3HD8+HGNW9D8W0ZGBjIyMtSPU1JeX06pVCqhVOrGH87sfuhKf95Fn/trJpdjfpdKCDseixl7r+PPC3G4dD8Z87pWgq+TlcRJ84c+f776gP3VbQXZX315D99GJnT4uM/FixcRGBiI9PR0WFhYIDw8HC1btkR4eDg+/fRTjWIOAAICAtCgQQNMnz491+VNmjQJkydPzjE9PDwcZmZmBdIHosJw5zkQdt0QzzJlMJIJdPBUIdBBQCaTOhkRUf5LS0tDSEgIkpOTYWWlGz9435fkewAvXLiQ57aVKlV6r2WXKVMG586dQ3JyMjZt2oTevXvj4MGD7xtRbdy4cRg5cqT6cUpKClxdXdG0aVOd+QIplUrs27cPTZo0gVyu+1eHsr//E5KmxJgtF3EgOgHrbxsizcIRP3ziC3OF5JuJD8bPV7exv7qtIPubfQRPn0m+Za9cuTJkMtkbT0DPnieTyZCV9X6D2hsbG8Pb2xsA4O/vj5MnT2Lu3Lno0qULMjMzkZSUBBsbG3X7x48fw9HR8Y3LUygUUCgUOabL5XKdWxl1sU9vw/4C9tZyLO0dgN8P38aMvdH448IjXI57joXdq6KsY9H+gcPPV7exv7qtIPqrT+/fm0heAMbExBTaa6lUKmRkZMDf3x9yuRz79+9Hhw4dAADR0dGIjY1FYGBgoeUh0jYGBjJ8Ud8L/u7FMDj8LG4/SUXbBUfxfZsK6FzNVep4RESUTyQvAN3d3QtkuePGjUOLFi3g5uaG58+fIzw8HBEREdi7dy+sra3Rr18/jBw5Era2trCyssKQIUMQGBj4xgtAiPRJNQ9b7BpWFyPWn8PB608wZtMFnLidiB/aloeZseSbDSIi+kiSb8l37NiBFi1aQC6XY8eOHW9t+8knn+R5ufHx8ejVqxfi4uJgbW2NSpUqYe/evWjSpAkAYPbs2TAwMECHDh2QkZGBZs2aYeHChR/VFyJdYmtujOV9qiP04C3M/Csam8/cx4X7SQjtURXeDpZSxyMioo8geQHYtm1bPHr0CA4ODmjbtu0b273vOYBLly5963wTExMsWLAACxYsyPMyifSNgYEMgxp4w9+9GIauPYsb8S8QPP8oprSrgPZVOTwjEVFRJfmNoFUqFRwcHNT//6Z/73sBCBHln5qliuPPoXVRx9sOL5VZGLnhPL7edAHpSq6XRERFkeQFIBEVDfaWCqzoG4ARjUtDJgPWn7qHtguO4vaTF1JHIyKi9yT5IeD/Sk1NxcGDBxEbG4vMzEyNeUOHDpUoFREBgKGBDMMa+6CaRzEMW3cW1x49R/D8I5jWoRI+8XOWOh4REeWRVhWAZ8+eRcuWLZGWlobU1FTY2toiISEBZmZmcHBwYAFIpCVqe9th19C6GLruLCJvJ2Lo2rM4cfspJrT2hYncUOp4RET0Dlp1CHjEiBEIDg7Gs2fPYGpqisjISNy9exf+/v745ZdfpI5HRP/iYGWC1f1qYEhDb8hkwJoTsegQegx3ElKljkZERO+gVQXguXPnMGrUKBgYGMDQ0BAZGRlwdXXFjBkz8M0330gdj4j+w8jQAKOalkHYpwGwNTfG5YcpCJ5/BLsvxkkdjYiI3kKrCkC5XA4Dg9eRHBwcEBsbCwCwtrbGvXv3pIxGRG9Rv7Q9dg2ti+oexfA84xUGrjmDSTsuI+MVrxImItJGWlUAVqlSBSdPngQA1K9fH9999x3WrFmD4cOHo0KFChKnI6K3cbQ2wdrPamJAfS8AQNixO+i86DjuJaZJnIyIiP5LqwrAqVOnwsnJCQAwZcoUFCtWDAMHDsSTJ0/w22+/SZyOiN7FyNAAY1uUxbI+1WBjJsf5+8loNe8w/rr8SOpoRET0L1p1FXC1atXU/+/g4IA9e/ZImIaIPlTDsiXw59C6GBx+Bmdjk/D5qtPoX8cTX7coC7mhVv3uJCLSS0ViS3zmzBm0bt1a6hhE9B5K2phi/eeB6F/HEwCw5EgMOv92HA+SXkqcjIiItKYA3Lt3L7766it88803uH37NgDg2rVraNu2LapXrw6VSiVxQiJ6X8ZGBvi2tS8W9/SHlYkRzsYmodW8w/jn2mOpoxER6TWtKACXLl2KFi1aICwsDNOnT0fNmjWxevVqBAYGwtHREZcuXcKuXbukjklEH6hpeUf8ObQu/FyskZSmRN+wU/hp9zUos/jDjohIClpRAM6dOxfTp09HQkICNmzYgISEBCxcuBAXL17EokWLUK5cOakjEtFHcrU1w4YBgehTywMAsOjgLYT8HolHyenSBiMi0kNaUQDeunULnTp1AgC0b98eRkZG+Pnnn+Hi4iJxMiLKTwojQ0z6pDwWdq8KS4URTt55hpbzDuPg9SdSRyMi0itaUQC+fPkSZmZmAACZTAaFQqG+HQwR6Z6WFZ3wx5A6KO9shcTUTPRZHoWZf0UjSyWkjkZEpBe05jYwS5YsgYWFBQDg1atXCAsLg52dnUaboUOHShGNiAqAh505Ng+shR//vILVkbGY/89NnLyTiHldq8DBykTqeEREOk0rCkA3Nzf8/vvv6seOjo5YtWqVRhuZTMYCkEjHmMgN8WPbigjwLI5xmy8g8nYiWs47jLldq6C2t927F0BERB9EKwrAO3fuSB2BiCT0iZ8zyjtbYdCaM7j26Dl6LD2BYY18MKShDwwNZFLHIyLSOVpxDiARkZe9BbYNqo2u1V0hBDDn7xvovSwKT55nSB2NiEjnsAAkIq1hIjfETx0qYXYXP5jKDXHkZgJazTuMyNtPpY5GRKRTWAASkdZpV8UFOwbXho+DBeKfZyDk90gsOHATKl4lTESUL1gAEpFW8ilhie2Da6NDVReoBPDz3mh8GnYSiamZUkcjIiryJC8AR44cidTUVADAoUOH8OrVK4kTEZG2MDM2wszOfpjRsRIURgY4eP0JWs49jFN3EqWORkRUpEleAM6fPx8vXrwAADRo0ACJidywE5GmztVcsX1wbZSyN8ejlHR0WRyJhRE38YpjCRMRfRDJbwPj4eGBefPmoWnTphBC4Pjx4yhWrFiubevVq1fI6YhIW5R1tMIfg+vgm60Xsf3cQ8zYE429lx5hesdKKOtoJXU8IqIiRfIC8Oeff8aAAQMwbdo0yGQytGvXLtd2MpkMWVlZhZyOiLSJucIIc7pURm1vO/yw8wrO309G8Pwj+DLIG4MaeMPYSPKDGkRERYLkW8u2bdvi0aNHSElJgRAC0dHRePbsWY5/PDRMRMDrH4Odq7ni75H10cS3BJRZAnP330Dw/CM4fy9J6nhEREWC5AVgNgsLCxw4cACenp6wtrbO9R8RUbYSViZY3NMf87tVQXFzY0Q/fo52C49i6q6rSFfyaAER0dtIfgj437y9vbFw4UJcv34dAFC6dGl06NABJUuWlDgZEWkjmUyGYD9n1Pa2w+Q/LmP7uYdYfOg2/rr8CNM7VEJVV54bSESUG60pABcuXIiRI0ciMzMTVlavN9opKSkYM2YMZs2ahS+//FLihESkrWzNjTG3axV84ueM8Vsv4c7TNHRZHImQABf4SR2OiEgLacUh4D///BNDhw7F4MGD8eDBAyQlJSEpKQkPHjzAl19+iWHDhmHXrl1SxyQiLdeoXAn8NbIeugW4AgDCo+5j2jlDHLqRIHEyIiLtohUF4M8//4yxY8fil19+gZOTk3q6k5MTZs2aha+//hozZsyQMCERFRVWJnJMa18J4f1rwLWYKZIyZei38gxGbjiHpDSOIkJEBGhJAXjmzBn07NnzjfN79uyJM2fOvNcyp02bhurVq8PS0hIODg5o27YtoqOjNdo8evQIPXv2hKOjI8zNzVG1alVs3rz5g/pARNqllrcddg4ORJCTCjIZsOXMAzSedQi7L8ZJHY2ISHJaUQBmZWVBLpe/cb5cLn/vewAePHgQgwYNQmRkJPbt2welUommTZuqh50DgF69eiE6Oho7duzAxYsX0b59e3Tu3Blnz5794L4QkfYwMzZCOw8V1n8WAG8HCyS8yMDANWcwcPVpxD9PlzoeEZFktKIALF++PLZv3/7G+du2bUP58uXfa5l79uxBnz59UL58efj5+SEsLAyxsbE4ffq0us2xY8cwZMgQBAQEoFSpUvj2229hY2Oj0YaIir4qrjb4c2gdDGnoDSMDGXZfeoQmsw5h8+n7EEJIHY+IqNBpRQE4aNAgjB8/HgsXLsSrV6/U01+9eoUFCxbg22+//eirgJOTkwEAtra26mm1atXC+vXrkZiYCJVKhXXr1iE9PR1BQUEf9VpEpH0URoYY1bQMtg+ujQolrZD8UolRG8+jz/KTeJD0Uup4RESFSituA9O7d29cvHgRgwcPxrhx4+Dl5QUhBG7fvo0XL15g6NCh6NOnzwcvX6VSYfjw4ahduzYqVKignr5hwwZ06dIFxYsXh5GREczMzLB161Z4e3vnupyMjAxkZGSoH6ekpAAAlEollErlB+fTJtn90JX+vAv7q9ty629pezNs/CwAS4/exbwDt3Dw+hM0nXUQo5uVRrdqLjAwkEkV96Px89Vt7G/+L1ufyYQWHf+IjIzE2rVrcePGDQCvbwTdtWtX1KxZ86OWO3DgQOzevRtHjhyBi4uLevqQIUMQFRWFqVOnws7ODtu2bcPs2bNx+PBhVKxYMcdyJk2ahMmTJ+eYHh4eDjMzs4/KSESF7/FLYO0tQ8Q8f130eVkKdPXKgoOpxMGIqEClpaUhJCQEycnJ6nsP6xutKgALwuDBg7F9+3YcOnQInp6e6um3bt2Ct7c3Ll26pHF+YePGjeHt7Y1FixblWFZuewBdXV2RkJCgM18gpVKJffv2oUmTJm+9MEdXsL+6LS/9VakEVkfdw8x9N5CWmQWFkQGGN/JGn0A3GBlqxVkyecbPV7exv/knJSUFdnZ2el0AasUh4IIghMCQIUOwdetWREREaBR/wOvqHwAMDDQ38IaGhlCpVLkuU6FQQKFQ5Jgul8t1bmXUxT69Dfur297V3351vdC0vBPGbbmIIzcTMH3vdey5/BgzOvqhjKNlISbNH/x8dRv7mz/L1HdF6+ftexg0aBBWr16N8PBwWFpa4tGjR3j06BFevnx9snfZsmXh7e2NL774AlFRUbh16xZmzpyJffv2oW3bttKGJ6JC52prhlX9AjCjQyVYmhjh/P1ktJ5/GHP+vo7MV7n/KCQiKqp0tgAMDQ1FcnIygoKC4OTkpP63fv16AK+r/127dsHe3h7BwcGoVKkSVq5ciRUrVqBly5YSpyciKchkMnSu7oq/R9ZHE98SUGYJzPn7Bj759QjO30uSOh4RUb7R6UPA7+Lj48ORP4gohxJWJljc0x87L8Rh0o7LuPboOdotPIrP6pbCiCalYSI3lDoiEdFH0ao9gA0bNkRSUlKO6SkpKWjYsGHhByIivSWTyRDs54x9I+ujbWVnqATw26HbaD7nEE7cfip1PCKij6JVBWBERAQyM3MO1p6eno7Dhw9LkIiI9J2tuTHmdK2CZX2qwdHKBHeepqHL4khM2HYJLzJevXsBRERaSCsOAV+4cEH9/1euXMGjR4/Uj7OysrBnzx6ULFlSimhERACAhmVL4K+Rtpi26xrWRsViVeRd7L/6GFPbV0RQGQep4xERvRetKAArV64MmUwGmUyW66FeU1NTzJ8/X4JkRET/Y2Uix7T2FRHs54Sxmy8iNjENfZafRIeqLpjQuhxszIyljkhElCdaUQDGxMRACIFSpUohKioK9vb26nnGxsZwcHCAoSFPuiYi7VDLyw57htfFzL+uY9nRGGw+cx8Hrz/BD23Ko0VFJ6njERG9k1YUgO7u7gDwxhswExFpGzNjI0xo7YtWlZwwZtMF3Ix/gYFrzqBFBUdMblMeDpYmUkckInojrSgAs61cufKt83v16lVISYiI8qaqWzH8ObQOfv3nJkIjbmH3pUc4duspvmvti/ZVS0Imk0kdkYgoB60qAIcNG6bxWKlUIi0tDcbGxjAzM2MBSERaSWFkiFFNy6BFBSeM2Xwelx6kYNTG89hx/iGmtq+IkjamUkckItKgVbeBefbsmca/Fy9eIDo6GnXq1MHatWuljkdE9Fa+zlbY9mVtfN28LIyNDHDw+hM0nXUQqyLvQqV6983piYgKi1YVgLnx8fHBTz/9lGPvIBGRNjIyNMDAIC/sHlYX1dyLITUzCxO2XULX3yMRk5AqdTwiIgBFoAAEACMjIzx8+FDqGEREeeZlb4ENXwRi8iflYWZsiKiYRDSfcwiLD93Cqyxe8EZE0tKqcwB37Nih8VgIgbi4OPz666+oXbu2RKmIiD6MgYEMvWt5oGFZB3yz9SIO30jA1F3X8OeFOMzo6IcyjpZSRyQiPaVVBWDbtm01HstkMtjb26Nhw4aYOXOmNKGIiD6Sq60ZVvYNwMbT9/Hjzis4fz8ZrecfxqAG3vgyyBvGRkXiYAwR6RCtKgB5H0Ai0lUymQydq7mifml7TNh2CX9deYw5f9/Arotx+K51edTxsZM6IhHpEa382ZmQkICEhASpYxAR5bsSVib4rac/fg2pguLmxrj++AV6LD2Bz1aewh1eJEJEhURrCsCkpCQMGjQIdnZ2KFGiBEqUKAE7OzsMHjwYSUlJUscjIso3MpkMrSs5459RQfi0tgcMDWTYd+Uxmsw+iGm7ruJ5ulLqiESk47TiEHBiYiICAwPx4MEDdO/eHeXKlQMAXLlyBWFhYdi/fz+OHTuGYsWKSZyUiCj/WJvJMTG4PLrXcMMPO6/i4PUn+O3QbWw+cx9fNS2DTtVcYWjAkUSIKP9pRQH4/fffw9jYGLdu3UKJEiVyzGvatCm+//57zJ49W6KEREQFx9vBEiv6BuDAtXj88OcV3H6SirFbLmJV5F1819oXNUoVlzoiEekYrTgEvG3bNvzyyy85ij8AcHR0xIwZM7B161YJkhERFZ4GZR2wd3g9TGjtC0sTI1x+mIIuiyMxaM0Z3EtMkzoeEekQrSgA4+LiUL58+TfOr1ChAh49elSIiYiIpCE3NEC/Op44OLoBetR0g4EM+PNiHBrNOohf9kYjNeOV1BGJSAdoRQFoZ2eHO3fuvHF+TEwMbG1tCy8QEZHEbM2N8WPbitg1rC5qeRVH5isVfj1wEw1nRmDz6fscW5iIPopWFIDNmjXD+PHjkZmZmWNeRkYGJkyYgObNm0uQjIhIWmUdrbCmfw381tMf7sXN8DglA6M2nke70GM4ffeZ1PGIqIjSmotAqlWrBh8fHwwaNAhly5aFEAJXr17FwoULkZGRgVWrVkkdk4hIEjKZDM3KOyKojD2WH72DX/+5ifP3ktAh9BjaVHbG2BZl4WRtKnVMIipCtKIAdHFxwfHjx/Hll19i3LhxEOL1oQ2ZTIYmTZrg119/haurq8QpiYikpTAyxID6XmhftSRm7r2ODafvYfu5h9h7+REG1vfGp4HcThJR3mhFAQgAnp6e2L17N549e4YbN24AALy9vXnuHxHRfzhYmmB6x0roGeiOyX9cxsk7zzD77+tYdzIWTR1kaCF4fiARvZ1WnAP4b8WKFUNAQAACAgJY/BERvUWFktbY8EUgfg2pgpI2pohLTseKG4botuQkLtxPkjoeEWkxrSsAiYgo77KHlds/qj6GN/KGsYHA6dgktFlwFKM3nkf883SpIxKRFmIBSESkA0zkhhgUVArjK2ehrZ8ThAA2nr6PBj9HYGHETaQrs6SOSERahAUgEZEOsVEAP3esiK1f1kJlVxukZmZhxp5oNJl9EHsuxakvsiMi/cYCkIhIB1VxK4YtA2thdhc/lLBS4F7iSwxYfQbdfo/ElYcpUscjIomxACQi0lEGBjK0q+KCA18FYWhDbyiMDBB5OxGt5x/GuC0X8fRFhtQRiUgiLACJiHScmbERRjYtg/2j6qN1JSeoBLA2KhZBP0fg90O3kflKJXVEIipkLACJiPSESzEz/BpSFRu+CESFklZ4nvEKU3ZdRbM5h7D/6mOeH0ikR3S2AJw2bRqqV68OS0tLODg4oG3btoiOjs7R7vjx42jYsCHMzc1hZWWFevXq4eXLlxIkJiIqHAGettgxqA5mdKgEOwsFYhJS0W/FKfRaFoUbj59LHY+ICoHOFoAHDx7EoEGDEBkZiX379kGpVKJp06ZITU1Vtzl+/DiaN2+Opk2bIioqCidPnsTgwYNhYKCzbwsREYDX5wd2ru6KA1/Vx4D6XjA2NMDhGwloPvcwJm6/hKS0TKkjElEB0pqh4PLbnj17NB6HhYXBwcEBp0+fRr169QAAI0aMwNChQzF27Fh1uzJlyhRqTiIiKVmayDG2RVl0C3DF1F1XsffyY6w4fhfbzj3EyCal0b2GG4wM+aOYSNfozVqdnJwMAOrh5eLj43HixAk4ODigVq1aKFGiBOrXr48jR45IGZOISBLuxc3xW89qCO9fA2UdLZH8UomJOy6jxdzDOHT9idTxiCif6ewewH9TqVQYPnw4ateujQoVKgAAbt++DQCYNGkSfvnlF1SuXBkrV65Eo0aNcOnSJfj4+ORYTkZGBjIy/nfbhJSU1/fSUiqVUCqVhdCTgpfdD13pz7uwv7qN/X1/1d2tsXVADWw4/QBz9t/EjfgX6LUsCg3K2GFc8zLwtDPPr7gfjZ+vbivI/urLe/g2MqEHl30NHDgQu3fvxpEjR+Di4gIAOHbsGGrXro1x48Zh6tSp6raVKlVCq1atMG3atBzLmTRpEiZPnpxjenh4OMzMzAquA0REEkh7Bey9b4BDj2RQCRkMZQJ1HQWauahgphe7D0hXpaWlISQkBMnJybCyspI6jiR0fhUePHgwdu7ciUOHDqmLPwBwcnICAPj6+mq0L1euHGJjY3Nd1rhx4zBy5Ej145SUFLi6uqJp06Y68wVSKpXYt28fmjRpArlcLnWcAsf+6jb29+N1BHD7SSqm7onGwesJiIiT4XyyAiMae6OzvwsMDWT58jofgp+vbivI/mYfwdNnOlsACiEwZMgQbN26FREREfD09NSY7+HhAWdn5xy3hrl+/TpatGiR6zIVCgUUCkWO6XK5XOdWRl3s09uwv7qN/f04ZZxtsKJvDUREx+OHnVdw60kqvttxFeFR9/FdsC9qednl22t9CH6+uq0g+qtP79+b6GwBOGjQIISHh2P79u2wtLTEo0ePAADW1tYwNTWFTCbD6NGjMXHiRPj5+aFy5cpYsWIFrl27hk2bNkmcnohI+wSVcUBtbzusjryL2fuu49qj5wj5/QSalS+B8S194Vacp8IQFRU6WwCGhoYCAIKCgjSmL1++HH369AEADB8+HOnp6RgxYgQSExPh5+eHffv2wcvLq5DTEhEVDXJDA3xa2xNtK5fE7L+vY3XkXey9/Bj/XItHR39XfBnkBVdbFoJE2k5nC8C8XtsyduxYjfsAEhHRuxUzN8b3bSqgew13/PjnFRy+kYC1UbHYeOoe2lctiUENvOFeXHuuGCYiTXpzH0AiIsp/ZRwtsapfDWz4IhB1fezwSiWw4dR9NJx5EKM2nMftJy+kjkhEuWABSEREHy3A0xar+tXA5oG1EFTGHlkqgc1n7qPxrIMYvu4sbsZzjGEibcICkIiI8o2/ezGEfRqA7YNqo1FZB6gEsO3cQzSZfQhD1p7F9ccsBIm0AQtAIiLKd36uNljapzp2DqmDpr4lIATwx/mHaDr7EL5ccxpX43gfNiIpsQAkIqICU6GkNRb3qoZdQ+uiZUVHAMCui4/QYu5hfL7yFC49SJY4IZF+YgFIREQFztfZCgu7+2Pv8HoI9nOGTAb8deUxWs8/gn5hJ3H+XpLUEYn0CgtAIiIqNGUcLTG/WxXsG1EPbSs7w0AG7L8WjzYLjqLP8iicvvtM6ohEeoEFIBERFTpvB0vM6VoFf4+sjw5VX48pHBH9BB1Cj6Hn0hM4eSdR6ohEOo0FIBERSaaUvQVmdvbD/pH10bmaC4wMZDh8IwGdFh1Ht8WROH7rqdQRiXQSC0AiIpKch505ZnT0w4GvgtAtwA1yQxmO336Kbr9HovNvx3H0ZkKeR3giondjAUhERFrD1dYM09pXRMToBuhZ0x3GhgaIiklE9yUn0HHRcRy8/oSFIFE+YAFIRERap6SNKX5oWwGHxjRAn1oeMDYywOm7z9B7WRTaLjyGf649ZiFI9BFYABIRkdZytDbBpE/K48iYBuhXxxMmcgOcv5eEvmGn0H7RCVxMlLEQJPoALACJiEjrOViZYEJrXxwe0xBf1CsFU7khLj1MwZJoQ3yyMBJ7LsVBpWIhSJRXLACJiKjIsLdUYFzLcjjydQMMqOcJhYHAtUfPMWD1GbScdxh/XmAhSJQXRlIHICIiel/FLRQY1cQHbmk3cN+8NFZFxuLao+cYFH4GPg4WGNzQG60rOcPQQCZ1VCKtxD2ARERUZJnLgRGNvXHk64YY3tgHViZGuBH/AsPWnUOTWQex5cx9vMpSSR2TSOuwACQioiLP2kyO4Y1L48jYhhjVpDSsTeW4nZCKkRvOo9Gsg9hw6h6ULASJ1FgAEhGRzrAykWNIIx8cHdsQY5qXQTEzOe4+TcOYTRfQcGYE1kXFIvMVC0EiFoBERKRzLBRG+DLo9aHhb1qWhZ2FMe4lvsTYLRfR4JcIrI68i4xXWVLHJJIMC0AiItJZ5gojfF7PC4fHNMS3rcrB3lKBB0kv8e22S6g/IwIrjt1BupKFIOkfFoBERKTzTI0N0b9uKRwe0wCTgn1RwkqBRynpmLjjMurNOIBlR2LwMpOFIOkPFoBERKQ3TOSG6FPbEwdHN8APbSvA2doE8c8z8P3OK6g74wAWH7qF5+lKqWMSFTgWgEREpHdM5IboWdMdEaMbYFr7inApZoqEFxmYuusaak37B1P+vIIHSS+ljklUYFgAEhGR3jI2MkC3ADcc+CoIMzpUgpe9OZ5nvMLvh2NQb8YBDFl7FhfuJ0kdkyjfcSQQIiLSe3JDA3Su7oqO/i6IuB6PJYdjcOzWU/xx/iH+OP8QAR626F/XE43KleDoIqQTWAASERH9PwMDGRqWLYGGZUvg0oNkLDsSgx3nHyLqTiKi7iTCo7gZ+tXxRAd/F5gZ808oFV08BExERJSLCiWtMatLZRz5uiEGBnnBysQId56mYcL2y6j10z/4ee81xKekSx2T6IOwACQiInoLR2sTfN28LI6Pa4TJn5SHm60ZktKUWHDgFmpP/wejNpzHlYcpUsckei/cf01ERJQH5goj9K7lgR413bHvymMsOXwbp+4+w+Yz97H5zH3U8bZDv7qeCCptD5mM5wmSdmMBSERE9B4MDWRoXsERzSs44mzsMyw5EoPdF+Nw5GYCjtxMgI+DBfrV8UTbKiVhIjeUOi5RrngImIiI6ANVcSuGBSFVcXB0A/Sr4wkLhRFuxL/A2C0XUfunfzDn7+t4+iJD6phEObAAJCIi+kiutmaY0NoXx8Y1xPiW5eBsbYKnqZmY8/cNBP70D8ZtuYCb8c+ljkmkprMF4LRp01C9enVYWlrCwcEBbdu2RXR0dK5thRBo0aIFZDIZtm3bVrhBiYhIZ1iZyPFZvVI4OKYB5nWrgkou1sh8pcLaqHtoPOsQPl0ehWM3EyCEkDoq6TmdLQAPHjyIQYMGITIyEvv27YNSqUTTpk2Rmpqao+2cOXN4wi4REeUbuaEBPvFzxvZBtbHhi0A09S0BmQw4EP0EIUtOoNW8I9hy5j4yX6mkjkp6SmcvAtmzZ4/G47CwMDg4OOD06dOoV6+eevq5c+cwc+ZMnDp1Ck5OToUdk4iIdJhMJkOApy0CPG0Rk5CK5UdjsPHUfVyJS8HIDecxfc819K7lgZAAN9iYGUsdl/SIzu4B/K/k5GQAgK2trXpaWloaQkJCsGDBAjg6OkoVjYiI9ICnnTm+b1MBx8c1xOhmZeBgqcDjlAzM2BONwGn/YOL2S7j7NOdRKqKCoLN7AP9NpVJh+PDhqF27NipUqKCePmLECNSqVQtt2rTJ03IyMjKQkfG/q7lSUl7f+FOpVEKpVOZvaIlk90NX+vMu7K9uY391W1Htr7lchs/ruKNPTVf8efERlh29g2uPX2DF8btYGXkXjcs6oG9td/i72WicnlRU+/uhCrK/+vIevo1M6MGZqAMHDsTu3btx5MgRuLi4AAB27NiBUaNG4ezZs7CwsADwelf91q1b0bZt21yXM2nSJEyePDnH9PDwcJiZmRVYfiIi0l1CANdTZDjwUIarSf87MOduIRDkpIJfcQFDnqaer7KPACYnJ8PKykrqOJLQ+QJw8ODB2L59Ow4dOgRPT0/19OHDh2PevHkwMPjfypaVlQUDAwPUrVsXEREROZaV2x5AV1dXJCQk6MwXSKlUYt++fWjSpAnkcrnUcQoc+6vb2F/dpov9vRH/AmHH7mLb+Tj1BSLO1iboHeiGdn4lcPzQAZ3q79sU5OebkpICOzs7vS4AdfYQsBACQ4YMwdatWxEREaFR/AHA2LFj0b9/f41pFStWxOzZsxEcHJzrMhUKBRQKRY7pcrlc51ZGXezT27C/uo391W261F/fksUwo1MxjGlRDquO38WqyLt4mJyOaXuuY96BW6hua4DKqa/gbq8/R50K4vPVle/Lx9DZAnDQoEEIDw/H9u3bYWlpiUePHgEArK2tYWpqCkdHx1wv/HBzc8tRLBIRERUmOwsFRjQpjYFBXth69gGWHL6NW09SERFngIazj6BFBUd8VrcU/FxtpI5KRZTOXgUcGhqK5ORkBAUFwcnJSf1v/fr1UkcjIiLKExO5IboFuGHfiPr4vWcVlLZWIUslsPNCHNosOIpOi45h7+VHyFLp9NlcVAB0dg/gh5zaqOOnQxIRURFlYCBDUGl7pPmq4FGlFlZE3sMf5x/i5J1nOHnnNDyKm6FvHU909HeBmbHO/mmnfKSzewCJiIh0ka+TFWZ1rozDYxpiYJAXrEyMcOdpGr7bfhmB0/7BjD3X8DglXeqYpOVYABIRERVBjtYm+Lp5WRwf1wjftykP9+JmSH6pxMKIW6gz/R8MCj+D47ee8ugW5Yr7iYmIiIowc4URegV6oHsNd/x99TGWHL6Nk3ee4c8LcfjzQhxK2Zujew13dKzqAmszXv1Kr7EAJCIi0gGGBjI0K++IZuUdcelBMsKjYrHt7APcfpKKH3ZewYw91xDs54zuNdxQ2VVzlBHSPywAiYiIdEyFktaY2q4ixrUoi+3nHmJ15F1ce/Qcm07fx6bT9+HrZIXuNd3QpnJJWChYCugjngNIRESkoyxN5OhR0x27h9XF5oG10L5qSRgbGeBKXArGb72EmlP349ttF3E1LkXqqFTIWPYTERHpOJlMBn/3YvB3L4YJrXyx+cx9rDkRi5iEVKyOjMXqyFhUdbNBj5ruaFnRCSZyQ6kjUwFjAUhERKRHipkbo3/dUuhXxxPHbz3FmhOx2Hv5Ec7EJuFMbBK+33kFHau6IKSGG0rZW0gdlwoIC0AiIiI9JJPJUMvbDrW87RD/PB0bTt7D2qh7eJD0EkuOxGDJkRjU8iqOHjXd0cS3BOSGPGtMl7AAJCIi0nMOliYY3NAHA4O8cfB6PNZExuKf6Hgcu/UUx249hb2lAl2quaJrgCtciplJHZfyAQtAIiIiAvD6VjINy5ZAw7IlcP9ZGtZF3cO6k/fw5HkGfj1wEwsjbqJBGQd0r+mG+qUdYGjAW8kUVSwAiYiIKAeXYmb4qlkZDGvsg31XHmPNibs4evMp9l+Lx/5r8ShpY4puAa7oXN0VDpYmUsel98QCkIiIiN5IbmiAlhWd0LKiE249eYG1J2Kx8fR9PEh6iV/+uo45f99As/KO6F7DDYFexXmD6SKCBSARERHliZe9Bb5t7YuvmpXBrotxWHMiFqfvPsOfF+Pw58U4lLIzR0gNN3T0d4GNmbHUcektWAASERHRezGRG6J9VRe0r+qCKw9TEB51F1vPPMDthFT8+OdVzNgbjdaVnNC9hjuqunHYOW3EApCIiIg+mK+zFX5sWxFjW5TDjv8fdu5KXAq2nHmALWceoJyTFbrXcEPbKhx2TpvwkyAiIqKPZqEwQkgNN3QLcMW5e0lYcyIWf5x/iKtxKfh22yVM23UVbaqURPcabijvbC11XL3HApCIiIjyjUwmQxW3Yqji9nrYuU1n7mPNibu4/SQV4SdiEX4iFpVdXw8717oSh52TCgtAIiIiKhDWZnL0q+OJvrU9EHk7EWtO3MXey49w7l4Szt1Lwvd/XEZHf1eE1HCDtwOHnStMLACJiIioQMlkMgR6FUegV3E8eZ6BjafvIfxELO4/e4llR2Ow7GgMAksVR/eabmjq6whjIw47V9BYABIREVGhsbdU4Msgb3xRzwuHbjx5Pezctcc4fvspjt9+CjsLY3Su5opOVZ2ljqrTWAASERFRoTM0kKFBGQc0KOOAB0kvsT4qFutO3kP88wwsjLiF0IO3UM7aAFaln6JBOUep4+oc7mMlIiIiSZW0McXIpmVwdGxDLOpRFXV97CAEcCXJAEdvPZU6nk7iHkAiIiLSCnJDAzSv4ITmFZxw41ESpq0/jK7VXaSOpZO4B5CIiIi0jkdxc7TxUMHd1kzqKDqJBSARERGRnmEBSERERKRnWAASERER6RkWgERERER6hgUgERERkZ5hAUhERESkZ1gAEhEREekZFoBEREREeoYFIBEREZGe0dkCcNq0aahevTosLS3h4OCAtm3bIjo6Wj0/MTERQ4YMQZkyZWBqago3NzcMHToUycnJEqYmIiIiKng6WwAePHgQgwYNQmRkJPbt2welUommTZsiNTUVAPDw4UM8fPgQv/zyCy5duoSwsDDs2bMH/fr1kzg5ERERUcEykjpAQdmzZ4/G47CwMDg4OOD06dOoV68eKlSogM2bN6vne3l5YcqUKejRowdevXoFIyOdfWuIiIhIz+lNlZN9aNfW1vatbaysrN5Y/GVkZCAjI0P9OCUlBQCgVCqhVCrzMa10svuhK/15F/ZXt7G/uo391W0F2V99eQ/fRiaEEFKHKGgqlQqffPIJkpKScOTIkVzbJCQkwN/fHz169MCUKVNybTNp0iRMnjw5x/QlS5bAzMwsXzMTERFRwUhLS0P//v2RlJQEa2trqeNIQi8KwIEDB2L37t04cuQIXFxccsxPSUlBkyZNYGtrix07dkAul+e6nP/uAXzw4AF8fX0LLDcREREVnHv37uVaF+gDnS8ABw8ejO3bt+PQoUPw9PTMMf/58+do1qwZzMzMsHPnTpiYmOR52SqVCg8fPoSlpSVkMll+xpZMSkoKXF1dce/ePVhZWUkdp8Cxv7qN/dVt7K9uK8j+CiHw/PlzODs7w8BAZ6+HfSudPQdQCIEhQ4Zg69atiIiIyLX4S0lJQbNmzaBQKLBjx473Kv4AwMDAQGd/OVhZWenFBiYb+6vb2F/dxv7qtoLqr74e+s2mswXgoEGDEB4eju3bt8PS0hKPHj0C8PoDNzU1RUpKCpo2bYq0tDSsXr0aKSkp6os67O3tYWhoKGV8IiIiogKjswVgaGgoACAoKEhj+vLly9GnTx+cOXMGJ06cAAB4e3trtImJiYGHh0dhxCQiIiIqdDpbAL7r1MagoKB3ttFHCoUCEydOhEKhkDpKoWB/dRv7q9vYX92mb/0tbDp/EQgRERERadLPS1+IiIiI9BgLQCIiIiI9wwKQiIiISM+wACQiIiLSMywACcDrYe169OiB4sWLw9TUFBUrVsSpU6ekjlUgsrKyMGHCBHh6esLU1BReXl744YcfdOqq8EOHDiE4OBjOzs6QyWTYtm2bxnwhBL777js4OTnB1NQUjRs3xo0bN6QJmw/e1l+lUomvv/4aFStWhLm5OZydndGrVy88fPhQusAf6V2f778NGDAAMpkMc+bMKbR8+S0v/b169So++eQTWFtbw9zcHNWrV0dsbGzhh80H7+rvixcvMHjwYLi4uMDU1BS+vr5YtGiRNGHzwbRp01C9enVYWlrCwcEBbdu2RXR0tEab9PR0DBo0CMWLF4eFhQU6dOiAx48fS5RYN7AAJDx79gy1a9eGXC7H7t27ceXKFcycORPFihWTOlqBmD59OkJDQ/Hrr7/i6tWrmD59OmbMmIH58+dLHS3fpKamws/PDwsWLMh1/owZMzBv3jwsWrQIJ06cgLm5OZo1a4b09PRCTpo/3tbftLQ0nDlzBhMmTMCZM2ewZcsWREdH45NPPpEgaf541+ebbevWrYiMjISzs3MhJSsY7+rvrVu3UKdOHZQtWxYRERG4cOECJkyY8N6jO2mLd/V35MiR2LNnD1avXo2rV69i+PDhGDx4MHbs2FHISfPHwYMHMWjQIERGRmLfvn1QKpVo2rQpUlNT1W1GjBiBP/74Axs3bsTBgwfx8OFDtG/fXsLUOkCQ3vv6669FnTp1pI5RaFq1aiX69u2rMa19+/aie/fuEiUqWADE1q1b1Y9VKpVwdHQUP//8s3paUlKSUCgUYu3atRIkzF//7W9uoqKiBABx9+7dwglVgN7U3/v374uSJUuKS5cuCXd3dzF79uxCz1YQcutvly5dRI8ePaQJVMBy62/58uXF999/rzGtatWqYvz48YWYrODEx8cLAOLgwYNCiNfbJ7lcLjZu3Khuc/XqVQFAHD9+XKqYRR73ABJ27NiBatWqoVOnTnBwcECVKlXw+++/Sx2rwNSqVQv79+/H9evXAQDnz5/HkSNH0KJFC4mTFY6YmBg8evQIjRs3Vk+ztrZGjRo1cPz4cQmTFZ7k5GTIZDLY2NhIHaVAqFQq9OzZE6NHj0b58uWljlOgVCoV/vzzT5QuXRrNmjWDg4MDatSo8dbD4kVdrVq1sGPHDjx48ABCCBw4cADXr19H06ZNpY6WL5KTkwEAtra2AIDTp09DqVRqbLPKli0LNzc3vdlmFQQWgITbt28jNDQUPj4+2Lt3LwYOHIihQ4dixYoVUkcrEGPHjkXXrl1RtmxZyOVyVKlSBcOHD0f37t2ljlYossfFLlGihMb0EiVKqOfpsvT0dHz99dfo1q1bgQwwrw2mT58OIyMjDB06VOooBS4+Ph4vXrzATz/9hObNm+Ovv/5Cu3bt0L59exw8eFDqeAVi/vz58PX1hYuLC4yNjdG8eXMsWLAA9erVkzraR1OpVBg+fDhq166NChUqAHi9zTI2Ns7xg01ftlkFRWeHgqO8U6lUqFatGqZOnQoAqFKlCi5duoRFixahd+/eEqfLfxs2bMCaNWsQHh6O8uXL49y5cxg+fDicnZ11sr/0P0qlEp07d4YQQj1euK45ffo05s6dizNnzkAmk0kdp8CpVCoAQJs2bTBixAgAQOXKlXHs2DEsWrQI9evXlzJegZg/fz4iIyOxY8cOuLu749ChQxg0aBCcnZ019pIVRYMGDcKlS5dw5MgRqaPoPO4BJDg5OcHX11djWrly5YrsFXTvMnr0aPVewIoVK6Jnz54YMWIEpk2bJnW0QuHo6AgAOa6ge/z4sXqeLsou/u7evYt9+/bp7N6/w4cPIz4+Hm5ubjAyMoKRkRHu3r2LUaNGwcPDQ+p4+c7Ozg5GRkZ6sw17+fIlvvnmG8yaNQvBwcGoVKkSBg8ejC5duuCXX36ROt5HGTx4MHbu3IkDBw7AxcVFPd3R0RGZmZlISkrSaK/r26yCxgKQULt27RyX3F+/fh3u7u4SJSpYaWlpMDDQ/OobGhqq9yToOk9PTzg6OmL//v3qaSkpKThx4gQCAwMlTFZwsou/Gzdu4O+//0bx4sWljlRgevbsiQsXLuDcuXPqf87Ozhg9ejT27t0rdbx8Z2xsjOrVq+vNNkypVEKpVOrUNkwIgcGDB2Pr1q34559/4OnpqTHf398fcrlcY5sVHR2N2NhYnd1mFQYeAiaMGDECtWrVwtSpU9G5c2dERUVh8eLFWLx4sdTRCkRwcDCmTJkCNzc3lC9fHmfPnsWsWbPQt29fqaPlmxcvXuDmzZvqxzExMTh37hxsbW3h5uaG4cOH48cff4SPjw88PT0xYcIEODs7o23bttKF/ghv66+TkxM6duyIM2fOYOfOncjKylKfN2RrawtjY2OpYn+wd32+/y1w5XI5HB0dUaZMmcKOmi/e1d/Ro0ejS5cuqFevHho0aIA9e/bgjz/+QEREhHShP8K7+lu/fn2MHj0apqamcHd3x8GDB7Fy5UrMmjVLwtQfbtCgQQgPD8f27dthaWmpXj+tra1hamoKa2tr9OvXDyNHjoStrS2srKwwZMgQBAYGombNmhKnL8IkvgqZtMQff/whKlSoIBQKhShbtqxYvHix1JEKTEpKihg2bJhwc3MTJiYmolSpUmL8+PEiIyND6mj55sCBAwJAjn+9e/cWQry+FcyECRNEiRIlhEKhEI0aNRLR0dHShv4Ib+tvTExMrvMAiAMHDkgd/YO86/P9r6J+G5i89Hfp0qXC29tbmJiYCD8/P7Ft2zbpAn+kd/U3Li5O9OnTRzg7OwsTExNRpkwZMXPmTKFSqaQN/oHetH4uX75c3ebly5fiyy+/FMWKFRNmZmaiXbt2Ii4uTrrQOkAmhA4Nf0BERERE78RzAImIiIj0DAtAIiIiIj3DApCIiIhIz7AAJCIiItIzLACJiIiI9AwLQCIiIiI9wwKQiIiISM+wACTSEX369CmyI3lIzcPDA3PmzHlrm0mTJqFy5cqFkkeX7d+/H+XKlUNWVla+Lnfs2LEYMmRIvi6TSJexACQqBH369IFMJoNMJoNcLoenpyfGjBmD9PR0qaOp5bXAmTRpEmQyGZo3b55j3s8//wyZTIagoKD8D/gOL168gFwux7p16zSmd+3aFTKZDHfu3NGY7uHhgQkTJgAATp48ic8//1w9TyaTYdu2bQUdOVdSfQ537tyBTCbDuXPn8h72A4wZMwbffvstDA0NAQBhYWGwsbHRaHP16lW4urqiU6dOyMzMzNNyv/rqK6xYsQK3b9/O78hEOokFIFEhad68OeLi4nD79m3Mnj0bv/32GyZOnCh1rA/i5OSEAwcO4P79+xrTly1bBjc3N0kyWVhYoFq1ajnGf42IiICrq6vG9JiYGNy9excNGzYEANjb28PMzKwQ0+YPbfwcAECpVOY6/ciRI7h16xY6dOjwxueePHkSdevWRfPmzbF+/fo8j9VsZ2eHZs2aITQ09IMyE+kbFoBEhUShUMDR0RGurq5o27YtGjdujH379qnnZ2RkYOjQoXBwcICJiQnq1KmDkydPaizj8uXLaN26NaysrGBpaYm6devi1q1bub7eyZMnYW9vj+nTpwMAkpKS0L9/f9jb28PKygoNGzbE+fPnAbzeCzN58mScP39evacyLCzsjX1xcHBA06ZNsWLFCvW0Y8eOISEhAa1atcqRo0mTJrCzs4O1tTXq16+PM2fOqOcLITBp0iS4ublBoVDA2dkZQ4cOVc9fuHAhfHx8YGJighIlSqBjx45vzNWgQQONQu/q1atIT0/HwIEDNaZHRERAoVAgMDAQgOYhYA8PDwBAu3btIJP9Xzv3G9PU+fYB/AullFJatghksiEo/7KRwSRCVYRaZ9dEx2ZcUDe2kOF4MTfghWFjyjZHJJtsxumyZY5gpwyUOCjbSCCOFQj+QwEBUWw6wJHNKlEXoVAGyPV7QTjPCoWWPOHn86TXJ+HFOfd9n153b/5cXOfcdROOp5WUlCAkJAS+vr7YsWMHhoaGhDZHa2iv2lVVVQU3NzehfbHWYXJyEvn5+XjqqacgkUjw3HPPoba2Vmhfvnw5AGDlypU21UNH46Yrh+Xl5VCpVPDy8kJpaandeE+dOgWNRgMvLy+77QaDARs2bMDOnTtRVFQEd/f/+RO1f/9+BAQEQC6X46233kJubu6sSmlycvKsCjBjzD5OABl7BLq6unD+/Hmb6sZ7772HiooKHD9+HG1tbQgLC4NWq8X9+/cBAH/99ReSkpIgkUhgMBjQ2tqK9PR0TExMzLq+wWCARqNBQUEB3n//fQBASkoKBgYGUFNTg9bWVsTGxuL555/H/fv3sX37duzevRtRUVEwm80wm83Yvn37vHNIT0+3SU6OHTuG1NTUWRWboaEhpKWl4ezZs7h48SLCw8OxadMmIXGqqKgQKqImkwlVVVV49tlnAQAtLS3IyspCfn4+jEYjamtrkZSUNGdMarUaRqMRZrMZAFBfX49169Zhw4YNNglgfX091qxZYzcRmU7YdDodzGazTQLX09ODqqoqVFdXo7q6Go2Njfjss8+Edkdr6MhirsPhw4dx8OBBfPHFF+js7IRWq8VLL70Ek8kEALh06RIAoK6uDmazGZWVlU6Nm5abm4vs7Gx0d3dDq9XajbWpqQmrVq2y26bX67F582bk5eUJ/7RMKy0tRUFBAQ4cOIDW1lYsW7bMbqUvPj4ef/7556zb/YwxO4gxtujS0tJIJBKRTCYjiURCAMjd3Z1+/PFHIiKyWCwkFouptLRUGDM2NkaBgYFUWFhIREQffPABLV++nMbGxuZ8jZdffpkqKyvJx8eHTp06JbQ1NTWRQqGg0dFRmzGhoaF09OhRIiL6+OOPKSYmxuFcpvuNjY1RQEAANTY2ksViIblcTh0dHZSdnU0qlWrO8Q8fPiS5XE6//PILEREdPHiQIiIi7M6roqKCFAoFDQ4OOoyLiGh4eJg8PT2prKyMiIhSUlKosLCQxsfHSSaTUW9vLxERLVu2jD755BNhXHBwMB06dEg4BkB6vX7WvL29vW1iycnJIaVSSUTOraFOpyNfX1+b6+r1evr3r+LFWofAwEAqKCiwuUZcXBzt2rWLiIj6+voIAF25csWmj7PjvvzyS4cx+/r60okTJ2zO6XQ6EolEJBKJ6MMPP7Q7TqlU0jvvvGNzLiEhYdb79ODBAwJADQ0NDmNhzNVxBZCx/xK1Wo329nY0NzcjLS0Nb775pvAsVE9PD8bHx5GQkCD0F4vFiI+PR3d3NwCgvb0diYmJEIvFc75Gc3MzUlJSUFJSYlM56ujogMViwZIlS+Dj4yN89fX1zXkL2RGxWIzXX38dOp0Op0+fRkREBKKjo2f1u3PnDjIyMhAeHg5fX18oFApYLBb09/cDmKpMWq1WrFixAhkZGdDr9UJVU6PRIDg4GCtWrMAbb7yB0tJSjIyMzBmTt7c34uLihGpfY2Mj1q9fDw8PD6xduxYNDQ3o7e1Ff38/1Gr1guccEhICuVwuHC9duhQDAwMAnFvDxeDMOgwODuLWrVs2sQFAQkLCvLEtZNxclb1/s1qtdquuUqkUGo0GRUVFduMxGo2Ij4+3OTfzePo6AOb9HmGMTfF41AEw5ipkMhnCwsIATN2mi4mJQXFxMXbu3OnU+Ok/bvMJDQ3FkiVLcOzYMWzevFlIFi0WC5YuXTprgwSAWc+kLUR6ejqUSiW6urqQnp5ut09aWhru3buHw4cPIzg4WHj2bnp3Z1BQEIxGI+rq6vDrr79i165d+Pzzz9HY2Ai5XI62tjY0NDTgzJkz+Oijj7Bv3z5cvnx5zrjVajXKy8tx7do1WK1WxMbGAgBUKhXq6+sxOTkJb29vKJXKBc93ZvLt5uaGyclJp8e7u7uDiGzOzbVhYiGcWYfFJpPJHPbx8/PD33//Peu8SCRCVVUVtm7dCrVajfr6ejz99NMLjmH6Vru/v/+CxzLmargCyNgj4O7ujj179iAvLw9WqxWhoaHw9PTEuXPnhD7j4+O4fPkynnnmGQBAdHQ0mpqa5k0Y/Pz8YDAY8Pvvv2Pbtm1C39jYWNy+fRseHh4ICwuz+fLz8wMAeHp6Lviz2aKiohAVFYWuri689tprdvucO3cOWVlZ2LRpE6KioiCRSHD37l2bPlKpFMnJyThy5AgaGhpw4cIFXL16FQDg4eGBjRs3orCwEJ2dnbh58yYMBsOcManVaphMJpSVlWHdunXCx40kJSWhsbERDQ0NSEhImHd3qVgsXvB74cwa+vv7Y2hoCMPDw0KfmR+7shjroFAoEBgYaBMbMLU207FNvx//fm1nxi3EypUrcf36dbttEokElZWViIuLg1qttukXGRk5a0PUzGNg6tlasViMqKioBcfGmKvhBJCxRyQlJQUikQhff/01ZDIZ3n77beTk5KC2thbXr19HRkYGRkZGhArhu+++i8HBQezYsQMtLS0wmUwoKSmB0Wi0uW5AQAAMBgNu3LiBV199FRMTE9i4cSPWrFmDLVu24MyZM7h58ybOnz+PvXv3oqWlBcDU7c2+vj60t7fj7t27+Oeff5yah8FggNlsnrMiFx4ejpKSEnR3d6O5uRmpqak21czvv/8excXF6OrqQm9vL3744QdIpVIEBwejuroaR44cQXt7O/744w+cOHECk5OTiIyMnDOetWvXQiKR4KuvvoJKpRLOx8fHY2BgAD/99JPD278hISH47bffcPv2bbsVK3ucWUOlUglvb2/s2bMHPT09KCsrm7XLd7HWIScnBwcOHEB5eTmMRiNyc3PR3t6O7OxsAFPfN1KpFLW1tbhz5w4ePHjg1LiF0Gq1OHv27JztEokEFRUVUCqVUKvVuHbtGgAgMzMTxcXFOH78OEwmE/bv34/Ozk5h9/S0pqYmJCYmOlUtZ8zlPeqHEBlzBdMbNGb69NNPyd/fnywWC1mtVsrMzCQ/Pz+SSCSUkJBAly5dsunf0dFBL7zwAnl7e5NcLqfExETq6emx+xq3bt2iiIgI2rZtG01MTNDg4CBlZmZSYGAgicViCgoKotTUVOrv7yciotHRUXrllVfoscceIwCk0+nszsXRJoWZmw/a2tpo1apV5OXlReHh4XT69GmbTRd6vZ6USiUpFAqSyWS0evVqqqurI6KpzSsqlYoef/xxkkqlFB0dTeXl5fO/2USkUqkIAF28eNHm/Pr16wkAXbhwweb8zE0gP//8M4WFhZGHhwcFBwfPOe9Dhw4J7UTk1Brq9XoKCwsjqVRKL774In333Xc2m0AWax0ePnxI+/btoyeffJLEYjHFxMRQTU2NzZiioiIKCgoid3d3YayjcXNtHrHn3r175OXlRTdu3BDO2dsYMzY2Rlu2bCF/f3+6evUqERHl5+eTn58f+fj4UHp6OmVlZdHq1attxkVGRtLJkycdxsEYI3IjmvFACmOMMbZIcnJyMDg4iKNHj/6vrqPRaPDEE0+gpKQEAFBTU4Pdu3ejs7MTHh78eDtjjvBPCWOMsf+avXv34ptvvsHk5KTNBz3PZ2RkBN9++y20Wi1EIhFOnjwpbBqaNjw8DJ1Ox8kfY07iCiBjjLH/06xWK5KTk3HlyhWMjo4iMjISeXl52Lp166MOjbH/tzgBZIwxxhhzMbwLmDHGGGPMxXACyBhjjDHmYjgBZIwxxhhzMZwAMsYYY4y5GE4AGWOMMcZcDCeAjDHGGGMuhhNAxhhjjDEXwwkgY4wxxpiL4QSQMcYYY8zF/AdA9V/PjALdlAAAAABJRU5ErkJggg==", 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", 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\n", " " ], @@ -1738,7 +2199,7 @@ "'Function from R1 to R1 : (Rocket Mass without motor (kg)) → (Out of Rail Speed (m/s))'" ] }, - "execution_count": 19, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -1766,7 +2227,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 22, "metadata": { "colab": {}, "colab_type": "code", @@ -1787,7 +2248,7 @@ "output_type": "stream", "text": [ "Simulating Rocket with Static Margin of 0.113->0.846 c\n", - "Current Simulation Time: 4.7317 s\r" + "Current Simulation Time: 3.9139 s\r" ] }, { @@ -1797,21 +2258,7 @@ "rocketpy - INFO - Simulation completed at time: 5.0000 s\n", "rocketpy - INFO - Starting flight simulation of 'Flight'.\n", "rocketpy - INFO - Entering flight phase 'initial_phase' at t=0.000 s.\n", - "rocketpy - INFO - Entering flight phase 'free_flight' at t=0.414 s.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Simulating Rocket with Static Margin of 1.064->1.796 c\n", - "Current Simulation Time: 2.4160 s\r" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ + "rocketpy - INFO - Entering flight phase 'free_flight' at t=0.414 s.\n", "rocketpy - INFO - Simulation completed at time: 5.0000 s\n" ] }, @@ -1819,6 +2266,7 @@ "name": "stdout", "output_type": "stream", "text": [ + "Simulating Rocket with Static Margin of 1.064->1.796 c\n", "Current Simulation Time: 5.0000 s\r" ] }, @@ -1836,7 +2284,7 @@ "output_type": "stream", "text": [ "Simulating Rocket with Static Margin of 2.014->2.747 c\n", - "Current Simulation Time: 2.6827 s\r" + "Current Simulation Time: 3.2246 s\r" ] }, { @@ -1845,7 +2293,8 @@ "text": [ "rocketpy - INFO - Simulation completed at time: 5.0000 s\n", "rocketpy - INFO - Starting flight simulation of 'Flight'.\n", - "rocketpy - INFO - Entering flight phase 'initial_phase' at t=0.000 s.\n" + "rocketpy - INFO - Entering flight phase 'initial_phase' at t=0.000 s.\n", + "rocketpy - INFO - Entering flight phase 'free_flight' at t=0.414 s.\n" ] }, { @@ -1853,44 +2302,29 @@ "output_type": "stream", "text": [ "Simulating Rocket with Static Margin of 2.964->3.697 c\n", - "Current Simulation Time: 0.1101 s\r" + "Current Simulation Time: 0.9850 s\r" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "rocketpy - INFO - Entering flight phase 'free_flight' at t=0.414 s.\n" + "rocketpy - INFO - Simulation completed at time: 5.0000 s\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Current Simulation Time: 3.0012 s\r" + "Current Simulation Time: 5.0000 s\r" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "rocketpy - INFO - Simulation completed at time: 5.0000 s\n", "rocketpy - INFO - Starting flight simulation of 'Flight'.\n", - "rocketpy - INFO - Entering flight phase 'initial_phase' at t=0.000 s.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Simulating Rocket with Static Margin of 3.914->4.647 c\n", - "Current Simulation Time: 0.3145 s\r" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ + "rocketpy - INFO - Entering flight phase 'initial_phase' at t=0.000 s.\n", "rocketpy - INFO - Entering flight phase 'free_flight' at t=0.414 s.\n" ] }, @@ -1898,7 +2332,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Current Simulation Time: 2.7757 s\r" + "Simulating Rocket with Static Margin of 3.914->4.647 c\n", + "Current Simulation Time: 3.5445 s\r" ] }, { @@ -1918,18 +2353,18 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "4b21b471b5fb4404b44e00d379fd723d", + "model_id": "440f6fbdbce6448397d9e196a4332032", "version_major": 2, "version_minor": 0 }, - "image/png": 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mQ/vkuUIIIXJHEkAhxGMURcGvuSddRtfC0taUqNAU/rHuQsbbn4NGQ9yu3ST3eZepjoOY1nQaDmYOBEQF0PPPnsw8NZM0XQZU6QrvHIdXpoGFI0pEAPVuzkS9/i2IuJFlv2YmajpVd2fjiEZsGtGQjtXdUasUjt6MZOiqU7Sevp9fTt4lTe4TFEKIPJMEUAiRJbdydnT/pC4u3rakJKZzwN+F1AlL0ZQpTfqDB9zu25f6J+LY3GUzr3i/glanZcnFJfT+qzeBMYFgYgr1hsH7Z8lo8B5aRY3qxm74qb5+oUhq4lP7rlnagdlv1eTguBa83awcdhYaboUnMO7X8zT/bh+rjgSSnJZReN8MIYQoYiQBFEI8lZW9GV1H16JSPVd0Wh2H9sQSMexHrFu3gbQ0gid+Tso3PzK1/mRmNJ+BnZkdlyMu0/2P7vzm/xs6nQ7M7dC2nMieyl+jLdsCMlL1C0Xm1gP/Hc/s393ego9fqcw/H7fkk/aVKWFtRlB0EhN+v0STaXtZeOAGCSnpz2xDCCHEkyQBFEI8k1qjolV/H2q/XAaAkzuDCHhpOE7vjwJFIXr9em73H0Bz29r81vE36rnWIyk9iS+OfMGY/WOIT9WvBE4wdyOjxy/QfZW+gHTMHVjbHX4bklk25mmszUwY2rQchz5qwVedq+BuZ05YXApf/3mVptP2svTQLVLSZUZQCCGySxJAIcRzKYpC/S7laNazEooCVw4/4JTSEPe5P6GysSHp9GkCe/TAITyZhW0X8kHtDzBRTNh1exdvbXuLG9E3HjUEvp3gnWPQ4F1QVHDhF5j7ElzalOUikX8z16jp28CLfWNbMO31apRxsiQiIZWv/rhMy+/388uJu1JLUAghskESQCFEtlVt6sHLw/xQmSjcPBvGwSsOeK5ei8bDg7Tbdwjs8RYp584zsOpAVryyAhdLFwJjA+m7sy8XUi/8vyFTK2g3BQbthpI+kBAGG/rDL30gIfy5cZiaqOhetxS7Rzfj665+uNjqLw2P++08bWceYNv5B2hl1bAQQjyVJIBCiBwpW6MkHUZUQ61RcftCBH9vj8Nj5RrMq1QhIyqK2/36E7trF9VKVmP9q+szLwmvT1zPD6d+IP3fNQE9a8Ow/dDsI1CZwJWtMK8hXN+drVg0ahU965Vm/9gWfNreBwdLDTfDEnhn7Wm6zjvMqdtRBfRdEEKIF5skgEKIHCvt60THd6tjYqbm7pUodqwPwn3xUqybNUOXkkLQe+8T/dtGnCycmN9mPgN8BwCw5toaRu4ZmXlfIAAmZtDiExiyF0pUgvgQWP06/PUxpGVvRxBzjZohTctyYFwL3m9VAUtTNefuRvP6vMOMXHeGe1FPX3EshBDFkSSAQohc8ajkQKeR1dGYqwnyj2bnqpu4zfwR+ze6gU7Hg08/JXLNGkxUJoysMZIelj0wV5tzKOgQff7qQ1B80OMNulXTzwa+NFT/9bF5sKgFhFzKdkw25ho+aFORfR82p3sdTxQFtp67T6sf9vP9jmuyYlgIIR6SBFAIkWtu5e31M4GmKu5cjmTXyms4f/4Fjv36AhAyaTIRixcDUNW0KotbL6akRUmuR1+n57aenA09+3iDGgto/x303ABWJSH0MixsAadX5iguZ1tzpnWrztZ3G1O/rCMp6Vrm7L1Oi+/3senMPX15GiGEKMYkARRC5IlbeXvav11NvzDkTBh7V1+j5LiPcBr+NgCh3/9AxNy5oNPh6+TL2g5rqexYmcjkSAbtGMSeO3uebLRiWxh+BCq0hYwU2DISNr/zzOLRWanqYce6IfVZ0Kc2ZZwsCY1L4YP15+i56BjXQ+Of34AQQhRRkgAKIfKslK8j7QZXRVEpXDsazKFfr1Pyvfco+cEHAETNX4Dj7r8BcLVyZcXLK2ju2ZxUbSof7PuATQGbnmzUuiS8tR5aTdSXizm7Gpa0eepWck+jKArtqriy84OmjG1XCTMTFUduRvDKrAN8t+MqSalSP1AIUfxIAiiEyBdla5SkVT8fAC7svce5v+9SYthQnD/+CIASu3cTtXQpAJYaS2a0mEGX8l3Q6rRMPDyRJReWPHlpVqWCJmOg7+/6S8IhF2Fhc/1q4RwyM1HzTovy7B7djFaVnUnL0DF37w3azNjP31dC8jR2IYR40UgCKITIN5XqudLwtfIA/PPrda6fCsWpf3+c3n8fgIgZM4lctRoAE5UJXzX8ioFVBwIw8/RMvjv5HVpdFoWcvZvCsINQugGkxML63rD3G9DmvOhzKUdLFverw4I+tXG3M+deVBKDVpzknbWnCY9PyeXIhRDixSIJoBAiX9VoUwq/Zh4A7F52mQfXo3EYPIiIVi0BCJkyhagNGwD95dkPan/Ah3U+BGDV5VV8fvhzMrRZXJa1dYN+W6H+O/qv938Lv/aH1IQcx/josvDuMc0Y1qwsapXCtvMPaDN9P7+fDZJFIkKIIk8SQCFEvlIUhcZvVsSrWgky0rVsm3eemLAkItq0wf7h6uDgiZ8Tu3175jn9qvTj68Zfo1bUbL6+mQn/TMg6CVRr4OWvofNcUGng8u+w9GWIuZerWC1NTRj/ig+/v9OIyq42RCWm8f7PZxmy8iTBMdmrQSiEEC8iSQCFEPlOpVJoO7gKzl62pCSks2PhJbTpCk5jxmD/Vg/Q6bg/dhwJx49nntOxXEemNp2KWlGz9eZWPjn0yeO7hvxbzd7Q/w/9fYHB5/WlYu4ez/rYbKjqYceWdxszuk1FNGqF3VdCaTNjP+tP3JHZQCFEkSQJoBCiQGhM1bQf7oeVvRnRwUlEnrcAHbh+9hk2bdqgS0vj3jvvknzNP/Ocdl7t+K7Zd5goJvx560/GHxz/9CSwdH0Ysgdc/CAhFJa/qp8RzCVTExXvtarAHyObUN3TjrjkdD767QKDV5wkLE7uDRRCFC2SAAohCoyVnRmvvO2H2kQhOdSEE9tuo6jVuH83DYvatdHGxXF36FDSHjzIPKdNmTb80PwHTFQmbA/czrgD456eBNqXhoHboVIHfb3AX/rB0Xl5irmSqw0bRzTik/aVMVWr+PtqKO1mHmDnpeA8tSuEEMZEEkAhRIFy8bKlac+KAJzdeZeAkyGozM0p9dNcTMuXIz0khDtDhpARE5N5TsvSLZnRfAYalYZdt3cx8Z+JWa8OBjCzhjdXQd3BgA62fww7Ps3VCuFH1CqFoU3LsWWk/t7AyIRUhq46xUe/nidetpMTQhQBkgAKIQpchbrOWHunArBn5RUi7sejtrOj9KJFmLi4kHr9BkEffIAuLS3znOalmvNDsx8y7wn8+tjXT78fT6WG9t9D6y/0Xx+ZA78NgvS8Xbqt7GrL7+82YlizsigKrD95l1dmHeBkYGSe2hVCCEOTBFAIUSjsKqXgUcme9FQtOxZeJDU5HY2bG6UWzEextCTh8BFCvvn2sXNalG7BlMZTUFBYf209M07PeHoSqCjQ+APoulC/QvjSRlj1GiTH5iluMxM141/xYd2Q+njYW3A3MonuC44w++8AMrSyQEQI8WKSBFAIUSgUBVr2q4SVnSlRwYnsX3sNnU6HeeXKeEybCkDU2rVErVv32HkdynZgQoMJACy7uIzFFxY/u6Pqb0LvX8HUBm4fgpWdIDHvM3b1yzrx16gmdK3pgVYHP+zyp+/SY4TGSbkYIcSLRxJAIUShsbAxpe3DPYP9j4dw+dB9AGxat6bk6NEABE+eQsKRI4+d90bFNzKLRf945kfWXX08SXxC2eb6MjEWjnD/DCxrD3F5X8Rha65hxps1+P6N6lho1PxzPYL2sw5yKCA8z20LIURhkgRQCFGo3CvYU79zWQAOrg8g7E4cAE5DBmPXuRNkZHDv/VGkBgY+dl6/Kv0YXn04AN8c+4a/b//9nI5qwIC/wMYNwq7oC0ZH3c6XMXSr7cnWhwtEwuNT6bP0GN/vuEZ6Ru4XngghRGGSBFAIUehqtimNl58TGelati/S3w+oKAquX32FRY0aaGNjuTfyPbSJiY+dN7z6cN6o+AY6dHx08CPOhp59dkfOlfVJoH0ZiLoFy16B8IB8GUN5Zxs2v9OInvVKo9PBnL3XeWvRUdlBRAjxQpAEUAhR6BSVQqv+vlg7mhEblsShX/RJmcrMDI8fZ6EuWYKUgAAeTPz8sUUfiqLwSb1PaO7ZnJSMFN7d8y63Ym49uzNHb32twBKVIDZInwSGXs2XcZhr1Hzd1Y/Zb9XE2syEE4FRvDr7IMduySphIYRxkwRQCGEQ5lYaWvf3BQWuHH7AjdOhAGicnfGcMQPUamL/+IOo1WseO89EZcLUplPxK+FHTEoMw3cPJzzpOffg2brrZwJdq0FCGKx4Nd+SQICO1d3Z9l7jzEvC/ZafYu99RbaRE0IYLUkAhRAG41HRgVptywCwd/VV4qP0dfss69TBZdxYAEKmTiXx9OnHzrPUWDKn1RxK25QmKD6Id/5+h8S0xy8XP8HKCfr+XmBJYBknKzaNaETXmh5kaHVsvq3mg18ukCCFo4UQRkgSQCGEQb3U0ZuSpW1ISUzn7xWX0T2srefQty+27V+B9HSC3h9FeljYY+c5mjsyr/U8HM0duRxxmU8Pffr03UIesXT8TxLYMV+TQAtTNdO7V2dih8qoFB3bLgbT9ad/uBWekG99CCFEfpAEUAhhUGoTFW0G+mJiquLe1SjO7bkL6O/3c5s0Sb9dXFgY9z/6CN1/tncrbVuaWS1moVFp2H1nN3PPzn1+h5lJoB8khOZ7EqgoCn3ql2akbwYlrU3xD4mn0+xD7L4ckm99CCFEXkkCKIQwOAdXKxp1qwDAkc03iLgfD4DKygrPH39EsbAg4fARIhYveeLcGs41+KLhFwAsPL+QP2/++fwOLR2h75bHk8CIG/k2HoCytrB5RAPqejkQl5LOkFUnmbfvhtwXKIQwCpIACiGMQpUm7pTxc0KbrmPPyqtoH9bUMytbFtfPPgUgbNYsEs+ceeLcTuU6MaDqAAAm/DOB82Hnn9/hoyTQpao+CVzZBWLu5dt4AJxtzFg7pD696+tLxUzdfpUxv5wjOS0jX/sRQoickgRQCGEUFEWhec/KmFqYEBoYy9nddzPfs3vtNWw7dICMDO6P+ZCM2Cf3932/5vs092xOqjaV9/e+T3BCNnb+sHSEPpvAqTzE3NEngfFhzz0tJzRqFZO7+DGpcxXUKoWNZ4J4a9FR2UJOCGFQkgAKIYyGtYMZjd/QXwo+vvUWkQ/0iycURcH1yy/QlCpF2v37PPhswhOXUtUqNd82/ZYKDhUITwpn1N5RpGSkZKNTZ+izGWw9ISIAVneFpOh8Hhn0aeDFigEvYWtuwpk70XSe8w8Xg2LyvR8hhMgOSQCFEEalcgNXSlfR7xKyZ+UVtA9XBautrfGY/gOYmBC3cyfR63954lwrjRWzW87GzsyOSxGX+ObYN9nr1L6UfmGIVUkIvgBru0Nq/q/cbVyhBL+/25iyJa14EJPMG/OP8NeFB/nejxBCPI8kgEIIo6IoCi16V8LUXE3IrVjO/etSsIWfH86jRwMQ8s03pNy8+cT5HtYeTGsyDQWF3wJ+4zf/37LXcYny+plAczu4ewx+7gXp2ZhBzCHvEvp6gU0qlCApLYPha06zYL8sDhFCFC5JAIUQRsfawZxGDy8FH9tyk+iQ/xd5duzfD6uGDdGlpHD/o4/RpT9ZaLmhR0NG1hwJwJRjU7gYfjF7HbtWhV6/gcYKbu6FTW+D9jm1BXPBzkLDsv516d/QC4Bv/rrKxN8vkZ6R/30JIURWJAEUQhgln4ZulPJxICNdy/511zJnyBSVCrevp6CytSX5wgXCFy7M8vxBfoNoXqo5ado0Ptj3AVHJUdnruFRd6LEGVBq4tBF2TcivIT3GRK3ii05VmPCqL4oCq47eZtiqUySmys4hQoiCJwmgEMIoKYpCs56VUGv0BaL9j/+/kLLG1RXXCfrELPyneSRdvPTE+SpFxdeNv6aMbRmCE4IZd2AcGdpsll8p1wK6/KR/fmQOHJ6T5/E8zaDG3vzUsxZmJir+vhrKmwtkhbAQouBJAiiEMFp2JS2p28ELgH9+DSA5IS3zPdtXO2Dz8suQns79jz5Cm/xk0mRjasOM5jOwMLHg6IOj2dsp5JFq3aHNV/rnOz+FC7/mZSjP9IqfG2uH1MfRypQLQTF0nXuY66FxBdafEEJIAiiEMGo1WpfGwc2KpLg0jmy8nvm6oii4fj4RdckSpN64QdjMWVmeX8GhAl82/BKAxRcWc/j+4ex33vA9qPe2/vnm4XDrQK7H8Ty1yziwcXhDvJwsCYpO4rWfDnPkRkSB9SeEKN4kARRCGDW1iYrmvSoBcPmfB9wPiM58z8TBAffJkwGIXLGChGPHs2zjFe9XeKPiG+jQMf7geMKTwrPXuaJAu6/BtzNkpOpXBgdnc0FJLniVsGLjiEbUKm1PbHI6/ZYeZ/tFKRMjhMh/kgAKIYyee3l7fBu7A7BvzVUy0v+/Wta6WTPsu3cHnY4HEyagTUrKso1xdcdRwaECkcmRfHzw4+zfD6hSQ9eFUKYRpMTqawTGZWOXkVxytDJl7ZD6tPV1ITVDy4g1p1l3/E6B9SeEKJ4kARRCvBAadC2HhY2GqOBEzu25+9h7zuPGYuLqStqdO4T9ODvL881NzPm+6fdYmFhw7MExllxckv3ONeb6lcElKkJsEKzrAamJzz8vl8w1an7qVYs365RCq4PxGy8wd+91qRUohMg3kgAKIV4I5lYaGnQtD8DJbYEkRP+/SLPa2hq3L78A9JeCk86fz7KNsvZl+bTepwDMPTuXUyGnsh+AhQP0XA8WjnD/DGwaViA1Ah8xUav49nU/RjQvB8B3O64x6Y//74wihBB5IQmgEOKFUbm+Ky7etqSlZHB40/XH3rNu1gzbTh1Bq+XBp5+hS03Nso3O5TvTsWxHtDotHx34iOjk6OwH4Fj2/zUCr2yBvZPzMJrnUxSFcS9X5rMOPgAs/ecWYzacI00KRgsh8qjIJoAZGRlMmDABb29vLCwsKFeuHJMmTXrsEkp8fDzvvvsunp6eWFhY4Ovry/z58w0YtRDiWRSVQtMeFUEB/2MhPLge/dj7LuPHo3Z0JCUggPCFi57azmf1P8PL1ouQxBAmHp6Ys0urZRpCp4eXmQ/+AGfX5mIkOTO4SVmmd6+OWqWw6UwQQ1eeJCk1m/cwCiFEFopsAjh16lTmzZvHnDlzuHLlClOnTmXatGnMnv3/+4NGjx7N9u3bWb16NVeuXGHUqFG8++67bNmyxYCRCyGexbmMLb4N3QA4sN7/sUuiJg4OuE74DIDwBQtI9vfPsg1LjSXfNfsOE5UJe+/uZdP1TTkLosZb0GSM/vmW9+B2DkrL5NJrtTxZ1Lc25hoVe6+F0XfpMeKS055/ohBCZKHIJoCHDx+mc+fOdOjQAS8vL7p160bbtm05fvz4Y8f069eP5s2b4+XlxdChQ6levfpjxwghjE/9LuUwtTAh/G48lw/df+w9m5dfxrpVK0hL48FnE9BlZD1TVtmxMu/VfA+Ab49/y53YHK60bfGZvjyMNk1fHibyVq7GkhMtK7uwelA9bMxNOBEYRa/Fx4hOzPpStxBCPIuJoQMoKA0bNmThwoX4+/tTsWJFzp07x6FDh5g+ffpjx2zZsoWBAwfi7u7Ovn378Pf3Z8aMGVm2mZKSQkrK/288j42NBSAtLY20tPz9JP6ovfxu11jJeIu2/B6viblCnfalOfzbTY5uvkGZag6YW2ky3y/xyXgSjx8n+fx5Itauxa5HjyzbeavCWxy4e4CToSf56MBHLG2zFBNVDv5afHU26qjbqB6cRfdzT9L7/QWmVgX651vdw4ZVA+owYMUpzt+L4c0FR1jevzYlrM3yva/skp/noq0ojrcojSW3FF0RrSug1Wr55JNPmDZtGmq1moyMDKZMmcL48eMzj0lJSWHo0KGsXLkSExMTVCoVixYtom/fvlm2+cUXX/Dll18+8fratWuxtLQssLEIIZ6k00LIP5akx6uxKp2KQ5WUx963O3IEl82/k2FmRuDYD8mwscmynWhtNHNi55BMMi3NW9LSvGWO4jBPjaTZtc8xT48hyP4lTnq9oy8gXcDuJ8K8y2pi0xSczXW845uBveFyQCFeKImJifTs2ZOYmBhsbW0NHY5BFNkZwF9++YU1a9awdu1aqlSpwtmzZxk1ahTu7u7069cPgNmzZ3P06FG2bNlCmTJlOHDgAO+88w7u7u60bt36iTbHjx/P6NGjM7+OjY2lVKlStG3bNt9/gNLS0ti1axdt2rRBo9E8/4QXnIy3aCuo8d6vGM0fsy+QeM+U9n0a4OD6/w9iunbtuBdwnZRLl6h++gyuU799ajsOgQ58evhT9qfsp1/TfviV8MtRHMrdCuhWd8Ej+jiuDu1IqTuiUP58W0ck0HfZKR7EJLPolg0rB9SmlEPhfxiVn+eirSiO99EVvOKsyCaAY8eO5eOPP6bHw0s/fn5+3L59m2+++YZ+/fqRlJTEJ598wqZNm+jQoQMA1apV4+zZs3z//fdZJoBmZmaYmT35EVuj0RTYL0VBtm2MZLxFW36Pt0yVknhVK0Hg+XBObAmkwzvV/90Zbl9+SWD37sT/+Sep3V7HqmHDLNvpVKETh+4f4q/Av5hwZAIbOm7AUpODRKpsY2j/HfwxCvXeyZg6+z4MoWD/fCu42rPh7Qb0WnyM2xGJ9FpyktWD61GupHWB9fks8vNctBWl8RaVceRFkV0EkpiYiEr1+PDUajXah4VbH92396xjhBDGr+Fr5VBUCoEXIrh3NfKx9yyqVsGhZ08Agr/8Cm1KSlZNAPBp/U9xsXThTtwdvj/5fc4DqTMAag8AdKg3D8MquXD28PV0sOSXYQ0o72zNg5hk3lxwhKvBMrshhHi2IpsAduzYkSlTprBt2zYCAwPZtGkT06dPp2vXrgDY2trSrFkzxo4dy759+7h16xbLly9n5cqVmccIIYyfg6sVVZvo9wn+57fr6P6zU0bJ999DXbIEqbdvE7F48VPbsTOzY0rjKQBs8N/AkftHch7MK9OgVH2UlFjq3ZoFKXE5byMXXGzNWT+0Pr5utoTHp/LWwqNcvi9JoBDi6YpsAjh79my6devGiBEj8PHx4cMPP2TYsGFMmjQp85iff/6ZunXr0qtXL3x9ffn222+ZMmUKb7/9tgEjF0LkVN1XvTE1VxN+N55rx4Mfe09tY4PLxx8DELFgIam3bz+1nXpu9Xiz0psAfHH4CxLSEnIWiIkpdF+JzsYNm+T7qLeMKNDt4v7NydqMdUPrU72UPVGJafRaLEmgEOLpimwCaGNjw8yZM7l9+zZJSUncuHGDyZMnY2pqmnmMq6sry5YtIygoiKSkJK5evcro0aNRCmEFnxAi/1jYmFL7FS8Ajm6+Sdp/dsmwbd8eq4YN0aWmEvzVpGfu/DG69mg8rD24n3CfH07+kPNgbFzI6LaCDEWDyv8v+CfrslIFwc5Cw8qBL0kSKIR4riKbAAohipdqLT2xdjQjITqFc7sfL+qsKAquEyegaDQk/PMP8X///dR2LDWWfNXwKyD3l4J17rU479lH/8WeyXDrQI7byK3/JoE9JQkUQmRBEkAhRJFgolHToEs5AE7tuENi7OM7ZJh6eeE4cCAAId9OfeaCkJfcXsq8FPz54c+JT43PcTx3nJqhrfaWvmDhrwMh9v7zT8on/04CoyUJFEJkQRJAIUSRUaGOC85lbEhPyeDkX4FPvF9i6BBMnJ1Ju3ePyGXLn9nWo0vBDxIe8MOpXFwKVhQyXp4KLlUhIQw2DICMwtt9wM5Cw6pBjyeBl+7HFFr/QgjjJgmgEKLIUFQK9bvqZwEvHQgiNjzpsfdVVlY4j/0QgPAFC0gLDn6ijUf+fSn4V/9fc7cqWGMJ3VeCmS3cPQq7v8h5G3lga/54Ethr8TFJAoUQgCSAQogiplRlRzwrO6DN0HHij1tPvG/76qtY1KyJLimJ0O+fPbP330vBiWmJOQ/IqRx0+Un//MgcuPx7ztvIA0kChRBZkQRQCFHk1O+snwW8diyYiPuP37+nKAoun30KikLsH3+QePr0M9v696Xg2Wdm5y4gn47QcKT++eZ3IPx67trJpf8mgX2WHCcgpHBqFAohjJMkgEKIIsfF25ayNUui08Gx328+8b5FlSrYd+sGQPDkyegyMp445hFLjSUT6k8AYO3VtVwMv5i7oFp9AaUbQmoc/NIX0pKee0p+epQE+nnYEZmQSs/Fx7gVnsM6h0KIIkMSQCFEkVSvU1kUBW6dCyf41pOXPEt+MAqVjQ0pl68Q/dtvz2yrkUcjOpTtgFan5fPDn5OmzcViDrUJvLEMrEpC6CXY8UnO28gjW3P96uDKrjaExaXQa9FR7kbm4rK2EOKFJwmgEKJIcnSzolJ9V0BfHPq/TBwdKfnuOwCEzZhJRtyzL4mOrTMWOzM7/KP8WXlpZe6CsnGFrgv0z08uhUubc9dOHjhYmbJ6cD3KlbTifkwyvRYfIzgmudDjEEIYliSAQogiq+6r3qhMFIKuRXH3SuQT7zv07ImptzcZUVFELHr6PsEAThZOjK0zFoB55+ZxJ/bOM49/qvKtoPEH+udb3oOowNy1kwclrM1YM7g+pR0tuROZSM/FRwmLe3pdRCFE0SMJoBCiyLJ1sqBqUw8Ajm6+8cQWcIpGk1kWJnLFCtLuP7tYc6dynajvVp+UjBS+OvrVM7eUe6YWn4LnS5ASA78OKtT6gI+42pmzdkg9POwtuBmWQO/Fx4hKSH3+iUKIIkESQCFEkVb7ZS80ZmpCb8dx61z4E+9bt2iBZd266FJSCJs165ltKYrCxPoTMVebc+zBMbbc2JK7oNQa6LYEzO0g6CT8/VXu2skjTwdL1gyuh7ONGddC4uiz9BgxSYWfjAohCp8kgEKIIs3S1pRqLTwBOLHt1pOzgIqC87hxAMT8voWkS5ee2V4p21IMrzEcgO9OfkdEUkTuArMvDZ3m6J8f/hECdueunTzyKmHF2iH1cLIy5WJQLP2XHSc+Jd0gsQghCo8kgEKIIq9G69JozNSE343PchbQwq8qth07AhA6ddpzL+328e1DZcfKxKTEMP3U9NwH5tsJ6g7RP980DGIf5L6tPCjvbMOqQfWws9Bw5k40Q1acJDnt6aVxhBAvPkkAhRBFnrm15pmzgADOo95HMTUl8fhx4vfte2Z7GpWGCfUnoKCw5cYWToWcyn1wbSeDix8khsPGIaA1TOLl627LqkEvYW1mwpGbEYxcd4b0DK1BYhFCFDxJAIUQxUKN1qXRmD+cBTz75CygxsMDx359AQj97nt06c++DFqtZDVer/g6AJOPTs5dbUAAjbm+PqDGCgIPwj/Pvg+xIFXztGdR3zqYmqjYdTmEj367gFaby4UuQgijJgmgEKJY+Pcs4PFtt9Blkdg4DR2K2sGB1Js3id6w4bltvl/zfRzMHLgefZ21V9bmPrgSFaD9NP3zvVPg/pnct5VHDco5MbdnLdQqhd9O32PStsu5X+0shDBakgAKIYqNR7OAEffiuXku7In31TY2lHhUHHr2HDLi45845t/sze35oLa+pt9PZ38iOCE4D8H1Ap9OoE2H34ZAquF26Gjj68J33aoBsOyfQGbvKdy9i4UQBU8SQCFEsWFupaF6y1IAnPgjMMtZQIfu3TH18iIjMpLIpUuf22bn8p2pUbIGiemJfHfiu9wHpyjQcRbYuEFEAOz8NPdt5YPXannyeUdfAKbv8mfF4UCDxiOEyF+SAAohipXqrUphaq4mIiiem2efnAVUNBpKfqCf1YtYvoL0iGeXeVEpKj6r/xlqRc3O2zs5HHQ498FZOkKXefrnJ5fCtb9y31Y+GNDIm1GtKwDw+ZZLbD4TZNB4hBD5RxJAIUSxYm6lodqjWcCn3Ato07YN5lWroktMJHz+gue2WcmxEm9VfguAKcemkJKRh23VyrWABu/qn//+LsSH5r6tfPB+qwr0b+gFwJgN59h9OcSg8Qgh8ockgEKIYuf/s4AJ3Dr/5IpgRVFwHjMagOiffyb13vNnvt6p8Q4lLUpyJ+4OKy+vzFuArSaCS1V9aZjf3wEDLsJQFIWJr/ryWk0PMrQ63ll7mqM3c1n8WghhNCQBFEIUO+ZWGvya61cEn/orMMtVrlYNGmDZoD66tDTC58x5bpvWptaMq6vfUWTp5aVEZkTmPkATM3h9MajNIGAnnFic+7bygUqlMLVbNVr7uJCSrmXwipNcDIoxaExCiLyRBFAIUSxVb1UKE42K0Ntx3LsSleUxzqP1s4Axv/9OSkDAc9ts59WOem71SMlIYXvy9rwF6OwDbR7uEbzzMwi9mrf28kijVjGnZ03ql3UkPiWd/suOczsiwaAxCSFyTxJAIUSxZGFjim8TdwBO/hWY9TF+fti0aQM6HaGznl+gWVEUxr80HrWi5nLaZY4FH8tbkPWGQblWkJ4MGwdDemre2ssjc42aRX3rUMXdlvD4VPosOU5YXB7udxRCGIwkgEKIYqtmm9Ko1Ar3A6J5cD06y2NKjnofVCrid/9N0tmzz22znH05ulfoDsD3p74nXfvsHUWeSVGgy09g4QjBF+DAtNy3lU9szDUsG1CXUo4W3IlMZMDy48Sn5GGMQgiDkARQCFFsWTuYU7m+KwCntt/O8hizcuWw69oFgNDpM7K1K8awasOwVCy5EXOD9dfW5y1IG1d4dYb++cHpcC8P+w7nE2cbc1YNrIeTlSkXg2J5Z91Z0mXbYCFeKEaTAF65coXPP/+cli1bUq5cOdzc3KhWrRr9+vVj7dq1pKTIZQYhRP6r2a4MigK3L0YQdjcuy2NKvvMOikZD4vHjJPzz/Dp/tqa2tDZvDcDcs3OJSs76HsNsq9IF/N4AXQZsGgZpSXlrLx94lbBi2YC6WJqqOXwjkjXXVbJvsBAvEIMngKdPn6Z169bUrFmTQ4cOUa9ePUaNGsWkSZPo3bs3Op2OTz/9FHd3d6ZOnSqJoBAiX9k7W1K+jgsAp/7KehZQ4+6OQ8+eAITNyN4sYB3TOlRyqERcahxzzjx/FfFzvTINrF31u4T8PSnv7eWDap72zO9dGxOVwukIFd/u8Jd9g4V4QRg8AXz99dd57bXXCA4O5u+//+abb75h5MiRDB48mHHjxrFy5Upu3brFH3/8wZkzZ/jhhx8MHbIQooip/XIZAG6cCSUqOOuVrU7DhqJYWpJ86RLxe/c+t02VomJs7bEA/BrwK9cir+UtSEtH6DRb//zoTxB4KG/t5ZOmFUvy7WtVAVh2+DYLD9w0cERCiOwweALo7+/PiBEjsLe3f+ZxDRo04Oeff2bs2LGFE5gQothw8rDGq1oJ0MHpHVnPApo4OuLYuzcAYT/ORqd9/k1vtZxr8bLXy2h1Wr45/k3eZ8cqtoVa/QAdbB4OKVlfsi5snau70blMBgDf/HWVjafvGTgiIcTzGDwB1Gg0BXq8EEJkR+1X9LOA/sdCiI3I+h47xwH9UVlZkXL1KnG7d2er3TF1xmCuNudUyCl2BO7Ie6DtpoB9aYi+o68PaCRauusY1Ej/PRz363n2XTPsFnZCiGczMXQA//bjjz9m+bqiKJibm1O+fHmaNm2KWq0u5MiEEEWdq7cdnpUduHc1irO77tK0R8UnjjFxcMCxX1/Cf5pH+Ow52LRujaJ69udoVytXBvoN5KezP/HDqR9oVqoZFiYWuQ/UzAY6/wQrXoVTy6Hyq1ChTe7by0fj2lYkIiGNzWfvM2LNadYNqU/1UvaGDksIkQWjSgBnzJhBWFgYiYmJODg4ABAVFYWlpSXW1taEhoZStmxZ9u7dS6lSpQwcrRCiqKn1chnuXY3iyuH7vPSqN+bWT15xcOzXj8hVq0kJCCBuxw5sX3nlue0OqDKAzQGbuZ9wn2UXlzGixoi8BerdBOqP0N8L+Pu7MOKI/h5BA1OpFKZ1q05EQioHA8IZsPwEv77dgLIlrQ0dmhDiPwx+Cfjfvv76a+rWrUtAQAARERFERETg7+9PvXr1mDVrFnfu3MHV1ZUPPvjA0KEKIYogz0oOlChlTXqqlgv7s76PTW1nh2P/fgCEzZ2LLiPjue2am5gzuo5+W7nll5YTkhCS92BbTQSnChAfDH99lPf28ompiYp5vWvj52FHZEIq/ZedIDxeqjcIYWyMKgH87LPPmDFjBuXKlct8rXz58nz//feMHz8eT09Ppk2bxj///GPAKIUQRZWiKNRsWxqAC/vukZ6adXLn2LcvKltbUq/fIPav7O3527ZMW2o61yQpPYnZZ2bnPViNBXSdD4oKLvwCV7bmvc18Ym1mwrIBdSntaMmdyEQGLT9BYqrsFiKEMTGqBPDBgwekpz/5l0R6ejrBwcEAuLu7ExdnHCvfhBBFT/laztg4mpMUl8bVo8FZHqO2scFp4AAAwufMQZfF31v/pSgKH9b5EIAtN7ZwNfJq3oP1rAONRumf/zEaEiPz3mY+KWFtxvIBdXGw1HDuXgzvrTtDeoZsFyKEsTCqBLBFixYMGzaMM2fOZL525swZhg8fTsuWLQG4cOEC3t7ehgpRCFHEqdQqqrfS32N8dtedp+5u4dC7D2o7O1IDA4ndti1bbVcrWY1XvF9Bh47vT3yfP0WTm30EJSpBQihsH5/39vJR2ZLWLO5XBzMTFbuvhPLF1ktSKFoII2FUCeCSJUtwdHSkdu3amJmZYWZmRp06dXB0dGTJkiUAWFtbSzFoIUSB8mnkhpmlCTFhSdw6F5blMWprKxwHDwIgbO5P2ZoFBBhVaxSmKlOOBR/jwL0DeQ9WYw6d5+ovBZ//GfzzodRMPqpdxpFZPWqgKLD66B3m75dC0UIYA6NKAF1dXdm1axeXL19mw4YNbNiwgcuXL7Nz505cXPRbNbVo0YK2bdsaOFIhRFFmam5C1aYeAJzZeeeps1aOPXuidnQk7c4dYn7/PVttu1u709tXX1D6+5Pfk6ZNy3vAperqVwUDbB0FyTF5bzMfvVzVjQkdfAGYuv0qv58NMnBEQgijSgAfKVu2LJUqVaJ9+/ZUqlTJ0OEIIYohvxaeqEwUQm7F8uBG1gmVysoKp8GDAQifvyDbs4CD/QbjYOZAYGwgv/r/mj8Bt/wMHMtB3H2jKhD9yMDG3gxqrL9958MN5zhyI8LAEQlRvBlVApiYmMigQYOwtLSkSpUq3LlzB4CRI0fy7bffGjg6IURxYmVnRuV6roB+FvBpHHq8idrBgbS7d7N9L6CNqQ3v1HgHgHln5xGXmg8L2zQW0HkOoMDplXBjT97bzGeftvehvZ8raRk6hq46iX+ILOgTwlCMKgEcP348586dY9++fZibm2e+3rp1a9avX2/AyIQQxVGNNqVBgcDz4UQFJ2R5jMrSEscBD1cEz1+QrbqAAK9XfJ2ydmWJSoli0YVF+RNwmYbw0lD98y3vGc1ewY+oVArTu9egThkH4pLT6b/0OCGxyYYOS4hiyagSwM2bNzNnzhwaN26MoiiZr1epUoUbN24YMDIhRHHk4GqFd7USAJzZ9YxZwJ5vobKzI/XWLeJ2ZG8RhonKhDF1xgCw+vJq7sVlXXg6x1pNBPsyEHMXdn+RP23mI3ONmkV961C2pBX3Y5IZsOwE8SlSI1CIwmZUCWBYWBjOzs5PvJ6QkPBYQiiEEIWlZht9Yehrx4JJiMl6Rwu1tTWOffoAED5vPjpt9urdNfFoQj23eqRp05h1elb+BGxmDZ0eFpo+sRhuHcyfdvORg5UpKwa8RAlrUy4/iGX46lOkSY1AIQqVUSWAderUYdu/7qF5lPQtXryYBg0aGCosIUQx5lbeHteytmjTdVzY9/RZOsc+vVFZWZESEEDC3r3ZaltRFMbWGYuCwvbA7ZwLO5c/QZdtBrX1l6XZ8i6kZn352pBKOVqytH9dLDRqDgaE88nGC1IjUIhCZFQJ4Ndff80nn3zC8OHDSU9PZ9asWbRt25Zly5YxZcoUQ4cnhCimarTWzwJeOnD/qdvDqe3scOitL+8StWABZDOZqeRYiS7luwAw/eT0/EuC2nwFtp4QFQh7JudPm/msmqc9c3rWRKXAhlP3mPV3gKFDEqLYMKoEsHHjxpw9e5b09HT8/PzYuXMnzs7OHDlyhNq1axs6PCFEMeVdvQQ2juYkJ6Rx7VjW28MBOPbvh2JpScqVq1hdvZbt9kfUGIGZ2ozToafzpzg0gLktdHx4WfnoPLhzLH/azWetfFyY1KUqADN3B/DLybsGjkiI4sGoEkCAcuXKsWjRIo4fP87ly5dZvXo1fn5+hg5LCFGMqdQqqrX0BODcnntPnaUzcXDAoUcPABz//jvbs3muVq708ukFwMzTM8nQZm8l8XNVaA01egE62DIS0rO+h9HQetUrw4jm5QD4ZOMFDvhnvfuKECL/GDwBjI2NzfZDCCEMxaeROxozNVEPErh3JeqpxzkN6I9iZobF3bskHc3+rNvAqgOxNbXlevR1ttzYkh8h67WdDFYlIfwaHJqRf+3ms7HtKtGlhjvpWh0j1pzm8n35O1+IgmTwBNDe3h4HB4dsPYQQwlDMLEyo3NANgLN/P/0ypUnJkth2ex2AyAULst2+nZkdQ6vpa/jNPTuX5PR8qo9n6QivTNU/P/A9hF7Nn3bzmaIoTOtWnfplHYlPSWfg8hM8iEkydFhCFFkGTwD37t3Lnj172LNnD0uXLsXZ2Zlx48axadMmNm3axLhx43BxcWHp0qWGDlUIUcxVa+EJCty5FPHUwtAADgMGoFWrST51isQTJ7Ldfo/KPXCzciMkMYS1V9fmR8h6VV6Dii+DNg22vgfZLFNT2ExNVCzoXYcKztYEx+prBMYm58NeyUKIJxg8AWzWrFnmY+XKlUyfPp1vvvmGTp060alTJ7755hu+//57li1bZuhQhRDFnL2zJV5++sLQ5/Y8vSSMiYsLsXXrABA+b1622zdTm/FuzXcBWHxhMTEpWe9BnGOKAh1+AFNruHsMTi7Jn3YLgJ2lhmUD6lLSxoyrwXGMWH1aagQKUQAMngD+25EjR6hTp84Tr9epU4fjx48bICIhhHhcjValALh25AHJCU+fnYps3hxMTEg4fISkCxey3X4H7w5UcKhAXGociy8szmu4/2fnCa0+1z/f/SXEBOVf2/nM08GSZf3rYmmq5tD1cMZLjUAh8p1RJYClSpVi0aIn98RcvHgxpUqVMkBEQgjxOPeK9jh5WpOepuXSwacnUekODti0fwWAiIXZ3+tXrVLzQa0PAFh7ZS0P4h/kLeB/qzsIPF+C1DjYNibbtQoNoaqHHXN71UKtUvhVagQKke+MKgGcMWMGs2fPxs/Pj8GDBzN48GCqVavG7NmzmTEjZ6vXMjIymDBhAt7e3lhYWFCuXDkmTZr0xKfIK1eu0KlTJ+zs7LCysqJu3brcufP0PT+FEMWboiiZs4AX9gWR8YzLkw4DBwIQt3s3KTdvZruPxh6Nqetal1RtKnPOzslbwP+mUkOnH0GlAf+/4PLm/Gu7ALSo5Mykzv+vEfjrqXzaL1kIYVwJYPv27QkICKBTp05ERkYSGRlJx44d8ff3p3379jlqa+rUqcybN485c+Zw5coVpk6dyrRp05g9e3bmMTdu3KBx48ZUrlyZffv2cf78eSZMmIC5uXl+D00IUYRUqOOCha0pCdEp3Dz99Jp1puXKYd2qFeh0RCzO/n13iqJkzgJuvbEV/yj/PMecydkHmozWP/9zHCQ9vaSNMehZr3RmjcCPfzvPoYBwA0ckRNFgVAkggKenJ1OmTGHjxo1s3LiRKVOm5Ory7+HDh+ncuTMdOnTAy8uLbt260bZt28fuJfz0009p374906ZNo2bNmpQrV45OnTrh7Oycn0MSQhQxao0Kv2YegL4kzLPuTysxZDAAMVu2kPYg+5dz/Ur60bZMW3TomHV6Vt4C/q8mY6BERUgIhZ0T8rftAvBh20p0flgj8O3Vp7jyQGoECpFXJoYO4M6dO5QuXTrbxwcFBeHh4fHc4xo2bMjChQvx9/enYsWKnDt3jkOHDjF9+nQAtFot27ZtY9y4cbRr144zZ87g7e3N+PHj6dKlS5ZtpqSkkJLy/0r6j4pTp6WlkZaWv6UKHrWX3+0aKxlv0VYUx1upgTMn/wokNDCWoIBIXLxtM9/793g1VapgUbcuSSdOELZkKSU/GpftPkb4jWDPnT0cuHeAo/eOUtslv7bEVKG0n4HJyg5wZhXpvl3ReTXNdWuF8ec7pbMvD6KTOB4YRf9lx9kwtB5udoa5WlMUf56fpSiOtyiNJbcUnYGXVrm4uNClSxcGDx5M3bp1szwmJiaGX375hVmzZjF06FDee++957ar1Wr55JNPmDZtGmq1moyMDKZMmcL48eMBCA4Oxs3NDUtLSyZPnkyLFi3Yvn07n3zyCXv37qVZs2ZPtPnFF1/w5ZdfPvH62rVrsbS0zOHIhRAvusjz5iQGabBwTcOp5tMLN1v6++O5ZClajYab4z9Ga2WV7T62Jm7lWOoxSqlLMdR6KIqi5EfoAFS7uxzv8D3Emzqz1+drtCrTfGu7ICSmw8yLakKSFNwtdbxfJQNzg09jiBdRYmIiPXv2JCYmBltb2+efUAQZPAGMiIhgypQpLF26FHNzc2rXro27uzvm5uZERUVx+fJlLl26RK1atZgwYUK27wX8+eefGTt2LN999x1VqlTh7NmzjBo1iunTp9OvXz/u37+Ph4cHb731FmvX/r/gaqdOnbCysmLdunVPtJnVDGCpUqUIDw/P9x+gtLQ0du3aRZs2bdBoNPnatjGS8RZtRXW8EUEJ/PbtaRQV9PzyJazszYAnx6vT6bj35pukXLmKw/C3cRoxItt9hCWF0XlLZ5IzkpnRdAbNPJ/8cJprybGYLGyEEveAjAbvoW05MVfNFOaf772oJN5YeIzw+FQal3diYe+aaNSFezdTUf15fpqiON7Y2FhKlChRrBNAg392cnJyYvr06UyZMoVt27Zx6NAhbt++TVJSEiVKlKBXr160a9eOqlWr5qjdsWPH8vHHH9Pj4cbsfn5+3L59m2+++YZ+/fpRokQJTExM8PX1few8Hx8fDh06lGWbZmZmmJmZPfG6RqMpsF+KgmzbGMl4i7aiNl5XL3vcK9hzPyCaa0dCqdep7GPv/3u8JYYNI2jUB8SsXYfz4MGosjkL6K5xp5dPL5ZcXMJPF36ipVdLVEo+JTwaJ+gwHX5+C/XRuairvQFu1XLfXCH8+Xo7a1jW/yW6LzjCoesRfPHHVaa+Xi1fZ0azq6j9PD9PURpvURlHXhjNIhALCwu6devGzJkz2bRpE9u3b2f16tWMGTMmx8kf6Kd3VarHh6dWq9E+3ALJ1NSUunXrcu3atceO8ff3p0yZMrkfiBCiWPFr7gnApYNBZKQ9vSSMTZs2mJYpgzYmhqgNG3LUx4CqA7DR2BAQFcBft/7KU7xPqNwefDuDLgO2jARtRv62XwD8PO2Y26smKgV+OXmP2XuuGzokIV44RpMA5reOHTtmzioGBgayadMmpk+fTteuXTOPGTt2LOvXr2fRokVcv36dOXPmsHXrVkbk4PKMEKJ4865RAit7M5Li0rh+OvSpxylqNY6DBwEQuWw52tTUbPdhZ2ZH/6r9AZh7di5p2ny+gf2V78DMDh6chePZL1ptSC0ruzCpi35yYPouf6kRKEQOFdkEcPbs2XTr1o0RI0bg4+PDhx9+yLBhw5g0aVLmMV27dmX+/PlMmzYNPz8/Fi9ezG+//Ubjxo0NGLkQ4kWiVquo2tQdgAv7np2E2HXujImzM+khIcRu3Zqjfnr79MbR3JG7cXfZFLAp1/FmycYF2nyhf75nklFvE/dvveqV4e1m/68R+M91qREoRHYV2QTQxsaGmTNnZt5PeOPGDSZPnoyp6eOr3AYOHEhAQABJSUmcPXuWzp07GyhiIcSLyrexByoThZBbsYQEPr1GncrUFMf+/QGIWLQYXUb2L7daaiwZWm0oAAvOLSA5/emrjnOlVv+H28TFw1/ZL1VjaOPaVaJj9Yc1Aled4lpwnKFDEuKFUGQTQCGEKCyWtqaUr60vIH9h77NnAe27d0dlZ0dqYCBxu//OUT9vVHwDNys3QpNCWX9tfa7jzZJKBR1ngsoErv4BV7flb/sFRKVS+P6Narzk7UhcSjr9lx0nOCafk2MhiiBJAIUQIh9Ua67fsSjgVAhJcU+/v09tbYVjr54ARCxa9MxdRP7LVG3K8OrDAVh8YTHxqfF5iDgLLlWg4Uj98z/HQsqLMZtmZqJmYZ/alCtpxYOYZAYsP0F8SrqhwxLCqBldArhq1SoaNWqEu7s7t2/fBmDmzJn8/vvvBo5MCCGezsXbFucyNmjTdVw5HPzMYx1690YxNyf54kUSjx7NUT8dy3XEy9aL6JRoVl5emZeQs9Z0HNiXgdgg2Pt1/rdfQOwtTVk+4CVKWJty5UEsI9acJi3j6auyhSjujCoBnDdvHqNHj6Z9+/ZER0eT8fD+GHt7e2bOnGnY4IQQ4jmqtdCXhLly6AG6Z+QeJo6O2L/+OgARS5bmqA8TlQnv1nwXgBWXVhCZHJm7YJ/G1BJe1W+ZybH5cP9M/rZfgEo5WrK0f10sNGoO+IcxYfPFHM2wClGcGFUCOHv2bBYtWsSnn36KWq3OfL1OnTpcuHDBgJEJIcTzla/tgoWNhoToVJJCnl1n37F/P1CpSDh0iOT/1CN9njZl2uDj6ENieiJLLizJS8hZK98aqnYDnRa2vg8ZL87l1Gqe9sx+S18j8OcTd5m7V2oECpEVo0oAb926Rc2aNZ943czMjISEBANEJIQQ2afWqPBtrC8JE3/n2TsNmJYqhU3btgBELl2Wo35Uior3aun3RP/56s8EJzz7knOutPsazO3gwTk48WLUBnykta8LX3aqAsD3O/3ZdEZqBArxX0aVAHp7e3P27NknXt++fTs+Pj6FH5AQQuRQ1aYeKCpIjTQhIujZH1ydBg0EIGbbNtKCc5bENXJvRC3nWqRqU5l/bn6u430qGxdo/aX++Z7JEPNiJVF9GngxrJl+a75xv57nsNQIFOIxRpUAjh49mnfeeYf169ej0+k4fvw4U6ZMYfz48Ywb9+LUpRJCFF/WDuZ4VSsBwKUD9595rIWfH5Z160J6OpErV+WoH0VReL/W+wBsvr6Z27G3cxfws9TqB6XqPawN+FH+t1/APmpXmVeruZGWoWPY6lP4h7wYq5qFKAxGlQAOHjyYqVOn8tlnn5GYmEjPnj2ZN28es2bNokePHoYOTwghsqVKUzcAAk6Ekpzw7G3bHB/OAkavX09GXM4SlFoutWji0YQMXQZzz8zNXbDPolLBqzP/Xxvwyh/530cB0tcIrE5dLwfiktPpv/Q4IbFSI1AIMLIEEKBXr14EBAQQHx9PcHAw9+7dY9CgQYYOSwghss2tvB0amwwy0rRcOfzgmcdaN22KaflyaBMSiP7llxz3NbKmvm7fX4F/cS0yZ4tJssXFFxrq7zfkr3EvTG3AR8w1ahb1rUPZklbcj0lmoNQIFAIwwgTwEUtLS5ydnQ0dhhBC5JiiKFiV1s/8XToQhE779FIkikqF04ABAESuXIUu9elFpLPi4+RDO692AMw9WwCzgABNx4KD1wtXG/ARe0tTlvfX1wi8dD+Wd9acJl1qBIpizuAJYM2aNalVq1a2HkII8aKwdE9DY64mJiyJe1ejnnmsbceOmJQsSXpICDF//pnjvkZUH4FKUbH37l4uhV/KbchPZ2oJHV7M2oCPlHayZEm/uphrVOz3D+MzqREoirlnF6oqBF26dDF0CEIIke9UJlChrjOXDz7gwv57lPJ1fPqxpqY49OlD2PTpRC5dhl3nziiKku2+ytqXpb13e/64+Qdzzs5hXut5+TGEx5VvBX5vwIUN+tqAg/eA2uD/hORI9VL2zH6rFsNWneTnE3cp5WjJOy3KGzosIQzC4L+9n3/+uaFDEEKIAuHbxI3LBx8QeD6c+KhkrB3Mn3qsQ483iZg/nxR/fxIOHcK6SZMc9TW8+nD+uvUXh4IOcTb0LDWca+Qx+iy0+xoCduprAx5fCA1G5H8fBayNrwtfdKrCxN8v8d2Oa3jYW9ClpoehwxKi0Bn8ErAQQhRVjm5WuFewR6eDS4eeXRJGbWuL/RtvADnfHg6gtG1pOpfvDMCcM3NyHmx2WDtDm6/0z1/A2oCP9G3gxdCm+hqBY389x+EbUiNQFD9GlQA6ODjg6Oj4xMPJyQkPDw+aNWvGsmU5q5gvhBCGVLWpfnbp8qH7ZDxn4YFjv76gVpN49ChJl3J+L9+wasMwUZlwLPgYJ4JP5Cre56rZF0rVh7QE+PPFrc/68cuV6eD3sEbgKqkRKIofo0oAJ06ciEqlokOHDnz55Zd8+eWXdOjQAZVKxTvvvEPFihUZPnw4ixa9WNsSCSGKr7I1S2JhoyExJpVbZ58906Rxd8e2fXsg59vDAbhbu/N6hdcB/SxggSxyUKmg40z9TY7Xtr1wtQEfUakUfuhenTpl9DUCByw7QajUCBTFiFElgIcOHWLy5MmsWrWKkSNHMnLkSFatWsXkyZM5deoUixYt4rvvvuPHH380dKhCCJEtahMVvo30+wNfPBD03OOdBupLwsRu305a0POP/68hfkMwVZlyOvQ0h+8fzvH52eLsA430u5Dw59gXrjbgI49qBHqXsCIoOomBK06QIDUCRTFhVAngjh07aN269ROvt2rVih07dgDQvn17bt68WdihCSFErvk2cUdRIOhaFFHBz94f2NzHB6uGDSAjg4gVK3Lcl4uVC90rdQcKcBYQ/l8bMO4+qv3fFkwfhcDBypTlA+riZGXKxaBY3l0rNQJF8WBUCaCjoyNbt2594vWtW7fi6KgvoZCQkICNjU1hhyaEELlm62RBGT/9/sDZmQV0HKjf/Sj619/IiInJcX+D/AZhYWLBxYiL7L+3P8fnZ4vGAl6dAYDq5CLsE1/cD+ZlnKxY3K8O5hoVe6+FMeH3S1IjUBR5RpUATpgwgbFjx9KpUycmT57M5MmT6dy5M+PGjcssF7Nr1y6aNWtm4EiFECJnHi0GuXokmLSUjGcea9WoIWaVK6NLTCRq3c857quERQneqvwWoJ8F1OoKaEarXEvwewNFp6X6nWWgfXEvn9Ys7cCPPWqiKLDu+B3m7b9h6JCEKFBGlQAOGTKE/fv3Y2VlxcaNG9m4cSOWlpbs378/cz/gMWPGsH79egNHKoQQOVPa1xHbEuakJqUTcDLkmccqipJ5L2DkmtVoc7g9HMCAKgOw0lhxLeoau2/vzlXM2dLuG3Tm9tgn3UZ14sVeoNe2iiufv+oLwLTt1/j9bM7vwRTiRWFUCSBAo0aNWLduHadPn+b06dOsW7eOhg0bGjosIYTIE0WlUKWJfhbw4v7nJxa2L7+MiYsLGWHhxG7L+fZw9ub29PHtA8BPZ38iQ/vsWcdcsy5JRkv9FRrV/m8h+m7B9FNI+jfyZnBjbwDGbjgvNQJFkWV0CaBWq8Xf359Dhw5x4MCBxx5CCPEi82nohspEIexOHCGBsc88VjE1xaF3LwAily3L1T1pfXz7YGNqw42YG/wV+FeuYs4OXY1eRFhVRElLgD8/hBf8/rlP2vvQ3s+V1AwtQ1ee4mJQzu/DFMLYGVUCePToUcqXL4+Pjw9NmzalefPmmY8WLVoYOjwhhMgTCxtTytd2BuDi/ufvouHQvTuKpSUp/v4kHjmS4/5sTW3pX6U/APPPzSe9oO7RU1ScKzUAnUoD/tvhypOL+V4kKpXC9O41qOftSHxKOv2XHScw/Nmrt4V40RhVAvj2229Tp04dLl68SGRkJFFRUZmPyMhIQ4cnhBB5VrWpJwABJ0NJTkh75rFqOzvsX3sNgIjly3PVXy+fXjiYOXA79jZbbxRcYhZn4YG2wXv6L/4aB8nPnuE0duYaNYv61cHXzZbw+FT6Lj0uhaJFkWJUCWBAQABff/01Pj4+2NvbY2dn99hDCCFedK5lbXHytCYjTcvVIw+ee7xj3z6gKCQcOEjK9es57s9KY8XAqgMBWHB+AWkZz04680LbaBQ4loW4B7BnUoH1U1hszTWsGPgSZZwsuROZSL9lJ4hJKrjvnxCFyagSwHr16nE9F3/BCSHEi0JRlMySMBcPBKHTPvt+OdPSpbF5WCA/MheFoQHerPwmJSxKEBQfxKbrm3LVRrZoLKDDdP3z44vg3qmC66uQlLQxY9XAepS0MePKg1iGrDxJcloBLagRohAZVQI4cuRIxowZw/Llyzl16hTnz59/7CGEEEVBxZdc0JiriQlN4t61qOce7zigPwAxv28hPSIix/1ZmFgw2G8woJ8FTMlIyXEb2VauBVR7E9DB1vch48WtDfhIaSdLVgx4CRszE47fiuSDX86T8WKvcxHCuBLA119/nStXrjBw4EDq1q1LjRo1qFmzZub/hRCiKDA1N6FyPVcgezuDWNSsiXm1auhSU4lauy5XfXar2A0XSxdCE0P51f/XXLWRbW2ngIUDhFyAY/MKtq9C4utuy+J+dTA1UbH7ahi/3FTJbiHihWZUCeCtW7eeeNy8eTPz/0IIUVRUaaa/DHzrXDjxUc+ekVMUBaeHs4BR69ahTc75YgQztRlDqw0FYNH5RSSlJ+W4jWyzLgltHt4DuPdriLpdcH0VonplnZj9Vk1UChwNVTF9t9yyJF5cRpUAlilTJstHqVKluHDhgqHDE0KIfOPkbo1beTt0Wh2X/7n/3ONt2rTBxN2NjMhIYrLYMz07upbvioe1BxHJEfx8NedbzOVIzd5QphGkJRaJ2oCPtKviyuTO+t1C5h+4xeKDMjkhXkxGlQD+1/Xr1/nkk0/w9PSka9euhg5HCCHyVdWHs4CXD91Hm/Hs/XoVExMc+/QFIHL5CnTanO/vq1FreLv62wAsvbiUhLQCrG2nKPDqDFBpIGAnXP694PoqZG/U9uTV0vqFIJO3XWHTmefXdBTC2BhdApiUlMTKlStp2rQplSpV4vDhw0ycOJF79+QXTAhRtJSr4YyFjYaE6BQCLzx/cYf9G91QWVmReuMGCYcO5arPV8u+ipetF9Ep0ay+vDpXbWRbyUrQ+AP9878+guSis6NGa3cdAxqWAeDDDefZeSnYwBEJkTNGkwCeOHGCYcOG4erqysyZM+ncuTOKovDTTz/x9ttv4+LiYugQhRAiX6k1KnwaugHZWwyitrbG/o03AIjMZWFoE5UJw6sPB2DF5RXEphZwweYmY8CxHMQHw99fFWxfhUhR4ON2FXm9licZWh3vrj3DwYAwQ4clRLYZRQJYrVo13njjDZycnDh8+DCnT59mzJgxKIpi6NCEEKJA+Tb2AAXuXo4kJizxucc79ukNajUJh4+QfPVqrvp82ftlytuXJy41jpWXVuaqjWzTmOsvBQOcWAJ3TxRsf4VIpVKY+rofr1T9/77BJwJl1yrxYjCKBPDatWs0bdqUFi1a4Ovra+hwhBCi0NiVtKC0ryMAlw4+fzGIxsMD23ZtAf29gLmhUlS8U+MdAFZdXkVU8vNrEeZJ2WZQ/S1AB3+MggLcjaSwmahVzOpRk2YVS5KUlsHAZSe4cK/oXOoWRZdRJIA3b96kUqVKDB8+HE9PTz788EPOnDkjM4BCiGKhShP9YpArhx+Qkfb8xR2O/fsDELNtG2khobnqs1XpVvg4+pCYnsiyS8ty1UaOtJ38sDbgRTgyt+D7K0SmJirm967NS96OxKWk03fpMfxD4gwdlhDPZBQJoIeHB59++inXr19n1apVBAcH06hRI9LT01m+fDn+/v6GDlEIIQqMl58T1g5mJMencePM8xM6i2rVsKhVC9LSiFq7Nld9KorCuzXfBWDdlXWEJ4Xnqp1ssyqhTwIB9n0LUYEF218hszBVs6RfHap72hGVmEbvxce4HVGAq6yFyCOjSAD/rWXLlqxevZoHDx4wZ84c9uzZQ+XKlalWrZqhQxNCiAKhUqvwbewOZG8xCPx/e7jon39Gm/j8ewez0sSjCdVKVCM5I5klF5bkqo0cqdELyjSG9CTYNqbI1AZ8xMZcw4qBL1HZ1YbQuBR6LjrG/egCLLgtRB4YXQL4iJ2dHSNGjODkyZOcPn2a5s2bGzokIYQoML6N3FFUCg+uxxARFP/c421atkRTqhQZMTFEb96cqz4VReGdmvp7AddfW09wQgGXMnlUG1BtCtd3w6VNBdufAdhbmrJqUD28S1gRFJ1E78XHCI8vwL2Xhcglo00A/61GjRr8+OOPhg5DCCEKjJW9Gd7VSwBwKRuzgIpajWPfh4WhV+SuMDRAA7cG1HapTZo2jUXnF+WqjRwpWREaj9Y/3/4xJEUXfJ+FrKSNGasH18PD3oKb4Qn0XnyMqIRUQ4clxGNeiARQCCGKg6pN9YtBrh4LJjU5/bnH27/WFZWtLWm37xC/b1+u+lQUhXdr6O8F3BiwkXtxhVB0v/EH4FQe4kPg7y8Lvj8D8LC3YPXgepS0MeNqcBy9Fh8jOlGSQGE8JAEUQggj4VnJAbuSFqQlZxBwIuS5x6usrHB4szsAkUtzv5K3jmsdGrg1IF2XzoLzC3LdTrZpzOHVmfrnJ5fC3eMF36cBeJewYt2QepSwNuXyg1h6LzlGTGLRKYEjXmySAAohhJFQVApVHs4CXjwQhC4biyQcevUCExMST54k6cLFXPf9aEXw1htbCYwJzHU72ebdRL8oBGDr+0WqNuC/lXe2Ye2Q+jhZmXIx6GESmFQ0xypeLEabACYnJxs6BCGEKHQ+DdxQm6gIvxtPaODza8lpXF2xbf8KoL8XMLeqlaxGM89mZOgymHduXq7byZE2k8DCEUIvw+HZhdOnAVR00SeBjlamXAiKoa8kgcIIGFUCqNVqmTRpEh4eHlhbW3Pz5k0AJkyYwJIlhVCiQAghDMzcWkP52s4AXDyYzZIw/foBEPvXX6Q9eJDrvh/tDvLXrb+4HnU91+1km5UTtJuif75/KkTeLPg+DaSSqw1rBtfDwVLDuXsx9Ft6nNhkSQKF4RhVAjh58mSWL1/OtGnTMDU1zXy9atWqLF682ICRCSFE4Xl0Gfj6iRCSE56fJFhUqYLlSy9BRgaRq1fnul8fJx/alGmDDh0/nfsp1+3kSPW3wKsJpCfDHx8UudqA/+bjZsuawfWxt9Rw9m40/ZYeJ06SQGEgRpUArly5koULF9KrVy/UanXm69WrV+dqLjc9F0KIF41rWVucPKxJT9Ny7Wj2avNlFob+ZQMZ8bnfgWJE9REoKOy6vYsrEVdy3U62KQp0nAUm5nBzH5xdU/B9GpCvuy2rB9XDzkLDmTvR9F92QpJAYRBGlQAGBQVRvnz5J17XarWkpckviBCieFAUhapN9TuDXDqYvcUg1s2aYerlhTYujpiNv+W67/IO5XnFW39P4dyzhbRnr1M5aD5e/3zHJxD3/BXQL7KqHnasHlQPW3MTTt2OoveS47I6WBQ6o0oAfX19OXjw4BOv//rrr9SsWdMAEQkhhGFUrOeKxkxNVHAi9/2jn3u8olLh2F9/L2DkipXo0p9fR/BphlcfjkpRsf/efs6Hnc91OznS4F1wqw7JMfDnh4XTpwH5edplXg4+dzeatxYdJUJ2DBGFyKgSwIkTJ/Luu+8ydepUtFotGzduZMiQIUyZMoWJEycaOjwhhCg0puYmVHzJBcj+/sB2nTujtrcnLSiIuN27c923l50XHct2BGDOmTm5bidH1CbQaQ4oariyBa5sLZx+DcjP046fh9bPrBPYY+FRQmOlAoYoHEaVAHbu3JmtW7eye/durKysmDhxIleuXGHr1q20adPG0OEJIUShqtpMvxjk5pkwEmOfv4uEysICh55vARCxbFm2Lh0/zdvV38ZEMeHIgyOcCjmV63ZyxK0aNHpf/3zbh0Vym7j/quxqy/phDXC1NScgNJ43Fx7lfnSSocMSxYBRJYAATZo0YdeuXYSGhpKYmMihQ4do27atocMSQohCV8LTBhdvW7RaHZf/uZ+tcxx69kQxNSX53HmSzpzJdd+eNp50rdAVgNlnZucpmcyRZh893CYuGHZNKJw+DaxcSWt+GdYAD3sLboUn0H3BEe5GJho6LFHEGV0CKIQQ4v8ezQJePngfrfb5SZhJiRLYdtJfvo1clvvt4QCGVhuKqcqUUyGnOPrgaJ7ayjaNOXT8Uf/89Eq4daBw+jWw0k6W/PJ2A7ycLLkXlcQb849wMyze0GGJIszgCaCDgwOOjo7ZeuRERkYGEyZMwNvbGwsLC8qVK8ekSZOe+in27bffRlEUZs6cmQ+jEkKI/FG+ljNmlibERSZz51JEts5xelgYOm7336Tevp3rvl2tXHmj0hsAzDk7p/BmAb0aQZ1B+udb3oPU4jEb5mFvwS/DGlDB2Zrg2GS6LzjCxaAYQ4cliiiDJ4AzZ85kxowZzJgxg88++wyAdu3a8cUXX/DFF1/Qrl07QL8bSE5MnTqVefPmMWfOHK5cucLUqVOZNm0as2c/ud3Qpk2bOHr0KO7u7nkfkBBC5CMTUzWVG7oBcCmbi0HMKlTAqkkT0OmIXLkqT/0P9huMudqc82HnORj0ZJWGAtP6C7D1gKhbsO/rwuvXwJxtzfl5aH183WwJj0+lx8KjHLmRvcRfiJwweALYr1+/zMc///zDV199xbp163jvvfd47733WLduHV999RX79+/PUbuHDx+mc+fOdOjQAS8vL7p160bbtm05fvz4Y8cFBQUxcuRI1qxZg0ajyc+hCSFEvqjaRH8ZOPBiBLER2Vsg4PSoMPTGjWRER+e67xIWJXirsn5hyZwzhTgLaG4LHabrnx+ZC0GnC6dfI+BkbcbPw+pTz9uR+JR0+i09zvaLud/iT4ismBg6gH/bsWMHU6dOfeL1l19+mY8//jhHbTVs2JCFCxfi7+9PxYoVOXfuHIcOHWL69OmZx2i1Wvr06cPYsWOpUqXKc9tMSUkhJeX/dZpiY2MBSEtLy/dC1Y/aKy4FsGW8RZuMN2+sHDV4VLQnyD+aiwfuUfdVr+eeo6lTB9OKFUn19yd83TocBw/Odf+9K/Vm/bX1XIm8wo5bO2hVqtVj7xfYn2/ZVqirvIbq0kZ0v79L+sDdoDb8B/XC+Hm2UMOSPjX5YMMFdl0JZcSa03zVyZc363gWWJ9PUxR/f4vSWHLLqBJAJycnfv/9d8aMGfPY67///jtOTk45auvjjz8mNjaWypUro1arycjIYMqUKfTq1SvzmKlTp2JiYsJ7772XrTa/+eYbvvzyyyde37lzJ5aWljmKL7t27dpVIO0aKxlv0Sbjzb0kSxPAgnN7bxPKZZRsXL+xqVEdN39/Qpcu46izM5jk/q/8l9QvsS99H9/98x1JNkmosgigIP58TZWWtFTvxCz0EgErRhLg2inf+8itwvh5bm8H8c4qjoSq+Oz3yxw5fYE2HjoUpcC7fkJR+v1NTCwe95U+i1ElgF9++SWDBw9m37591KtXD4Bjx46xfft2Fi1alKO2fvnlF9asWcPatWupUqUKZ8+eZdSoUbi7u9OvXz9OnTrFrFmzOH36NEo2f5PGjx/P6NGjM7+OjY2lVKlStG3bFltb2xzF9zxpaWns2rWLNm3aFItL0zLeok3Gm3faDC1rJh4nKTYNH4+6lK1Z8rnn6Nq0IXDffggNpbFOh2379rnuv0lqE179/VVC00IxqWLCy14vZ75X0H++SlkVbBmBT+hWKnQaAyUq5HsfOVHYP88ddDpm/H2deftvse2uGkeP0nz2ciVUqsLJAovi7++jK3jFmVElgP3798fHx4cff/yRjRs3AuDj48OhQ4cyE8LsGjt2LB9//DE9evQAwM/Pj9u3b/PNN9/Qr18/Dh48SGhoKKVLl848JyMjgzFjxjBz5kwCAwOfaNPMzAwzM7MnXtdoNAX2S1GQbRsjGW/RJuPNS2NQpbEHJ/8M5Mo/wVR6KRuL1jQaHHv3Jmz6dGJWrsLx9dez/YH3vxw1jvSr0o85Z+ew8OJCXin3Ciaqx/8JKbA/35o94fImlOu70Gx7HwZuB5U6//vJocL8ef7oFV+cbS34cutlVh29Q3h8KjPerIG5pvC+D0Xp97eojCMvDL4I5L/q1avHmjVrOH36NKdPn2bNmjU5Tv5AP72rUj0+PLVajVarBaBPnz6cP3+es2fPZj7c3d0ZO3YsO3bsyJexCCFEfvJt7I6iQNC1aKKCE7J1jsOb3VEsLUnx9yfh8OE89d/btzf2ZvYExgay7ea2PLWVI4oCHWeCmS3cO65fFFIMDWjkzaweNTBVq/jrYjA9Fh4lLE72Dxa5Y1QJ4J07d575yImOHTsyZcoUtm3bRmBgIJs2bWL69Ol07aqvbO/k5ETVqlUfe2g0GlxdXalUqVJBDE8IIfLExtGcMn4lALh0IHs7g6jt7LB/7TUAIpctz1P/VhorBlYdCMC8c/NI0xbijfR2ntDuYTmYPZMh7Frh9W1EOtfwYNWgl7C31HD2bjRdf/qH66Fxhg5LvICMKgH08vLC29v7qY+cmD17Nt26dWPEiBH4+Pjw4YcfMmzYMCZNmlRA0QshRMGr2lRfEubq0Qekp2Zk6xzHfn1BpSLh0CGS/f3z1H+Pyj1wMnciKD6Izdc356mtHKvZG8q3gYwU2DwCtNkbf1FTr6wTG4c3pMzDXUNe++kwh2+EGzos8YIxqgTwzJkzmZd+T58+zbFjx5g/fz4VK1Zkw4YNOWrLxsaGmTNncvv2bZKSkrhx4waTJ0/G1NT0qecEBgYyatSoPI5CCCEKTilfR2yczElJTCfgZGi2zjEtVQqb1q0BiFy+Ik/9W5hYMKTaEAAWnFtASkYhXoJUFOg4C8zsIOgkHH6ysH9xUbakNZtGNKJ2GQdik9Ppu+Q4v566Z+iwxAvEqBLA6tWrP/aoU6cOQ4YM4fvvv+fHH380dHhCCGFwKpVClSb6BSCXDmZvZxAAx4eFoWO3biU9LCxPMXSr2A0XSxdCEkP41f/XPLWVY3Ye8PLDS8F7v4bQq4XbvxFxtDJlzeB6vFrNjXStjg83nOPrP6+QkY09o4UwqgTwaSpVqsSJEycMHYYQQhgFn4buqNQKIbdiCbuTvfu/LGvWxKJGDXRpaUSuWZOn/s3UZgyrPgyAhecXkpCWvQUp+aZGL6jQ9uGl4OGQkV64/RsRc42aH3vU5N0W5QFYeOAmA5afICZRCh2LZzOqBDA2NvaxR0xMDFevXuWzzz6jQgXD1n0SQghjYWlrSrmHdQAv5mgWcAAA0et+RpvHQrhdynehtE1pIpMjWXt1bZ7ayrF/Xwq+fxoOF+8rRCqVwoftKjGnZ00sNGoO+IfRee4hAkJkcYh4OqNKAO3t7XFwcMh8ODo64uvry5EjR5g3b56hwxNCCKNR5eFiEP/jIaQmZW8GzKZ1KzSlSpERE0P05s156l+j0jCy5kgAVl5ZSYK2kGcBbd3hlW/1z/d9A6FXCrd/I/RqNXd+Hd4AD3sLAiMS6TL3H3ZeCjZ0WMJIGVUCuHfvXvbs2ZP52LdvH5cvX+bGjRs0aNDA0OEJIYTRcK9gj4OrJekpGVw7lr1/5BW1Gse+fQGIXLECXUbeVtG29WqLj6MPCekJHEg5kKe2cqX6W1ChHWSkFvtLwY9Ucbdjy7uNqF/WkYTUDIauOsX0ndfkvkDxBKNKABVFoVGjRjRr1oxmzZrRpEkTKleuDMCBAwb4y0UIIYyUoiiZs4AXDwSh02XvH3j717qisrUl7fYd4vfuzVMMKkXF+7XeB+BYyjGCEwp5tunRpWBzO7h/Bv6ZUbj9GyknazNWDapHvwZlAPhxz3X6Lj0mRaPFY4wqAWzRogWRkZFPvB4TE0OLFi0MEJEQQhivyvVdMdGoiLyfQPCNmGydo7KywuHNNwGIyGNhaICG7g2p41yHdNJZeHFhntvLMVs3eGWa/vm+b+H+2cKPwQhp1Cq+7FyVGW9Wx0Kj5p/rEXT48SDHbkYYOjRhJIwqAdTpdFnuUxkREYGVlZUBIhJCCONlZqmhQl0XQD8LmF0OvXuDRkPSqVMknT+fpxgUReHdGu8CsOXmFm7G3MxTe7lS7U3w6QTadNg4FNKSCj8GI9W1pidb3m1EBWdrQuNSeGvRUX7adx2tXBIu9owiAXzttdd47bXXUBSF/v37Z3792muv0blzZ9q1a0fDhg0NHaYQQhidqs30l4Gvnw4lKT41W+doXJyxa98egIhly/IcQ7US1fAx8UGr0zLnzJw8t5djigKvzgRrFwi/Bru/KPwYjFgFFxt+f7cRr9X0QKuDaduvMWjFCbkkXMwZRQJoZ2eHnZ0dOp0OGxubzK/t7OxwdXVl6NChrF692tBhCiGE0XEuY0vJ0jZo03VcOfwg2+c9Kgwdt2MnqfeyP3v4NK0tWqOgsOv2Li6GX8xzezlm5QSdf9I/PzYfbuwp/BiMmKWpCT90r863r/lhZqJi77UwXpl1gL+vhBg6tKfS6XTcOB1KRobW0KEUSSaGDgBg2cNPoF5eXnz44YdyuVcIIXKgajMP9q66yqWD96nZujSK6slbaf7LvHJlLBvUJ/HIUaJWrcRl/Pg8xeCiduFV71fZemsrM0/PZHHbxXlqL1cqtIa6g+HEYv1ewcMPg6Vj4cdhpBRFocdLpalR2p5RP5/lanAcg1acpHf90nza3hcLU7WhQ8wUHZrI3lVXuR8QTaNu5anRurShQypyjGIG8JHPP/9ckj8hhMihCnVcMLUwITYsibtXn1xI9zROjwpDb/iVjNjYPMcxzG8YGpWGYw+OceT+kTy3lyttJoFTeYh7ANtGQzZXRxcnlV1t2fxOIwY19gZg9dE7dJh9kPP3og0bGKDT6jj3913WTzrO/YBoTExV2fpAI3LO4AlgrVq1iIqKAqBmzZrUqlXrqQ8hhBBP0pipqVTfFYBLB+5n+zyrJk0wLV8ObWIi0Rs25DkOd2t3ulfqDsCs07OyXZomX5lawmsLQVHDpU1woZD3Kn5BmGvUTHjVl9WD6uFia8bNsAS6/nSYaduvkpyWt/qQuZUUn8qWH89yaEMA6WlaPCs78NbEelRvWcog8RR1Br8E3LlzZ8zMzDKfZ7UKWAghxLNVbeLBhb33uHU+nPioFKwdzJ57jqIoOPXvz4PPJhC5chWOffqgmJrmKY4hfkPYGLCRSxGX2H1nN23KtMlTe7niURuafQT7voZtY6B0fbCXJCIrjSuUYMeopny6+SLbzj/gp3032H4pmGmvV6OOV+FdPg+9HctfCy4QH5mCiamKRt0qUKWJu+QEBcjgCeDnn3+e+fyLL74wXCBCCPECc3S3wr2CPfcDorn8z31eetU7W+fZduxI6KxZpIeEELPtT+y7dslTHE4WTvSr0o/55+bz4+kfaVGqBSYqA/xT02QMBOyEoJP6XUL6/g4q47nHzZjYW5oyt2ctOlYLZsLvF7kZlsAbC47Qr4EXY9tVwrSArxUGnAjh7xVXyEjXYudswStv++Hkbl2wnQrDXwL+t7JlyxIR8WSRyujoaMqWLWuAiIQQ4sVR9eHOIJcP3UebzZWTKjMzHPvot4eLWLIYnTbvKy77+fbD3syewNhAfr/+e57byxW1if5SsMYKAg/CPzMNE8cL5OWqruz+oBlv1PZEp4PlhwNpM30/2y+FFNitlOf+vsvOJZfISNfiVa0Eb4yvK8lfITGqBDAwMJCMLPamTElJ4d69ewaISAghXhxla5TEwkZDQnQKgReyv+ODQ483UVlZkXr9BvH79+c5DmtTa4b4DQHgp7M/kZiWmOc2c8WpHLR/uEvInilw97hh4niB2Flq+O6N6qwa9BKeDhbcj0lm5M/nmHdFxY2whHzrR6fTcXjjdQ5tCACgWgtP2r/th5mFwS9MFhtGkQBu2bKFLVu2ALBjx47Mr7ds2cKmTZuYNGkS3t7Zu5whhBDFlVqjwqehO5CznUHUtrbY93i4PdziJfkSS4/KPfCw9iA0KZRVl1flS5u5UqMXVO0Gugz4dRAkRRsulhdIkwol2fVBM95rWR5TExXXYlS8Oucw3/x5hfiU9Dy1rdPpOLQhgDM77wBQv0tZGnevIKt9C5lRJIBdunShS5cuAPTr1y/z6y5dutCjRw927drFDz/8YNgghRDiBVCliTsocPdyJDFh2Z95c+zbN3N7uMTTZ/Ich6nalPdqvgfA0otLCU8Kz3ObuaIo8OoMcPCCmDuw9X0pDZNNFqZqRretxJ8jG+JrryVdq2PBgZu0+H4fa47dJj0XBZp1Oh3/bLjO+T36q3rNe1Wi9stestjDAIwiAdRqtWi1WsqUKUNoaGjm11qtlpSUFK5du8arr75q6DCFEMLo2ZawoLSvE5CzkjAaFxfsOnUEIGJJ/swCvuz9MlWcqpCYnsj8c/Pzpc1cMbeF15eCygQub4bTKwwXywuojKMlw3y0LOhdk9KOloTFpfDppou0nXmA7ReDc1Tu58imG5zbcxfQJ39VmngUVNjiOYwiAXzkyy+/xMbG5onXU1NTWblypQEiEkKIF0/VpvrLwFcOPyAjLfuzNE6DBoGiEP/336TcuJHnOFSKijF1xgDwq/+v3Iy5mec2c82zNrScoH/+18cQetVwsbygWlYqye7Rzfi8oy+OVqbcDEvg7dWneH3eYQ4FhD83ETy7+07mZd9mPSX5MzSjSgAHDBhATEzME6/HxcUx4GHFeiGEEM9Wxq8E1g5mJCekcf10aLbPMytbFutWLQGIWLI0X2Kp61qX5p7NydBlMPPUzHxpM9cavgflWkJ6Evw6ENKSDBvPC8jURMWARt7sH9uckS3LY6FRc/pONL2XHOO1eYfZczUky0Qw4EQI//x6HYAGXctlrlh/loy4OMLnzUOXlpbv4xBGlgDqdLos7wO4d+8ednZ2BohICCFePCqVgm9j/SzgpYPZXwwCD2cBgZitW0kLCcmXeD6o/QFqRc3eu3s5FXIqX9rMFZUKuswHq5IQegl2fGq4WF5wNuYaxrStxP6xzenf0AszExVn7kQzcPlJOs45xJ8XHmTeI3jvaiS7l18GwK+FJzXbPn9fX51Wy/2PPiZs1o/cH/9JgY6luDKKBPDRFnCKotCqVavHtn+rXr06TZo0oXXr1oYOUwghXhi+jd1RVAoPrscQERSf7fMsa9bEok5tSEsjckX+3HpT1r4sr1V4DYAfTv5gmC3iHrFxga4L9M9PLoHzed8CrzhztjXni05VOPhRC4Y2LYulqZqLQbGMWHOaZt/tY/7Wq/w5/wLaDB3lapWk8RsVsrXgI2LhQuL37EExNcWxX79CGEnxYxQJYJcuXejcuTM6nY527drRuXPnzEePHj1YsGABq1evNnSYQgjxwrCyM6Ns9RIAXMpBSRgAp8GDAYhev56M2Nh8iWdEjRFYmFhwIfwCOwJ35EubuVa+FTT5UP986/tyP2A+cLYx55P2Phz6qCUjW5bHwVJDeGQSYX/dIy05gxR7E9zbeaLKRqmX+IMHCZv1IwCuEydg4Ve1oMMvloyi4uKj7eC8vLx48803MTc3f+KYixcvUrWq/BAIIUR2VWnmwY0zYVw9Fkz9ruUwNc/eX/nWzZphVqECKQEBRP28nhJDh+Q5lhIWJRhQdQA/nf2Jmadn0rJ0S0zVedt3OE9afAL3jsOtA/BLXxiyB8xkB4q8crQyZUzbSgxvVo41350kNTaRWEXLKm0cP847jI+bLd1qe9KlhjtO1k/uV5167x5BH44FnQ777t2x79bNAKMoHoxiBvCRfv36PZb8xcXFsXDhQl566SWqV69uwMiEEOLF41nRATtnC9KSMwg4kf37+RRFwXHQQAAiV65Em5KSL/H08+1HSYuSBMUH8fPVn/OlzVxTqfWlYWzcIPwabH1P6gPmo3N/3ib1XiJqjYqX+lSiRQ03TNUqrjyIZdIfl6n39d8MXnGS307dIyZRv8hDm5TEvZHvoY2JwbxaNVw+k3s0C5JRJYCPHDhwgH79+uHm5sb3339Py5YtOXr0qKHDEkKIF4qiUjJXW148EJSje+/sOnTAxM2NjPBwYjbnz36+lhpL3qnxDgALzi8gJuXJqg+FyrokvLEcFDVc/A1OLDZsPEXEzbNhnN5xG4CWfSvTqmEp5vasxfFPW/FV5ypU87QjXatj95UQxmw4R+3Ju+iz5Bj/vPcxKVeuoHZ0xHPWTFSmBpwhLgaMJgEMDg7m22+/pUKFCrzxxhvY2tqSkpLC5s2b+fbbb6lbt66hQxRCiBdO5fpuqE1UhN+NJzQwLtvnKRoNTv31N99HLl2KLot92nOjS/kulLcvT2xqLAvOL8iXNvOkdH1o85X++fbxcM+Aq5SLgNjwJPasvAJAjdalqFjXNfM9e0tT+jbwYsu7jdkxqinvt6pAJRcb0rU6TPbspMTBnWSg8EO9PnxxNJy/LjwgKiHVUEMp8oziHsCOHTty4MABOnTowMyZM3n55ZdRq9XMn2/AyvH5KD09ndTUnP0Qp6WlodFoSExMRKPRFFBkxkPGW7RlZ7zm5uaoVEbzmbTIMLfWUL6OM9eOBnPxwD1cvH2zfa59t26E/TSP1Nu3idv9N7bt2uY5HrVKzYd1PuTt3W+z7so63qj4Bt52Bt7rvcE7cPcoXNkKG/rB0P1g5WTYmF5AGeladiy6SEpiOi7ettTvWu6px1ZytaGSqw0ftKnIjTNXSOyvL9K9vnIbdpiVgmN3WHNMXzR6WNOyjG/vUyhjKE6MIgH866+/eO+99xg+fDgVKlQwdDj5RqfTcefOHcLDc7cHpouLC9evX8/nqIyXjLdoe954VSoVvr6+mJk9eWO4yJuqTT24djSYgJOhNOpWAXOr7H3oUFlZ4dDzLSLmzSdiwQJs2rbJlz1bG3k0oqlnUw7cO8D3J79nbqu5eW4zTxQFOs+FkMsQeUOfBPbZBOqi/+EsPx3ZdIPQ23GYWZrQdnAV1Ornf6DTpqaimvQZJilJWNapw9j539LsTiyHrofzz/VwAkLjKe1kWQjRFz9GkQAeOnSIJUuWULt2bXx8fOjTpw89evQwdFh59ij58/DwwNraWmY3hHgKrVbLrVu3uHXrFpUqVZKN4fOZi7ctTp7WRNyL59rRYKq3KpXtcx379iVy+QqSL18m4dAhrJs0yZeYxtYZy+Ggwxy4d4BDQYdo7NE4X9rNNXM76LEGFreGwIP6ItHtpxk2phfIzbNhnPtbv8dvq34+2DpZZOu8sB+mk3z5Mmo7O9y//w6NtQWtfS1o7esCQER8ChoT+bezIBhFAli/fn3q16/PzJkzWb9+PUuXLmX06NFotVp27dpFqVKlstwj2Jilp6dnJn+urq7PP0GIYs7Dw4Nbt26xceNGGjdujIuLi6FDKjIURb8YZP/aa1w8EES1lp7ZTrJNHBxwePNNIpcvJ3zefKwaN86XBN3LzouePj1ZeXkl005Mo55bPTQqA8+4OfvAawvh555wfAG4VoVafQ0b0wsgISaFPav09/1Vb10K7+ols3Ve3N69RK5YAYDbN9+gyeLfyqxKxYj8YVRptZWVFQMHDuTQoUNcuHCBMWPG8O233+Ls7EynTp0MHV6OPLrnz9pa6koJkR2PLv0+ePCAP/74g9DQ7O9hK56v4ksuaMzURIckEuQfnaNzHQcMQDE1Jen0aRJPnMi3mIZVH4aDmQO3Ym7xy7Vf8q3dPKncAZo/3Hrsj9Fw55hh4zFyOp2OPSuvkpKQTolS1jTo8vT7/v4tLTSUBw+3eHPs1xebli0KMkyRBaNKAP+tUqVKTJs2jXv37rFu3TpDh5NrctlXiOx5NKvk6urKgwcPuHbtmoEjKlpMzU2oWE8/w5LTnUE0Ls7Yva7fyi0iHxfn2Zra8m7NdwH46exPRCdH51vbedJ0LPh0BG0arO8NMTn7fhUnlw7e586lCNQmKtoMqII6G5drdTodDz79jIzoaMx8fSg5ZkwhRCr+y+izE7VaTZcuXdiyZYuhQxFCFAJFUTA1NSUmxsA14oqgqk3dAbh5JoyEmJwVd3YaNBhMTEg4fISkc+fyLabXK7xORYeKxKbGMvesgReDPKJSQZf54OwLCaGwvhekJRk6KqMTHZLIP78GAFC/S1kc3a2yd97PP5Nw8CCKqSke06ZJvT8DMfoEUIicUBSFwMBAQ4dRYJo3b87y5csNHUaBUxQFrVZr6DCKnBKeNriWtUWr1XH50P0cnWvq6YFdx44AhM/Pv/p9apWaj1/6GIBf/H8hICog39rOEzNr6LEWLBzg/hnYNAzkZzKTNkPL7uWXSU/V4lHJnuots7ewKDUwkJBp3wHgPGY0ZuXLF2SY4hkkARRPmDt3Ll5eXpibm1OvXj2OHz/+zOMvXbrE66+/jpeXF4qiMHPmzCeOOXDgAB07dsTd3R1FUdi8eXPBBJ8N+/bto1atWpiZmVG+fPnnJlTJycn0798fPz8/TExM6NKlS5bHrVmzhurVq2NpaYmbmxsDBw4kIiIi/wfwHHfu3KFDhw5YWlri7OzM2LFjSU9Pf+Y5/v7+dO7cmRIlSmBra0vjxo3Zu3fvY8coivLE4+efDbydl8gxv+aeAFzcH0RGes4SGqehQ0BRiN+7l+SrV/MtprqudWldujVanZZpJ6blaMeSAuXoDW+uBpUGLv8Ouz83dERG4/SO24TcisXUXE2rfr4oqucvDNKlpxP00UfokpKwrF8fhz59CiFS8TSSAIrHrF+/ntGjR/P5559z+vRpqlevTrt27Z55Q35iYiJly5bl22+/feqK54SEBKpXr87cuYa9xHPr1i06dOhAixYtOHv2LKNGjWLw4MHs2LHjqedkZGRgYWHBe++9R+vWrbM85p9//qFv374MGjSIS5cusWHDBo4fP86QIUMKaihPjbVDhw6kpqZy+PBhVqxYwfLly5k4ceL/2rvvqKiOt4Hj36X3onRRsYKNYg32bmwJphiNsddEY9QklqjBEmvUFGs0UVMssceosQSxV1SsxAaoIGCnl2V33j/4ua9Ih11AnM85e45778zc53HbcO+dmVzrdevWjfT0dA4ePMi5c+fw8vKiW7duREdHZyq3Zs0aoqKiNI+cOsNS6VWtvgNm1kYkxaVx61zBBtoYV6mCVec3AXj0k3ZX8RjXcBxGekacijrFwbsHtdp2kbg1z5gjEODEjxC0umTjKQUeRyZwdnc4AC171cSynEm+6j1auZKUi5fQs7TEZc5sFPIe+RIl//elTBYtWsTQoUMZOHAgtWvXZsWKFZiZmbF6dc5feo0aNeLbb7+lV69eOU7i27lzZ7755ht69OhRoHj+/vtvGjVqhImJCXZ2dgWu/7IVK1ZQpUoVFi5cSK1atRg1ahTvvfce3333XY51zM3NWb58OUOHDs2xg3vy5Enc3NwYPXo0VapUoXnz5gwfPjxfZ0+7deuGlZUVlpaWtGjRgtu3bxc6v/3793Pt2jX++OMPvL296dy5MzNnzmTp0qU5rkbz6NEjbt68ycSJE/H09KRGjRrMnTuXpKQkrly5kqmsjY0NTk5OmoeJSe5f/BEREfTu3Zty5cphbm5Ow4YNOX1ajqosSfoGetRrlbE+8KWD9wp8tq388OEAxO/dR2pomNbiqmhZkf51Mpaem3t2LknKJK21XWReH/z/yODdX6C4HVCy8ZQgtVpw8Pf/UKsEbp52moFFeUm+cpVHy5YD4DR1CobOzroMU8oH2QEsRkIIktLSi/2R3y/4tLQ0zp07l+ksl56eHu3bt+fkyZO6+m/J0e7du+nRowddunThwoULBAQE0Lhx4yK1efLkySxn8Tp16lTk/Hx9fbl37x579uxBCEFMTAxbtmyhS5cuOdaJjIykZcuWGBsba868DRo0KM/Ltbk5efIk9erVyzSHXqdOnYiLi+Pq1avZ1ilfvjzu7u789ttvJCYmkp6ezk8//YSDgwMNGjTIVHbkyJHY2dnRuHFjVq9enet7KyEhgVatWhEZGcnOnTu5ePEi48ePl/f2lQJ1WlRA30CPB3fiiQmLK1BdE3d3LNq2BSF4vHKlVuMa6jkUF3MXohOjWXV5lVbbLrJW48HrQxAq9LcNwirpbklHVCIuHbzHg/CMS7+teudv0nZ1Sgr3x4+H9HQs33wTq//dSyqVrFIxEfTrIlmpovbXOV9q1JVrMzphZpT3S/3o0SNUKlWWCXgdHR35T4v3++TXrFmz6NWrF9OnT9ds8/LyKlKb0dHR2eYXFxdHcnIypqb5m73+Zc2aNWPdunV88MEHpKSkkJ6eTvfu3XO95L106VKsra3ZuHGjZn3cmjVrFur4z+WU3/N92VEoFPz777/4+flhaWmJnp4eDg4O7N27F1tbW025GTNm0LZtW8zMzNi/fz+ffPIJCQkJjB49Ott2169fz8OHDzl79izlypUDoLq84btUMLU0omZjR0JORHEx4B5OVa0LVN9uxHASDh4k9u+/sRs1CoWjg3biMjBlYuOJjA4czdqra+lerTtVratqpe0iUyig+w8QF4Ei7AhvhC6E2G5gV8LrGBejuEfJnP4rFICm71bHwjZ/kzQ//O570kJDMbC3x8n/a7nSTykhzwBKpVZwcDDt2rUr6TDy5dq1a3z22Wd8/fXXnDt3jr179xIeHs6IESNyrBMcHEyLFi00nb+SIoRg5MiRODg4cPToUc6cOYOfnx/du3cnKipKU27q1Kk0a9YMHx8fJkyYwPjx4/n2229zbDc4OBgfHx9N508qXTz/N2rz9oWHxD9JKVBdU09PzJs2BZWKxz9r90xd64qtaenaknR1OrNPzy49A0IADIyg5+8IO3dMlU8x2PAeJBZurfdXjRBwZMNN0pUZo35rN3fJV72k8+d58ttvADh/MxODF/6olEqWPANYjEwN9bk2o1OJHDc/7Ozs0NfXJyYmJtP2mJiYElnOrrBn43Lj5OSUbX5WVlZFOt6cOXNo1qwZX375JQCenp6Ym5vTokULvvnmG5yzud9FV/m9fN/h83xzeg0PHjzIrl27ePr0KVZWVgAsW7aMAwcO8OuvvzJx4sRs6zVp0oSZM2eSmpqa7b2fushP0h47Vwsq1LQh8sYzrhyOxLdH/lZw0NT/eASJJ04Qu3Ub1oMHay0uhULBxMYTOR11mtNRp9l3Zx9vur2ptfaLzNSG9N6bUK5og9njW/DHO9B/F5hYlXRkOpUUYcjTG7EYGOrRuo9Hvi/9Rn01GYTA2s8Pi1atiiFSKb/kGcBipFAoMDMyKPZHfk+3GxkZ0aBBAwIC/v8GZ7VaTUBAAL6+vrr6b8mRp6dnpli0wdfXN0ubBw4cKHJ+SUlJWVZ90dfP6HjndAbD09OTo0ePolQqi3TsF/n6+nL58uVMo7YPHDiAlZUVtWvXzrZOUlLGzfYvx6+np5fr/XrBwcHY2trmOPDH09OT4OBgnjx5UtA0pGLy/Czg1WORKNNUBapr1qgRZo0aIZRKnv78s1bjqmhZkcH1MjqV3575lkRlolbbLzKrCpysPgFhZgdRFzPWDlYW7CzqqyQxNpVn/2V8zhu/VRUbB7N81Xu0ZAlp4eEY2NvjOCn7PyRzE5kQyZeHv+RpytMC15XyJjuAUibjxo1j1apV/Prrr4SEhPDxxx+TmJjIwIEDNWX69evHpEmTNM/T0tIIDg4mODiYtLQ0IiMjCQ4O5tatW5oyCQkJmjKQMR1LcHAwd+/mfCO1v78/GzZswN/fn5CQEC5fvsy8efOKlN+IESMIDQ1l/Pjx/PfffyxbtoxNmzYxduxYTZklS5ZkufR87do1TWcmNjY2Uy4A3bt3Z9u2bSxfvpzQ0FCOHz/O6NGjady4MS4u2V8qGTVqFHFxcfTq1YugoCBu3rzJ77//XqQl0Dp27Ejt2rXp27cvFy9eZN++fUyZMoWRI0dqOmpnzpzBw8ODyMiM5a18fX2xtbWlf//+XLx4kRs3bvDll19qpsyBjNHYP//8M1euXOHWrVssX76c2bNn8+mnn+YYS+/evXFycsLPz4/jx48TGhrK1q1bS2RAkZQ9N087rOxMSE1M58bp7O8RzY3dpxnLuMVt3YbB02dajW1Q3UFUtKzIg+QHLA9ertW2tSHBxJn0Xn+CkSWEH4UtA0FV+AFcpdmp7WGIdAX2lSzwauuarzrJly7xePUaAJymT0PfumD3maap0vj80OfsDd/LjJMzChyzlA9CKrTY2FgBiNjY2Cz7EhMTRVBQkEhMTCyByIpm8eLFolKlSsLIyEg0btxYnDp1KtP+Vq1aif79+2ueh4WFCSDLo1WrVpoygYGB2ZZ5sZ3sbN26VXh7ewsjIyNhZ2cn3nnnnVzLAyIsLCzXMoGBgZo2q1atKtasWZNpv7+/v6hcuXKmbZUrV842/hf9+OOPonbt2sLU1FQ4OzuLPn36iIiIiFxjuXjxoujYsaMwMzMTlpaWokWLFuL27ds5lm/VqlWWeF8WHh4uOnfuLExNTYWdnZ34/PPPhVKpzJT/y/9PZ8+eFR07dhTlypUTlpaW4o033hB79uzR7P/nn3+Et7e3sLCwEObm5sLLy0usWLFCqFSqPGN59913hZWVlTAzMxMNGzYUp0+fzrbs88/Mli1bxPz588Vff/2Va9ulWVpamtixY4dIS0sr6VDydOHAHbFkeIBYP/2UUKvVBa4f3q+/uObuIU707af1fI/cOyLqrq0rvH71Ejee3NBq20WR6fUNOyrEDHsh/K2E2DpUCFV6SYenVRH/PRFLhgeIJcP/FfdvP8lXHVVqqrjVtau45u4hIj7/olDHnXlypqi7tq5otqGZiIyPLFQbucnt9/t1oRCiNN1h+2qJi4vD2tqa2NhYzb1TzyUlJRESEkKtWrUwM8vf6XKp6BQKBWFhYbi5uZV0KDrRunVrBgwYwIABA0o6FK17/pkJDw8nNDQUd3d33nrrrZIOq1CUSiV79uyhS5cuJT7IJy+pyen8OvE4ylQVb33mTcVaBRu0kxQUxJ2P+iL09HDbsxszLX/2xgSOIeBuAD4OPqx9cy16ipK/cJXl9f1vD/z5EQgVePXOmDhaL3/3XpdmKpWaTbPO8uR+IuaV0ujzZbt8vZ8ffP89j1f8hH758lTd9XeBB37sDt3NxKMZl4yXtVtGC9cWhYo/N7n9fr8uSv6TJEmSJJUYY1MDPHwzBildOnivwPXNGjbE9I03UKjVPF2l/bn7JjSagJmBGRceXGDLjS1ab18rPLrAe6tBoQ8XN8Bfo0BdsHsqS6PLgRE8uZ+IsbkB1jVS81Un+epVHq/KuCfU6euvC9z5u/3sNtNPZkz9NcxzmE46f1IG2QGUJEl6zXm2ybivK/zKY549KPgKHOVGfgJA3F87SYuI0GpszhbOjK6fMdfkonOLiEmMyaNGCanjB+/98r9O4HrYORpe4UnPE2NTObMrY6WXxt3d0DPKu45IS8sY9atSZUz43KljgY6ZpExi3KFxJKcn08SpCZ94fVKY0KV8kh1AqUzx9/fHxsampMPQmQEDBuDt7V3SYUhljI2jGZXrlQcBlwIL3oEz9fYmsUYNSE/n0XLtD9jo5d4LTztPEpWJzDo9q3TNDfiiOj3g3VUZncDgP2DnqFd2YMiJbbdQpqhwqGyJh2/+pgF7tGoVqdevo29jg9PUKQU6nhAC/xP+hMaG4mDqwLyW89AvA5fRSzPZAZTKlGnTpskOoCQVgtf/poQJORFFSmLBpyZ63KEDALE7/iItl9H9haGvp8+0ptMwUBgQeC+Qf+/+q9X2taruu//rBOpB8DrYMgDS83f5tLS4f/MZN07HgAJa9nZHoZf3VGIp12/waMVPADhOmYJB+fIFOua6kHXsDd+LgcKABa0XUN60YPWlgpMdQEmSJAlXD1vKu1qQnqri6tHIAtdPqVwJs2bNQKXi0fIVWo+vhm0NBtUbBMDs07OJSyvYGsbFqu670PM30DeCkL9hfU9ITSjpqPJFrVJzZOMNAGo3c8HRLe8BEiI9naivvgKlEot27bDqmvMa6Nm58OACC4MWAvB5w8/xcfApeOBSgckOoCRJkoRCocCnQyUALh2MQKUs+P1rz+8FjP3rL9LCw7UZHpAxKMDNyo1HyY9YFLRI6+1rVa3u0GczGJpD6CH43Q+SSv+k6FeORPI4MgFjcwPe8MvfOsyPf1lNytWr6FlbF3it30fJj/j80Oeki3TedHuTPrX6FDZ0qYDKbAdQpVIxdepUqlSpgqmpKdWqVWPmzJmae0eUSiUTJkygXr16mJub4+LiQr9+/bh//34JRy5JklQyqjd0wNzGmKS4NK6fKfjE0Cb16mUs96VW8/DHxVqPz1jfmGlNpwGw9eZWzkaf1foxtKpqa+i/E0xsIOIsrOkMT++UdFQ5SopL4/TOjIEfb7xdDVOLvEd+pN6+zaMlSwBwnDQRQweHfB8vXZ3Ol4e/5GHyQ6pZV2N60+kF6jxKRVNmO4Dz5s1j+fLlLFmyhJCQEObNm8f8+fNZvDjjSykpKYnz588zdepUzp8/z7Zt27h+/forO++YJElSUenr62nuBQw+cBehLvhgC/sxnwEQt2cPKSEhWo0PoIFjA96r+R4AXx//miRlwUctFyvXhjDwH7B0hof/wc/tICKopKPK1sntt0hLTse+kiW1m2e/gtGLhEpF1OQpCKUS85YtsH777QId74fzPxAUE4SZgRmL2izCzFDOmVucymwH8MSJE7z99tt07doVNzc33nvvPTp27MiZM2cAsLa25sCBA/Ts2RN3d3feeOMNlixZwrlz53JdnkySJKksq9PCBSMTfZ5GJ3Hn6uMC1zepVQur/y0h+OC777QdHgDjGozDydyJiIQIFp0r5ZeCARxrw5AAcKwHiQ9hbVe4uqOko8okOjSW/05mnPVt2asmevkY+PF03TqSg4PRMzfHeXrBzt4duHOAtVfXAjCz2UyqWufvcrOkPWW2A9i0aVMCAgK4cSPjZtaLFy9y7NgxOnfunGOd2NhYFApFmR5FWtYpFArCdXDvUWnRunVr1q5dW9JhSGWYkakBtVtUADLOAhaG/ehPwcCAxCNHSTqr/cu0lkaWzGiasT7sn9f/5OT9V2B9aesKMOgfqNEJ0lNgc384uhBKwZQ2arXg8IaMNchrNXXGqWre6/am3bvHg+++B8Dhyy8wdHbO9/HCYsOYenwqAP1r96ejW8HmC5S0o8x2ACdOnEivXr3w8PDA0NAQHx8fxowZQ58+2d9gmpKSwoQJE+jdu3eOy8KkpqYSFxeX6QEZ9xNm93gVHTlyhO7du+Pi4oJCoWDHjh35qnfo0CHq16+PsbEx1atXz7aTEhkZyUcffUT58uUxNTWlXr16BAUV76WQqKgoPvzwQ2rWrImenh5jxozJV73Ro0fToEEDjI2Ns52G5fr167Rp0wZHR0dMTEyoWrUqU6ZMKZH3QV6xZic6Opq+ffvi5OSEubk59evXZ+vWrZnKPHnyhD59+mBlZYWNjQ2DBw8mIUH7IxuFEAghUKvVOX62XoUH5PzdUNoftVs4odBTEHnjGfdvPylwvgoXF6ze6QFAzMJFpKWlaT3GhvYNeb/G+0DGpeAnifmLs0RfXz0TlO/9iqrh0Iw3e8AM1Bs/QplQvLG//Lh86C6P7iVgZGpAw26V8sw3LS2N+1OmIpKTMWnYEPMePfJ9rNikWMYEjiFRmUh9h/qM9BxZYnm/7gxKOgBd2bRpE+vWrWP9+vXUqVOH4OBgxowZg4uLC/37989UVqlU0rNnT4QQLM9lEtM5c+Ywffr0LNv379+fZb1fQ0NDHB0dtZNMMUpMTMTLy4tBgwbxzjvv5KtOWFgYXbt2ZcSIEaxbt46AgACGDBmCs7MznTp1AuDp06c0a9aMNm3a8M8//2Bvb8/NmzexLeAyQUWVmpqKvb09U6ZM4bsCXp4aNGgQp0+f5tKlS1n2GRoa0q9fP+rXr4+NjQ0XL15k6NChqNVqZs+era3wtRJrdvr168ezZ8/YuXMndnZ2rF+/np49exIUFISPT8aUDH369CEqKooDBw6gVCoZOHAgw4YNY/369VqN/e7du9y/fx+lUvnK3xB+4MCBkg6h0EydTEi6b8jeP85S3jslX3VezFe/Rg2qGBqSEhzMkYULSaxdW+sxeggPbPVsiU6KZuzOsbxjlr/vLG0p/OvbgsoVlXhG/I7e9V0khQdxpspo4k1dtRpffqjSFEQfMQcUmFVJIPBoznMsPs/X6sxZnE6fRm1oSEib1lzauzdfxxJCsClpE6HKUCwVlnRI6cD+vftzLG+eGkPdiHUEVxpEqqFNAbLKW1JSKb93tDiIMsrV1VUsWbIk07aZM2cKd3f3TNvS0tKEn5+f8PT0FI8ePcq1zZSUFBEbG6t53Lt3TwDi0aNHIi0tLdPj2bNnIigoSCQmJmo9t+ICiO3bt+dZbvz48aJOnTqZtn3wwQeiU6dOmucTJkwQzZs3L3AMO3fuFA0bNhTGxsaifPnyws/PL8+Yw8LC8tV2q1atxGeffVagePz9/YWXl1e+yo4dOzbPnK9cuSK6du0qLC0thYWFhWjevLm4detWjuVbtWol1qxZo/VYzc3NxW+//ZZpW7ly5cSqVauEEEJcu3ZNAOLs2bOa/f/8849QKBQiMjIyx3afPn0qhg0bJhwcHISxsbGoU6eO+Pvvv7Mtm5iYKIKCgsTmzZvFvHnzxPbt27N8rl6VR2JiotixY4dITEws8VgK+4gKfSKWDA8QSz8OEI+j4gqV7/3534pr7h7iVtduIjU5WSdxnoo4JeqtrSfqrq0rAsICXqnXVxl2UqgXeAjhbyXU3zgJZdBvIi01tVhf53/XXhVLhgeIDTNOiZTk7I/9Yr5JERHiv4aNxDV3DxGzalWBjrUyeKWou7au8P7VW5yOOJ1z2dQUkX5iuVB/4ySEv5VQbeyr9bwfPXokABEbG5uv78iyqMyeAUxKSkJPL/MVbn19fdQvrM34/MzfzZs3CQwMpHweM5cbGxtjbGycZbuhoSGGhoZZtmUhBJTEiDVDM9DhmZSTJ0/Svn37TNs6deqU6fLqzp076dSpE++//z6HDx+mQoUKfPLJJwwdOjTHdnfv3k2PHj2YPHkyv/32G2lpaezZs0dXaWjVrVu32Lt3b65nUSMjI2nZsiWtW7fm4MGDWFlZcfz4cdLTi3/pqKZNm/Lnn3/StWtXbGxs2LRpEykpKbRu3RrIeI1tbGxo2LChpk779u3R09Pj9OnT9OjRI0ubarWazp07Ex8fzx9//EG1atW4du0a+vq5L++kUChQKBTo6ell/zl6hWT33fCqcKpiS8VattwLecrVI1G06Fkzzzov5+swbChxmzeTdusWyfv3Y62DWRaaVGhC39p9+e3ab8w8M5Ntb23D1qR4riwU+fV1ewNGHIUtg1CEHcbg71Fw+1/o9h2YldNeoDmICYvjv1MZAz9a9XbH2CT3aV8MDAyImTUbdXw8Jp6e2A8ciCKPz/NzRyKOsDg4YxaOiY0n0rhC4+wLPg2Hvz/LmDsRoEpL9DrNQk/Ln6NX9XOpTWW2A9i9e3dmzZpFpUqVqFOnDhcuXGDRokUMGpQxk7xSqeS9997j/Pnz7Nq1C5VKRXR0xgehXLlyGBnlY+XrglImwey8h9Zr3Vf3wchcZ81HR0dnudzt6OhIXFwcycnJmJqaEhoayvLlyxk3bhxfffUVZ8+eZfTo0RgZGWW5JP/crFmz6NWrV6bL7l5eXjrLQxuaNm3K+fPnSU1NZdiwYcyYMSPHskuXLsXa2pqNGzdqvoxq1sz7R1YXNm3axAcffED58uUxMDDAzMyM7du3U716dSDjNXZ4aX4vAwMDypUrp/ncvOzff//lzJkzhISEaPKqWlWO9HuV+HSozL2Qp1w7HkWjrlUwMS/Yj6a+tTXlhwzh4aJFPPxxMVZvvolCB9+tn/p8ytHIo4TFhvH1ia/5sc2Pr87tA+Z20Hc7HPsODs2Bazvg3hnosQKqttLZYdVqwZGN10GA+xtOOFe3ybNOwt69JBw8CIaGOH8zM9+dv/DYcCYemYhA8F7N9+jp3jNrIZUSTi2DwDmQngwGptBhBjQaAnpldrhCiSqz/6uLFy/mvffe45NPPqFWrVp88cUXDB8+nJkzZwIZZ1927txJREQE3t7eODs7ax4nTpwo4ejLHrVaTf369Zk9ezY+Pj4MGzaMoUOHsmJFzktGBQcH065du2KMsuj+/PNPzp8/z/r169m9ezcLFizIsWxwcDAtWrQoFX+JTp06lWfPnvHvv/8SFBTEuHHj6NmzJ5cvXy50m8HBwbi6upZYp1YqOtdatthVzFge7lJgRKHaKNf3Iwzs7VFGRPB040YtR5jBxMCE+S3nY6hnyKF7h9h0fZNOjqMzevrQ8gsYfADKV4f4+/DbW7DzU0h+qpNDhhy/z4M78RiZ6NP0nep5ltdPSODhnLkA2A0fjkk+P9cJaQmMDhxNvDIeHwcfvmr8VdbOecQ5WNkGDnyd0flzawEjjkGTYbLzp0Nl9gygpaUl33//Pd9//322+93c3DSrghQbQ7OMs3HFTceTazo5ORETE5NpW0xMDFZWVpiamgLg7OxM7ZduAq9Vq1aWkaYvel73VVKxYsYkurVr10alUjFs2DA+//zzbC97lpb8bt++zZIlS7hy5Qp16tQBMs60Hj16lKVLl7JixQqcnJx48OBBpnrp6ek8efIEJyenbNstLflJhadQKKjfqTL7f77KpYP38G5fESOTgv1s6JmaYvfpKKK/9ufh0mVYv/UW+jqYasujnAdjG4xl/tn5fBv0LfUd61PDtobWj6NTFerD8COwfyoE/QLnf4Pre+HNORnrC2vprGZKgpKTO24D0Lh7Vcys8j4ra//3LtRPn2JcsyZ2w3K+dedFaqFm4tGJhMWG4WjmyKLWizDUf+EP3vhoODgTLqwDBJjaQsdZ4P2hTm9bkjLIrnVxUigyLsUW90PHHyRfX18CAgIybTtw4AC+vr6a582aNeP69euZyty4cYPKlSvn2K6np2eWdl8lz6cxefG+0xd5enpy9OjREp+O4PlouNzumfX19eXZs2ecO3dOs//gwYOo1WqaNGmSbbuenp5ERERo5uKUXk3V6jtg42hGalI6V48U7g9Ym3ffxbhGDdSxsTxanvNZ/6L6qNZHNK/QnFRVKuOPjCclPX+jl0sVI3PotggG7gU7d0h8AFsHw29vQ1T+RvXn5dTOUFIT0ylfwZx6rSvkWT7x8GGsgoNBTw/nWd/k+zL+kgtLOBxxGGN9Y35o8wN2pnYZO9KS4PB8+LE+XPgDEOD5AYwKAp8+svNXTGQHUMokISGB4OBggoODgYwpXoKDgzOtjjJp0iT69euneT5ixAhCQ0MZP348//33H8uWLWPTpk2MHTtWU2bs2LGcOnWK2bNnc+vWLdavX8/KlSsZOXJkjrH4+/uzYcMG/P39CQkJ4fLly8ybN6/IOT7PLyEhgYcPHxIcHMy1a9c0+7dv346Hh0emOrdu3SI4OJjo6GiSk5M1baSlpQGwbt06Nm3aREhICKGhoWzatIlJkybxwQcf5HiJd9SoUcTFxdGrVy+CgoK4efMmv//+e5aOckHlFWtkZCQeHh6aVXE8PDyoXr06w4cP58yZM9y+fZuFCxdy4MAB/Pz8gIyztW+++SZDhw7lzJkzHD9+nFGjRtGrVy9cXLK/r7VVq1a0bNmSd999lwMHDhAWFsY///zD3nxOGSGVDnp6Cup3qgRA8L93SVeqCtyGQl8fhwkTAHiyfj1pd3SzHq5CoWBms5mUNynPrWe3Xo1VQnJS2TdjgEjrr0DfCMIOw08tYfvHEBtZ6GYf3Inj6tGM+i171URPP/dugCo+ngczMm6dsunfD9N69fJ1nH3h+1h1eRUA/r7+1LGrA8pkOLUcfvSBwFmgTIQKDTMufb+zMuN+SKn4lPQw5FdZbGxsjsPIn09p8apNAxMYGCiALI/+/ftryvTv31+0atUqSz1vb29hZGQkqlatmu1UJX///beoW7euMDY2Fh4eHmLlypV5xrN161ZNu3Z2duKdd97JtTz5mAYmu/wqV66s2b9mzRrx8kejVatW2dZ7fqyNGzeK+vXrCwsLC2Fubi5q164tZs+eLZKTk3ON5eLFi6Jjx47CzMxMWFpaihYtWojbt2/nWD4/08DkFWtYWJgARGBgoKbOjRs3xDvvvCMcHByEmZmZ8PT0zDItzOPHj0Xv3r2FhYWFsLKyEgMHDhTx8fG5xvL48WMxcOBAUb58eWFiYiLq1q0rdu3alW3Z55+ZLVu2iPnz54u//vor17ZLs7S0NLFjxw6RlpZW0qFoRXq6SqyddEwsGR4gLgXey7I/v/neGTJUXHP3EPdGfaqrUIUQQhyLOCbqrq0r6q6tK/aH79d6+8X++j4JE2LzQCH8rTIeMx2E2DNeiGcRBWpGrVKLzXPPiiXDA8S+n6/kq879KVPFNXcPEdy0mUiJi8tXnUsPLokGvzcQddfWFQvOLhAiNUGIE0uF+LbG/+ewqK4QlzYLoVYXKAdtye33+3WhEKIUrEPzioqLi8Pa2prY2Ngsq4ckJSUREhJCrVq1skwSLemOQqEgLCwMNze3kg5FJ1q3bs2AAQMYMGBASYeidc8/M+Hh4YSGhuLu7s5bOpg2pDgolUr27NlDly5dSsUgH224fCiCIxtvYFnOhD4z30D/hTNH+c039eZNQt/2A7Wayn/8jtkL0wpp26KgRay5ugZzQ3M2dt2Im7Wb1tousdc34hzsnwJ3/zdQUc8QvHvDG5+AQ608q187fp/A3//D0FifPtPfwNwm67RmL0o8dYq7AwYCcG/4MNqMGpVnvpEJkXy4+0OepDyhpWNDfsQZ/eA/ICU2o4B1RWgxDrz7gEHux9el3H6/XxfyErAkSZKUp1pNnTG1MiL+SQo3z8TkXSEbxjVqYPN+xvJtMXPnIXK4P1YbRtcfTQPHBiQqExl7aCxJJTEHq7a5NoCBezKmjancHNTKjIEiy96ANV3g8hZIT822akqikpPbMwZ+NOpWJc/OnzopiaipXwNg9UFPkvMxhVN8Wjwj//2YJylPcMeE+ad3oH9qaUbnz7YKdP8BPj0PDQeVaOdPyiA7gJIkSVKeDIz08W6XMcr93N47qNWFu3hk/+ko9MzMSLlyhbjdu7UZYiYGegZ82/Jb7EztuPXsFjNPzSz+mR90QaGAam1h4G4YtA88uoFCH+4czxgs8m2NjPsEbx6A9DRNtTM7Q0lJUGLrbI5n27yXnHuwYCHKe/cwcHbGLq8109OSUP73D59v7sLt2DAc0tNZcvc25kIN1drBh5syOn4NBoCBDubYlQqlzE4DI72e/P39sdHBFBOlxYABA/D29i7pMKTXVN1WFTi/7w7PYpIIvfCQ6g0c8q70EgM7O8oPG8bD77/nwYKFWLZti565biaqtzez59uW3zJk/xB2he7C296bDzw+0MmxSkSlNzIesZEZZwIv/A5xkXBxfcbD0BzcmvPQ5k2uHHEDMgZ+6Ocx8CPx1Cme/m+Nb5dZ36BnYZG5gDIFoi5CxBkIPYwIP8psa1NOWlliqlazOFEfJ9/R4PUh2Mt5QEsr2QGUypRp06aVdAg6VRbv/ZNeHUYmBtRr40rQ7nDO7Q2nWn37Qq24UW5Af55t3owyMpJHP63EYdzYvCsVUkOnhoxtMJYFQQuYe3Yu7uXc8Xbw1tnxSoR1BWgzCVpNgHun4Mo2uPYXJD5A3NjHkSetEAKqm57A9cg3cL0O2LqBTWWwdgVjKzCxAkMzVAmJRE2aBIDNWx0wd0wl/cpm3KP2o79jBzy5DTHXMi4//88aa0u2WFmiB8yvN4ra9YfLCZxfAbIDKEmSJOWbV5uKBP97j0f3Egi/9IgqXvYFbkPPxATHryYRMXIUj9eswbqHH8ZVqugg2gz9avfj4sOLHLhzgM8CP2ND1w24WJTAspy6pqcHlZtmPDrPh5grXD9wgeiYihgoUmhmsRoiH0PkuRybeHDWGmWUOYbm6Tga/ga//4oB4AHw4qqP5vbg2pgdNuX47sFhAL5sNIHWtT/SZYaSFskOoCRJkpRvJhaGeLZx5fzeO5z+Owy3eoWbu82ibVvMW7Qg8ehRYmbPoeLKn3S2fq9CoeCbZt9wN+4u159e59ODn/J7598x0/EqSSVKT49Um9qcuBIHpNHo7dpYNDoAUcHw8AY8uwPP7kLcfUiNg5Q4EiL1eHY743K88xvx6NnYgbk9atNy3ItX4OrdBn0HD3CsAzaVORRxmGmBYwAYUGcAH8nO3ytFdgAlSZKkAvFpX4nLhyJ4HJFA6MWHVKprW+A2FAoFTpO/IrT7WyQePUpCYCCWbdvqINoMZoZmLG67mN67e3Pj6Q0mHZ3Ed22+Q09Rdi9Vnvk7lOS4NGwczfBqXwkM9KB8tWzLquLiiOreHXiAbd+PMJ88+f/3KZUE79mDi28X9P83Dcz5mPN8cfgLVELFW9XeYmwD3V3Gl3Sj7L7zJUmSJJ0wsTDEq23GiOAzf4chCjki2MjNjXIDM+aZi5k9B3WKbpduc7Zw5vs232OoZ8jBewf58fyPOj1eSXocmcDlQ/9b8eODmugb5P5zHzNnLukxDzCsXAmHsbl35q4/uc6ogFGkqlJp5dqKaU2nlemOdFklXzFJkiSpwLzaVcTI1IAn9xMJDX5U6HbsRgzHwMkJZUQEj3/+RYsRZs/bwZvpTacD8MuVX9j430adH7O4CSE4svEGQi2o5mNPxdrlci0fHxhI7PbtoFDgMmcOerksXnA37i4f//sx8cp4fBx8+LbVtxjqlY3Jzl83sgMoSZIkFZiJuSHe7f83L+A/dyjsFHt6ZmY4ThgPwOOVK0kNC9NWiDnqXq07n3h9AsDs07P5986/Oj9mcbpxOpr7N59hYKRHs/dr5Fo2/elTor7OmPC53IABmNWvn2PZiIQIBu0bxMPkh1S3qc7itosxNTDVauxS8ZEdQKnMCA8P19lN5KWFQqEgPDy8pMOQJAA821bE2MyAZ9HJJEcV/pZyyzffxLx5c0RaGtH+04plwuYRXiN4v+b7CAQTjkwgKDpI58csDqlJSo5vvQVAwy5uWJYzybGsEIKoKVNRPXyEUbVq2H82OseyT9VPGfbvMGKSYqhiXYVVHVdhbWyt9fil4iM7gFImc+bMoVGjRlhaWuLg4ICfnx/Xr1/Ps97mzZvx8PDAxMSEevXqsWfPnkz7t23bRseOHSlfvjwKhYLg4GAdZZC7VatW0aJFC2xtbbG1taV9+/acOXMmz3qHDh2ifv36GBsbU716ddauXZtpv0qlYurUqVSpUgVTU1OqVavGzJnFv/LAtm3b6NChA/b29lhZWeHr68u+fftyrTNt2jQUCkWWh/kLk/OuXbs2y34Tk5x/WKTXg7GpAd4dKgEQd8sYtapw73eFQoHTNH8UJiYknTlD7Lbt2gwzx2NObjKZthXbkqZOY/TB0Vx/kvd3XWl3+u8wkuOV2Dia4d2+Uq5ln23eTEJAABgaUmHBt+jl8JmOToxmdcJqopOicbNy45eOv2BnWrjR31LpITuAUiaHDx9m5MiRnDp1igMHDqBUKunYsSOJiYk51jlx4gS9e/dm8ODBXLhwAT8/P/z8/Lhy5YqmTGJiIs2bN2fevHnFkUaODh06RO/evQkMDOTkyZNUrFiRjh07EhkZmWOdsLAwunbtSps2bQgODmbMmDEMGTIkU8dq3rx5LF++nCVLlhASEsK8efOYP38+ixcvLo60NI4cOUKHDh3Ys2cP586do02bNnTv3p0LFy7kWOeLL74gKioq06N27dq8/781W5+zsrLKVObOnTu6Tkd6BXi2ccXY3ID0RD1uBT0odDtGrq7Yf/opADHz55P+qPD3FeaXvp4+81rOw8fBh3hlPEP3D+Xm05s6P66uPLwbz5VDEQC07J37wI/UsDBi5swFwGHMGExq1cq2XFRCFMMChvFU/RRXC1d+7vgz9mYFn/tRKoWEVGixsbECELGxsVn2JSYmiqCgIJGYmKjZplarRWJaYrE/1Gp1oXN88OCBAMThw4dzLNOzZ0/RtWvXTNuaNGkihg8fnqVsWFiYAMSFCxfydfxffvlF1K5dWxgZGQknJycxcuTIHMs+b7sg0tPThaWlpfj1119zLDN+/HhRp06dTNs++OAD0alTJ83zrl27ikGDBmUq884774g+ffrkevydO3eKhg0bCmNjY1G+fHnh5+eXa3lAhIWF5VrmZbVr1xbTp0/Pd/ng4GABiCNHjmi2rVmzRlhbWxfouEIULL/nn5ktW7aI+fPni7/++qvAxyst0tLSxI4dO0RaWlpJh1Iszuy+LZYMDxC/Tjom0tNUhW5HrVSK2z16iGvuHiJi3OdajDB3samxouffPUXdtXVFy40txe2nt3MtXxpfX7VKLbbMOyuWDA8Qe1ddzr1sWpoIffc9cc3dQ4T3HyDUquxfs7BnYaL95vai7tq6osVvLcTdp3d1EXqJyO33+3Uh5wEsRsnpyTRZ36TYj3v6w9OFnvA0NjYWgHLlch5FdvLkScaNG5dpW6dOndixY0ehjvnc8uXLGTduHHPnzqVz587ExsZy/PjxIrX5sqSkJJRKZZ75tW/fPtO2Tp06MeaFBdKbNm3KypUruXHjBjVr1uTixYscO3aMRYsW5dju7t276dGjB5MnT+a3334jLS0ty6XzolKr1cTHx+ea38t+/vlnatasSYsWLTJtT0hIoHLlyqjVaurXr8/s2bOpU6dOju0UR35S6VC3lQtB+0KJf5LKlSOReLWrWKh2FAYGOM+YSXjPnsTt3o31229h0bKllqPNysrIipUdVjJk/xD+e/Ifg/cPZnWn1VSx1t3qJNoWcjKK6NA4DI31afZu7gM/Hi5ZSsqVK+hZW+Mydw6KbJZtu/7kOsMODONJyhPcrNx4j/dwMnfSVfhSCZAdQClHarWaMWPG0KxZM+rWrZtjuejoaBwdHTNtc3R0JDo6Ooca+fPNN9/w+eef89lnn2m2NWrUqEhtvmzChAm4uLhk6eC9KKf84uLiSE5OxtTUlIkTJxIXF4eHhwf6+vqoVCpmzZpFnz59cmx31qxZ9OrVi+nTp2u2eXl5FT2pFyxYsICEhAR69uyZr/IpKSmsW7eOiRMnZtru7u7O6tWr8fT0JDY2lgULFtC0aVOuXr2Kq6trtm0VR35S6WBgpI91jTSeXjEhaE84Hk2dMTYt3M+Lad06lOvXjydr1xI1bRpVd+5E38JCyxFnZW1szaoOqxi8fzA3nt5g8L7BrOywkuq21XV+7KJKSVRycvttABp1q4KFrXGOZZOCgni8ciUAztOnYeiUtVMX/CCYTwI+IT4tHo9yHixpvYRTB0/pJnipxMgOYDEyNTDl9IenS+S4hTFy5EiuXLnCsWPHtBxR3h48eMD9+/dp166dzo4xd+5cNm7cyKFDh4o8oGHTpk2sW7eO9evXU6dOHc29gi4uLvTv3z/bOsHBwQwdOrRIx83N+vXrmT59On/99RcODg75qrN9+3bi4+OzxOzr64uvr6/medOmTalVqxY//fQTM2fOzLYtXecnlS5mFZSIh7Y8i0nmwr47vOGX/YoT+WE/+lPi//0XZUQEMXPn4vLNN1qMNGc2Jjas6riKwfsGc+vZLQbsG8CydsvwtPcsluMX1qm/QklJUFLOxRzPttn/QQagevaMyPHjQQise/TA6s03s5Q5cOcAk45OIlWVio+DD0vaLcFUIad6KYvkIJBipFAoMDM0K/ZHYaZGGTVqFLt27SIwMDDHMzzPOTk5ERMTk2lbTEwMTtn8ZZlfpqa6/cJZsGABc+fOZf/+/Xh65v7lnlN+VlZWmji//PJLJk6cSK9evahXrx59+/Zl7NixzJkzJ8d2dZnjxo0bGTJkCJs2bcr17ObLfv75Z7p165bljOfLDA0N8fHx4datWzmW0fVrKJUuCj1o3N0NgIsB90h4mlrotvTMzHCZMxsUCmK3bCX+0CHtBJkP5UzKsfbNtXjaeRKbGsuQ/UM4cf9EsR2/oGLC47h6NGMQW6veNdHXz/5nXQjB/a8mk34/CsNKlXB8Yam35/tXX1nNuEPjSFWl0tK1JSvar8DKyErnOUglQ3YApUyEEIwaNYrt27dz8OBBqlTJ+x4YX19fAgICMm07cOBApjNGBWVpaYmbm1uWdrVh/vz5zJw5k71799KwYcM8y+cnv6SkJPReuo9GX18ftVqdY7uenp46yW/Dhg0MHDiQDRs20LVr13zXCwsLIzAwkMGDB+dZVqVScfnyZZydnXMso6v8pNKrsmd5nKpak65Uc3Z30SZ0NmvUiHL/OxMdNXUq6U+faiPEfLE2tmZVx1X4OvuSnJ7MyICR7Li1o9iOn19qlZrD66+DgJpNHHGpkfOazE9+/ZWEgwdRGBri+v136Fv8/zRPSrWS6Sen89257wD40ONDfmjzQ6HvHZdeDbIDKGUycuRI/vjjD9avX4+lpSXR0dFER0eTnJysKdOvXz8mTZqkef7ZZ5+xd+9eFi5cyH///ce0adMICgpi1KhRmjJPnjwhODiYa9euAXD9+nWCg4NzvU9w2rRpLFy4kB9//JGbN29y/vz5Ik+rMm/ePKZOncrq1atxc3PT5JeQkKApM2nSJPr166d5PmLECEJDQxk/fjz//fcfy5YtY9OmTYx9Yb3M7t27M2vWLHbv3k14eDjbt29n0aJF9OjRI8dY/P392bBhA/7+/oSEhHD58uUiT5Ozfv16+vXrx8KFC2nSpIkmv+eDeQCWLFmS7aX11atX4+zsTOfOnbPsmzFjBvv37yc0NJTz58/z0UcfcefOHYYMGVKs+Umlm0KhwPedjEu/Icfv8zQ65+mj8sN+zGcYVauG6uEjYr6ZpY0Q883M0Iwl7ZbwptubpKvTmXp8KovOLUKlVhVrHLm5FBjBw7vxGJsZ5DrwI/niRR4sWAiA41eTMKldW7PvYdJDhuwbwtabW9FT6DGx8UQmNZmEgZ68Q6zMK+FRyK+0gk4D8yoAsn2sWbNGU6ZVq1aif//+mept2rRJ1KxZUxgZGYk6deqI3bt3Z9q/Zs2abNv19/fPNZ4VK1YId3d3YWhoKJydncWnn36aY9n8TANTuXLlPOPo37+/aNWqVaZ6gYGBwtvbWxgZGYmqVatm+v8QQoi4uDjx2WefiUqVKgkTExNRtWpVMXnyZJGampprPFu3btW0a2dnJ955551cy5PHNDCtWrXKNr8XXy9/f39RuXLlTPVUKpVwdXUVX331VbbtjhkzRlSqVEkYGRkJR0dH0aVLF3H+/PlcYy1ofnIamFfXy/nuWnpRLBkeIHYtvVjktpMuXRLXatcR19w9ROxL3yvFQaVWicXnF4u6a+uKumvrilEBo8STxCcl/vrGPkwSKz4NFEuGB4irxyJzLJf+9Km42aatuObuIe6NGZNpWrCzUWdFq42tRN21dUWTdU1E4N3AbNsoi+9nOQ2MEAohinmpgjIkLi4Oa2trYmNjsbLKfJ9EUlISISEh1KpVC7NcFtaWtCc8PJwqVaoU++obxUmhUBAWFoabm1tJh6J1zz8z4eHhhIaG4u7uzltvvVXSYRWKUqlkz549dOnSBUNDw5IOR+dezvdJVCIbZ55BqAVvjfamYu38T0OUnYc//sijZcvRs7SkyvZtGOVxX7Iu7A7dzdfHvyZNnUZFi4p0pztD3hpSIq+vEIJdSy5y9+oTXGrY4DfOJ9t7vYVaTcTIUSQEBmJYqRJVtm1F38IClVrFmqtrWHJhCSqhorpNdb5r/R1u1m7ZHq8svp9z+/1+XchLwJIkSZJWlXM2p17rCgAc3XwTtSrne2Hzw+7jjzH19kYdH0/k558jlEpthFkgXat2Ze2ba3E2d+Zewj1+SviJjdc3lsgfnDeDYrh79Ql6Bgpa93HPcaDfo+XLSQgMRGFoSIXvFqFvYcG9+HsM3DeQH87/gEqo6Fq1K+u6rMux8yeVXbIDKEmSJGldo65VMDE35GlUIleO3C9SWwpDQ1wWLEDPyoqUi5d4+OOPWoqyYOrZ12Nz9820dm2NChXzz83nk4BPiEqIKrYYUhKVHNuUsVxdw85u2DqZZ1su/uBBHi1eAoDTNH+Ma9di843NvLfzPS48uIC5oTkzms5gTvM5crDHa0p2AKUyw8bGBn9//5IOQ6f8/f2xsbEp6TAkKU8m5oY0ebsqAGf+zpinriiMXCvg/L85Jx+v+pmEY9pdFSi/rI2tWdhiIV1Mu2CoZ8ixyGP4/eXHn//9iVoU7UxnfpzYeovkeCW2zubU71Q52zKpoaHc/3I8ALYffkh0mzr0/acvM07OICk9ifoO9dnSfQs9avQo1DRhUtkgO4BSmWFjY8O0adNKOgydmjZtmuwASq+M2s1dKF/BgtSkdM7sKtq0MABWnTpi07sXAPcnTED50vycxUWhUNDUuCkbO2/E296bpPQkvjn9DX339CX4QbDOjhtx/SkhJzLONrb5yAN9g6w/4ar4eCJGjkKdmIihjxdr2+nxwa4PuPTwEmYGZoxvNJ7VnVbjaln891FKpYvsAEqSJEk6oaenoHnPjOlJrhyJ5HFkQh418uY4YQLG7u6oHj8mcvRnqNPSitxmYVWxrsKvnX9lYuOJmBqYcunRJfr+05fPD31O6LNQrR4rLSWdwN9DAKjTsgLO1ayzlBEqFffHTyAtLIyUcuaMaH2Ldbc2ohZqOlbuyE6/nfSt3Rd9PX2txia9mmQHUJIkSdIZV3dbqvnYI9SCQ+uuI9RFGzShZ2KC6+If0bOyIvnixWKfHzBLPAo9+tTqw+4eu3m3xrsoULD/zn78/vJjbOBYrj66qpXjnNp+m7hHKVjYGtO0R/bL7IV+409CYCBKfZjePYUYk1TqlK/Dyg4rWdh6IY7mua/wI71eZAdQkiRJ0qnmPWtgaKxPdGgs144XbUAIgFGlSlRYuAAUCp5t2sTTPzdpIcqisTezZ1rTaWzuvpl2ldohEPx791967e7FB7s+YNP1TcSnxReq7YjrT7l8OGO5t7b9amFk+v+TNKeqUtkfvp+VU98mbcNWAJZ204Na1fm+9fds6LoBX5fCr8oklV1yqm9JkiRJpyxsTWjyVlWObb7Jye23qeJlj5mVUdHabNEC+zFjePjdd0R/8w3G1ath1qCBliIuPPdy7nzf5ntuPb3FL1d+YV/4Pq49vsa1x9eYc2YOjRwb0aZSGxo7NaaKdRX0FLmfh0lLSefgb/+79NvCBVcPW6ITozl5/yTH7x/nROQJal6L5cstGQNQjnavTJ9Pv6J5heZ5ti293mQHUJIkSdK5eq0r8N+pKB7dS+DE1lu0H1g770p5KD9sKCnXrhG/bx8Rn4yk8sYNGOdj/fLiUN22OnNazGF8o/HsvL2THbd2cOvZLU5GneRk1EkALI0sqVu+LpWtKlPJqhL2ZvZYGFpgbmiOSq0iTZVG6K4k4h/robZIZXO5pUzbdJnHKY81x6l2XzDmL4EeoPd2J4bO/U6O7JXyRXYApTKlLK+UAWU/P6ns0tPXo/WHHmyZH8T109F4+Drh6lG0FUIUCgUuc+dwJyqKlEuXuDd8BG4bN2BQrmjtapOtiS396/Snf53+hMeGE3gvkKORR7n88DLxafGZOoQvc33mTreQTwDYVWkV9x9kzP+nr9CndvnadBC1aLJsJwplAuYtWlBx1gLZ+ZPyTZ4fljKZM2cOjRo1wtLSEgcHB/z8/Lh+/XqudZRKJTNmzKBatWqYmJjg5eXF3r17s5SLjIzko48+onz58piamlKvXj2CgoJ0lUq2jh07RrNmzTQxeHh48N133+VZ79KlS7Ro0QITExMqVqzI/PnzM+3ftm0bDRs2xMbGBnNzc7y9vfn99991lUaOCpPftGnTUCgUWR7m5pknmN28eTMeHh6YmJhQr1499uzZo8tUpDLIsYoV9VpmrBByaP11lGmqIrepZ2pKxWVLMXR1RXn3LhGfjESdklLkdnXBzdqNgXUHsrrTak58eIKN3TYyvel0htQbQofKHWjo2JBa5WpR0bIiNUw86BDaD4DYGuG09/VlcpPJ/NHlD05+eJK1XvNpPv9fFLEJmNSrR4XvvkNhIM/pSPkn3y1SJocPH2bkyJE0atSI9PR0vvrqKzp27Mi1a9eydAiemzJlCn/88QerVq3Cw8ODffv20aNHD06cOIGPjw8AT58+pVmzZrRp04Z//vkHe3t7bt68ia2tbXGmh7m5OaNGjcLT0xNzc3OOHTvG8OHDMTc3Z9iwYdnWiYuLo2PHjrRv354VK1Zw+fJlBg0ahI2NjaZOuXLlmDx5Mh4eHhgZGbFr1y4GDhyIg4MDnTp1KtX5ffHFF4wYMSLTtnbt2tGoUSPN8xMnTtC7d2/mzJlDt27dWL9+PX5+fpw/f566devqNCepbGniV43Qi4+IfZDM6b9Caf5+jSK3aWBnR8WVPxHeqzfJwcFEfjYG18U/ojAq2n2GumSoZ0id8nWoU75Oln1CCPb+dIXQ1IfYOJoxbFR/DI3/f+oWZUwMdwYMJP3BA4xr1KDiyp/Qt8j++1mSciSkQouNjRWAiI2NzbIvMTFRBAUFicTExBKITHsePHggAHH48OEcyzg7O4slS5Zk2vbOO++IPn36aJ5PmDBBNG/evMDH37lzp2jYsKEwNjYW5cuXF35+frmWB0RYWFiBjtGjRw/x0Ucf5bh/2bJlwtbWVqSmpmq2TZgwQbi7u+faro+Pj5gyZUquZUpDfi8LDg4WgDhy5IhmW8+ePUXXrl0zlWvSpIkYPnx4rm0VJL/nn5ktW7aI+fPni7/++ivfMZc2aWlpYseOHSItLa2kQykWBc03/PIjsWR4gFgyIkBE3nyqtTgSz5wRIZ5e4pq7h7g3+jOhViq11vaLdP36XjkSIZYMDxDLPjkoHtyJy3zs6Ghx683O4pq7h7jZoaNIi4nRSQyZjlkG38+5/X6/LuQl4GIkhECdlFTsD1GExcpjY2OBjDNcOUlNTcXExCTTNlNTU44dO6Z5vnPnTho2bMj777+Pg4MDPj4+rFq1Ktdj7969mx49etClSxcuXLhAQEAAjRs3LnQu2blw4QInTpygVatWOZY5efIkLVu2xOiFswmdOnXi+vXrPH36NEt5IQQBAQFcv36dli1b5thuacnvZT///DM1a9akRYsWmm0nT56kffv2mcp16tSJkyezv3cJiic/6dVUuW55PJo6g4CDv4Zo5VIwgFmjRrguWYLC0JD4ffuImjwZodb98mza9DQ6kWObM+71a/J2VewrWWr2KSMjufNRX9LCwjBwdqbS6tUYOjiUVKjSK05eAi5GIjmZ6/WLf5oC9/PnUJgVfLFvtVrNmDFjaNasWa6X+Tp16sSiRYto2bIl1apVIyAggG3btqFS/f+XemhoKMuXL2fcuHF89dVXnD17ltGjR2NkZET//v2zbXfWrFn06tWL6dOna7Z5eXkVOI/suLq68vDhQ9LT05k2bRpDhgzJsWx0dDRVXhpZ6OjoqNn3/DJ2bGwsFSpUIDU1FX19fZYtW0aHDh1ybLe05PeilJQU1q1bx8SJEzNtj46O1uT8nKOjI9HR0Tm2pcv8pFdf8/eqExHyhNiHyZzacZsWPWtqpV2LFs2p8P13RIz+jNi/doKePs4zZ7wS98ep0tUcWH2N9DQ1rh62+LSvpNmXdvcudwYMIP1+FIYVK1JpzRqMXCuUYLTSq06eAZRyNHLkSK5cucLGjRtzLffDDz9Qo0YNzf1vo0aNYuDAgejp/f/bS61WU79+fWbPno2Pjw/Dhg1j6NChrFixIsd2g4ODadeundbyedHRo0cJCgpixYoVfP/992zYsKHIbVpaWhIcHMzZs2eZNWsW48aN49ChQzmWL435bd++nfj4+Bw75QWhy/ykV5+xmSFtPvIA4NLBCO5ee5xHjfyzbNcOl/nzQE+P2O3biRw7rkSXjMuv41tu8fBuPCbmhrTrXxuFXsaI3pRr17jT5yPS70dh5OZG5d9/k50/qchK/59EZYjC1BT38+dK5LgFNWrUKHbt2sWRI0dwdc190XB7e3t27NhBSkoKjx8/xsXFhYkTJ1K1alVNGWdnZ2rXzjzvV61atdi6dWuO7ZoWIu78en5Gr169esTExDBt2jR69+6dbVknJydiXlp0/vlzJycnzTY9PT2qV68OgLe3NyEhIcyZM4fWrVtn225pye9FP//8M926dctyti+n/4MX83+ZLvOTyoZKdcpTp2UFrh6J5N+1IfSa0rjIE0Q/Z921KwojI+6P+5z4AweIGDEC18WL0cthMFtJu3E2msuHIgBoN6AWFrbGACQcPkzE2HGIpCSMa9Sg0upfMLC3L8lQpTJCngEsRgqFAj0zs2J/FGReKCEEo0aNYvv27Rw8eDDLpc/cmJiYUKFCBdLT09m6dStvv/22Zl+zZs2yTCdz48YNKleunGN7np6eBAQE5Pv4haVWq0lNTc1xv6+vL0eOHEGpVGq2HThwAHd391xHMefVbmnJ77mwsDACAwMZPHhwln2+vr5ZYj1w4AC+vjkvMVVc+UmvtubvVaeciznJcWkE/HqtyGsFv8iqQwcqrvwJhZkZiSdOcqdff5Qv/SFTGjy5n0jg7/8B0KBzZdzq2QHwdOOf3PtkJCIpCfOmvlRev052/iStkR1AKZORI0fyxx9/sH79eiwtLYmOjiY6Oprk5GRNmX79+jFp0iTN89OnT7Nt2zZCQ0M5evQob775Jmq1mvHjx2vKjB07llOnTjF79mxu3brF+vXrWblyJSNHjswxFn9/fzZs2IC/vz8hISFcvnyZefPmFSm/pUuX8vfff3Pz5k1u3rzJL7/8woIFC/joo480ZZYsWZLp0uWHH36IkZERgwcP5urVq/z555/88MMPjBs3TlNmzpw5HDhwgNDQUEJCQli4cCG///57pnZLa37PrV69GmdnZzp37pxl32effcbevXtZuHAh//33H9OmTSMoKIhRo0YVa35S2WNgpE/HwXXQN9Tj7tUnXDx4T6vtm/v6UnnNavRtbEi5epXw994n+dIlrR6jKNJS0tm78rLmvr/G3auiTk0laurXRE+bBioV1j16UHHFCvQtLfNsT5LyrWQHIb/ayuI0MEC2jzVr1mjKtGrVSvTv31/z/NChQ6JWrVqaqT769u0rIiMjs7T9999/i7p16wpjY2Ph4eEhVq5cmWc8W7duFd7e3sLIyEjY2dmJd955J8/4c5sm5ccffxR16tQRZmZmwsrKSvj4+Ihly5YJlUqlKePv7y8qV66cqd7FixdF8+bNhbGxsahQoYKYO3dupv2TJ08W1atXFyYmJsLW1lb4+vqKjRs3vjL5qVQq4erqKr766qsc2960aZOoWbOmMDIyEnXq1BG7d+/Wan5yGphXlzbyvXzonmbqk+hQ7U/NkXr3rrjdrZu45u4hQup5iqebNwu1Wl2otrT1+qrVavHPT5fFkuEBYs2EYyIxNlWk3osQoe+8K665e4hrHrXEw+XLCx2ntpTF97OcBkYIhRBFmCPkNRcXF4e1tTWxsbFYWVll2peUlERISAi1atXCrBAjcKXCKetLpZXl/J5/ZsLDwwkNDcXd3Z233nqrpMMqFKVSyZ49e+jSpQuGhoYlHY7OaSNfIQR7V14h9MJDzG2M6flVI63dD/icKiGB+1+OJyEwEADLzm/iPH06+i99f+dFW6/vmV1hnN0Vhp6egrfH+WB2KZCY2bNRJySgb22Ny4IFWLRoXuj2taUsvp9z+/1+XchLwJIkSVKJUygUtOtXC1snMxKfpbJ35WVUKu3O4advYYHr0iU4fPE5GBgQ/89eQv38SDhyRKvHyY+bQTGc3RUGQPNuzqgWTSbqq69QJyRg6uVFlW1bS0XnTyq7ZAdQkiRJKhWMTA3oPKIehib6RN2K5fiWW1o/hkJPj/JDhuC2fh2GFSuSfj+Ke8OGEzF2LMoHD7R+vOzEhMUR8GsIAO6OzzCa0oeEgwfB0BD7ceOovO4PDCvIaV4k3ZIdQKlM8ff3x8bGpqTD0Jmynp8k2TqZ02FgxpRRlwMjuHbsvk6OY+rpSdUd2yk3cCDo6xP/z15ud3qTB4u+Q/W/FZB0IfZhMruXXUSlVGOfdAuXP6egTkrCxMuTKls2Yzds6CsxabX06pMdQKlMmTZtWpnuIJX1/CQJoIqXPY27Z0xBdWj9de5c0d4k0S/SMzfHccJ4qmzZjKmXFyI5mccrV3KrfQceLFxIWkSkVo8Xf/8JO2YdIzleiUVCBLXOLcPArjzOc+bgtmEDJu7uWj2eJOVGdgAlSZKkUqdhFzfc33BCqAV7V13h4d14nR3LpFYtKm/cgOuypRjXrIk6Pp7Hq37mdocO3B0+nNi//ir0WUF1aioJR48SPtGfbRP2kJCij0nyQ+rf+QPnz0ZSbe9ebHr4odCTP8dS8ZLnmSVJkqRSR6FQ0OYjDxKfpRLx31P+XnKRdz6vj42jbmZVUCgUWLZti0Xr1iQcPMjT9etJPHGSxMNHSDx8BAwMMKlTGzOf+hjWrIlJRATpMTHo29qiMDFBpKaiTk5GGRWNMjKSlP9CSLl0maTz50lPVRHsOZIEGxeMVQm0902j4vId6MkZIqQSJDuAkiRJUqmkb6DHm8PrsX3heR5HJLDjuwv0+Lw+1va6W2ZQoaeHZfv2WLZvT2poGHG7/ib+wAFSb94i5eIlUi5mTCJdCQhftizP9tL1jbnUcAyxZpUxMlLw9pdtsK8oJ3SWSp7sAEqSJEmllrGpAW+N9mbHdxd4GpXIX99dwO9zH6zK636taeOqVbAfPRr70aNJi4gk+fw5ks6fJ/V2KHHXr2OYmAgqVaY6+vZ2GLq4YFy1Gvq1PTkSWoFnUUqMTA3o/qmX7PxJpUaZvelApVIxdepUqlSpgqmpKdWqVWPmzJm8OO+1EIKvv/4aZ2dnTE1Nad++PTdv3izBqCVJkqSXmVkZ8fYYb6wdTIl/ksL2hed5Gp1YrDEYuVbA+q23cJ42jQqrfyFs8ldUu3Ae9wvnqXHyBO7ngvC4eoWaR49S5c8/sZk0jcP3qvEgSomxmQFvj/HGqap1scYsSbkpsx3AefPmsXz5cpYsWUJISAjz5s1j/vz5LF68WFNm/vz5/Pjjj6xYsYLTp09jbm5Op06dSElJKcHIJUmSpJeZWxvjN9YHawdTEp6ksm3BeR7ciSvRmBQKBXqmphjY2qJnbo5CXx+Ap9GJbJkXxMO78ZiYG/L2GB8cKr+eq01IpVeZ7QCeOHGCt99+m65du+Lm5sZ7771Hx44dOXPmDJBx9u/7779nypQpvP3223h6evLbb79x//59duzYUbLBS4WmUCgIDw8v6TB0pnXr1qxdu7akw5CkEmFha8I7XzTAvpIlKQlKti+6QPjlRyUdViaRN56ydf454h+nYGVvyrvjM+KVpNKmzHYAmzZtSkBAADdu3ADg4sWLHDt2jM6dOwMQFhZGdHQ07du319SxtramSZMmnDx5skRiLg2WL1+Op6cnVlZWWFlZ4evryz///JNrHaVSyYwZM6hWrRomJiZ4eXmxd+/eTGWOHDlC9+7dcXFxQaFQlFgn+9ixYzRr1ozy5ctjamqKh4cH3333XZ71Nm3ahLe3N2ZmZlSuXJlvv/02S5l169bh5eWFmZkZzs7ODBo0iMePdTN/WX48fvwYV1dXFAoFz549y7P87t27adKkCaamptja2uLn55dpv0KhyPLYuHGjboKXpByYWRnhN84HVw9b0lNV7F52ibO7wxDqkl3WXgjB+X13+Ov7YFKT0nGsYsW7XzbQ2ahlSSqqMjsIZOLEicTFxeHh4YG+vj4qlYpZs2bRp08fAKKjowFwdHTMVM/R0VGz72WpqamkpqZqnsfFZVx+UCqVKJXKTGVffv6qcHV1Ze7cudSoUQMhBL/++itvv/02Fy5coE6dOtnWmTJlCn/88QerVq3Cw8ODffv20aNHD06cOIGPjw8AiYmJeHl5MWjQIN55553iTCkTc3NzRo0ahaenJ+bm5hw7dozhw4djbm7OsGHDsq3zzz//0KdPHxYvXkzHjh0JCQlh6NChmJqaMmrUKACOHz9Ov379+O677+jevTuRkZGMGDGCoUOHsm3btuJMUWPw4MF4enoSGZn3ZLZbt25l6NChzJ49m7Zt25Kens6VK1eylFuzZg1vvvmm5rkuJqUWQiCEQK1Wv7Kfo+dxv6rxF1Rx56vQh07Da3NyayjXjkVx5u8wYsJjafVhTUwsDHV+/JfzTUlUcnjdTe5czviDr3ojB1r2qo6BkaJMvAfK4vu5LOVSWGW2A7hp0ybWrVvH+vXrqVOnDsHBwYwZMwYXFxf69+9fqDbnzJnD9OnTs2zfv38/Zi/N52RoaJilcymEID1Nu4ub54eBkR4KhSJfZbt3757p+axZs1i+fDmnTp3KsQP4+++/M3nyZLp06QLAxx9/zL///svChQv5448/AOjcubPm7GtB/P3338yYMYPLly9jYWFBixYt2L59e4Hbec7Hx0fTKQVwc3Nj27ZtHD16NMcO4O+//46fnx8jRowAoGrVqkyaNIl58+YxcuRIFAoFJ0+exM3NjdGjRwNQpUoVhg8fzrx583KN5+rVq0yYMIEjR44ghMDb25u1a9dSrVq1QucIGWdynz17xtdff53nGdz09HQ+++wzvv32WwYPHqzZXrt27SxlbWxscHJyynccERERfPnll+zbt4/U1FRq1arF0qVLadKkSY517t69y/3791Eqlfl+35ZWBw4cKOkQilWx52sJtnUNeHrNhDuXn/CH/wls66Zi6pheLIc/cOAASVEGPLtmjDpNDxQCm9qpJJe/zf5/bxdLDMWpLL2fk5KSSjqEEldmO4BffvklEydOpFevXgDUq1ePO3fuMGfOHPr376/5EYuJicHZ2VlTLyYmBm9v72zbnDRpEuPGjdM8j4uLo2LFinTs2BErq8w3+CYlJXHrVuaFzNPT1Kz87LA20iuQYT+0wtBYv8D1VCoVmzdvJjExEV9f3xzLpaamYmJikmmbqakpx44dK/AxX7R792569OjB5MmT+e2330hLS2PPnj1FavNlFy5c4MSJE3zzzTc5lklNTc3SwTc1NSUiIoI7d+7g5uaGr68vX331FXv27KFz5848ePCALVu2aDrF2YmMjKRly5a0bt2agwcPYmVlxfHjx0lPL9qP17Vr15gxYwanT58mNDQ0z/Lnz58nMjISPT09fHx8iI6Oxtvbm2+//Za6detmKjty5EiGDBlC1apVGTFiBAMHDsyxk5aQkECrVq2oUKECO3fuxMnJifPnz6NW5/5HUKVKlUhPT6dmzZq5/v+VZkqlkgMHDtChQwcMDXV/RqqklXS+j+4lEPjbdZ5GJ/H4vCnVGtjT5O0qWNga6+R4SqWSf/76F+MnlXhy+QkAts5mtP6oZpm836+kX19deH4F73VWZjuASUlJ6L20tI6+vr7mx6dKlSo4OTkREBCg6fDFxcVx+vRpPv7442zbNDY2xtg46xeKoaFhlg/Fq/whuXz5Mr6+vqSkpGBhYcH27duzPRv0XKdOnVi0aBEtW7akWrVqBAQEsG3bNlQvzY9VULNmzaJXr16Zzrp6eXkVqc3nXF1defjwIenp6UybNo0hQ4bkWLZTp06MHTuWAQMG0KZNG27dusXChQsBiIqKws3NjWbNmrFu3To++OADUlJSSE9Pp3v37ixdujTHdpcuXYq1tTUbN27UvF9q1qxZpLxSU1Pp3bs33377LZUqVcpXB/B5mWnTprFo0SLc3NxYuHAhrVu35saNG5QrVw6AGTNm0LZtW8zMzNi/fz+ffPIJCQkJmrOeL1u/fj0PHz7k7NmzmjaqV6+eZzzP7y/U09N7pT9HkP13Q1lWUvk6V7Wl5+RGnN0VxoX9d7l97iF3Lj3Gq31FvNtV0upl4cRnqZzbd4/oI+YgnqDQU9Dgzco07OyGvmGZva0eKFvv57KSR1GU2Q5g9+7dmTVrFpUqVaJOnTpcuHCBRYsWMWjQICDjR2bMmDF888031KhRgypVqjB16lRcXFyy3PyuLQZGegz7oZVO2s7ruAXh7u5OcHAwsbGxbNmyhf79+3P48OEcO4E//PADQ4cOxcPDA4VCQbVq1Rg4cCCrV68uUtzBwcEMHTq0SG3k5OjRoyQkJHDq1CkmTpxI9erV6d27d7Zlhw4dyu3bt+nWrRtKpRIrKys+++wzpk2bpvkj49q1a3z22Wd8/fXXdOrUiaioKL788ktGjBjBL7/8kmN+LVq00OoX0aRJk6hVqxYfffRRvus8/6No8uTJvPvuu0DGvX6urq5s3ryZ4cOHAzB16lRNHR8fHxITE/n2229z7AAGBwfj4+Oj6fxJki4ZGOrj26M61eo7cGzzTaJuxXLunztcDLhHrWYu1GnuQvkKFoVqWwhBTFgcV4/d58bpaNQqAShw9bCh+fs1C92uJJWkMtsBXLx4MVOnTuWTTz7hwYMHuLi4MHz4cL7++mtNmfHjx5OYmMiwYcN49uwZzZs3Z+/evVkuZ2qLQqEo1KXY4mZkZKQ5U9OgQQPOnj3LDz/8wE8//ZRteXt7e3bs2EFKSgqPHz/GxcWFiRMnUrVq1SLFYWqqu5n+q1SpAmTcGhATE8O0adNy7AAqFArmzZvH7NmziY6Oxt7enoCAAABNjnPmzKFZs2Z8+eWXAJpBJi1atOCbb77JdJvBc7rI7+DBg1y+fJktW7YAaCY+t7OzY/Lkydnew/o8thc7+MbGxlStWpW7d+/meKwmTZowc+ZMUlNTsz0zrsvXT5Jy4lDZih6f1yc0+CFBe8J5dC+By4ERXA6MoLyrBVU87XB1t8WhihWGRjl/H6cmpxN9O5aI/54QevERcQ+TNfucqlqRXi6aLv21+wecJBWnMtsBtLS05Pvvv+f777/PsYxCoWDGjBnMmDGj+AJ7BanV6kyjn3NiYmJChQoVUCqVbN26lZ49exbpuJ6engQEBDBw4MAitZOX/Oanr69PhQoVANiwYQO+vr7Y29sDGbccGBgYZCkPZFp95kWenp78+uuvKJVKrf2IbN26leTk//+hOnv2LIMGDeLo0aM5Dixp0KABxsbGXL9+nebNmwMZ9/yEh4dTuXLlHI8VHByMra1ttp0/yMjv559/5smTJ/IsoFSsFAoF1XwcqOptT0TIUy4fjuDOlcc8jkjgcUQCQXvCAbAsZ4KVnQmGJgYYGOqhTFORlpxO7MNkkmLTMrVpYKRHVW976rV2pXxFM/bsyXt0vSSVZmW2AygVzqRJk+jcuTOVKlUiPj6e9evXc+jQIfbt26cp069fPypUqMCcOXMAOH36NJGRkXh7exMZGcm0adNQq9WMHz9eUychISHToJiwsDCCg4MpV64clSpVyjYWf39/2rVrR7Vq1ejVqxfp6ens2bOHCRMmFDq/pUuXUqlSJTw8PICM+QkXLFiQ6TLmkiVL2L59u+Ys36NHj9iyZQutW7cmJSWFNWvWsHnzZg4f/v8BPd27d2fo0KEsX75ccwl4zJgxNG7cGBcXl2xjGTVqFIsXL6ZXr15MmjQJa2trTp06RePGjXF3dy9Ufi938h49ypgkt1atWpopW86cOUO/fv0ICAigQoUKWFlZMWLECPz9/alYsWKmeQ7ff/99IGM0dkxMDG+88QYmJiYcOHCA2bNn88UXX+QYS+/evZk9ezZ+fn7MmTMHZ2dnLly4gIuLS66DiiRJWxQKBRVrl6Ni7XKkJCoJDX5IxH9Pibz+lKS4NOKfpBD/JOeVn6zsTKjgbktFj3JUrlceI5OMn0w5hYhUFsgOoJTJgwcP6NevH1FRUVhbW+Pp6cm+ffvo0KGDpszdu3czDbBJSUlhypQphIaGYmFhQZcuXfj9998zzREXFBREmzZtNM+fj6bu379/jitbtG7dms2bNzNz5kzmzp2LlZUVLVu2LFJ+arWaSZMmERYWhoGBAdWqVWPevHma+9wgo9N0+3bmKRx+/fVXvvjiC4QQ+Pr6cujQIRo3bqzZP2DAAOLj41myZAmff/45NjY2tG3bNtdpYMqXL8/Bgwf58ssvadWqFfr6+nh7e9OsWbMi5ZiXpKQkrl+/nulH7Ntvv8XAwIC+ffuSnJxMkyZNOHjwILa2tkDGDdNLly5l7NixCCGoXr06ixYtyvUeTSMjI/bv38/nn39Oly5dSE9Pp3bt2rkOjJEkXTExN6R2MxdqN3NBCEFKgpKnMUkkPE1BmaIiPU2NoYk+RiYGWJYzwcbJDGNT+RMplV0KkdP1KSlPcXFxWFtbExsbm+00MCEhIdSqVSvLFCKS7igUCsLCwnBzcyvpUHSidevWDBgwgAEDBpR0KFr3/DMTHh5OaGgo7u7uvPXWWyUdVqEolUr27NlDly5dXot7xGS+ZVtZzDe33+/XRdkesy5JkiRJkiRlITuAkiRJkiRJrxnZAZTKFH9/f52sT1taDBgwIMeVaiRJkiQpv+QdrlKZMm3atJIOQafK4r1/kiRJUvGTZwB1LK91TyVJyiDHo0mSJBUf2QHUESMjIyBj/jtJkvL2fDLu9PT0Eo5EkiSp7JOXgHXEwMAAOzs7IiMzZou3sLDINHeeJEn/T61Wc+/ePZKSklCpVCUdjiRJUpknO4A69HyFi+edQEmScqZWq4mOjtb8O6cl5iRJkqSikx1AHVIoFFSuXJmrV69y69YtnJycNOvD5kUIwd27d6lUqRIKhULHkZY8mW/Zlle+QgiUSmXGCg0pKahUqjI9mluSJKmkyQ5gMWjevDnPnj3j1q1b+b4MLIQgKiqK1NTU16aDIPMtuwqSrxACT09POd2NJEmSDskOYDGwsrKiW7duXL9+nbi4uHyNdlSpVFy6dAlPT898nzV8lcl8y7b85quvr4+VlRV169aVl4AlSZJ0SHYAi4mVlRWNGjXKd3mlUklKSgpt27YtM2sv5kbmW7a9bvlKkiSVdnJYqiRJkiRJ0mtGngEsgueXcuPi4rTetlKpJCkpibi4uNfijInMt2yT+ZZtMt+yrSzm+/x3+3WegF52AIsgPj4egIoVK5ZwJJIkSZIkFVR8fDzW1tYlHUaJUIjXuftbRGq1mvv372Npaan1kZxxcXFUrFiRe/fuYWVlpdW2SyOZb9km8y3bZL5lW1nMVwhBfHw8Li4ur+0iDfIMYBHo6enh6uqq02NYWVmVmQ9cfsh8yzaZb9km8y3bylq+r+uZv+dez26vJEmSJEnSa0x2ACVJkiRJkl4zsgNYShkbG+Pv7//aTIYr8y3bZL5lm8y3bHvd8n1dyEEgkiRJkiRJrxl5BlCSJEmSJOk1IzuAkiRJkiRJrxnZAZQkSZIkSXrNyA6gJEmSJEnSa0Z2AEvI0qVLcXNzw8TEhCZNmnDmzJlcy2/evBkPDw9MTEyoV68ee/bsKaZItacgOa9atYoWLVpga2uLra0t7du3z/P/qLQp6Gv83MaNG1EoFPj5+ek2QC0raL7Pnj1j5MiRODs7Y2xsTM2aNV+p93VB8/3+++9xd3fH1NSUihUrMnbsWFJSUoop2qI5cuQI3bt3x8XFBYVCwY4dO/Ksc+jQIerXr4+xsTHVq1dn7dq1Oo9TWwqa77Zt2+jQoQP29vZYWVnh6+vLvn37iidYLSjM6/vc8ePHMTAwwNvbW2fxSbohO4Al4M8//2TcuHH4+/tz/vx5vLy86NSpEw8ePMi2/IkTJ+jduzeDBw/mwoUL+Pn54efnx5UrV4o58sIraM6HDh2id+/eBAYGcvLkSSpWrEjHjh2JjIws5sgLp6D5PhceHs4XX3xBixYtiilS7ShovmlpaXTo0IHw8HC2bNnC9evXWbVqFRUqVCjmyAunoPmuX7+eiRMn4u/vT0hICL/88gt//vknX331VTFHXjiJiYl4eXmxdOnSfJUPCwuja9eutGnThuDgYMaMGcOQIUNemU5RQfM9cuQIHTp0YM+ePZw7d442bdrQvXt3Lly4oONItaOg+T737Nkz+vXrR7t27XQUmaRTQip2jRs3FiNHjtQ8V6lUwsXFRcyZMyfb8j179hRdu3bNtK1JkyZi+PDhOo1Tmwqa88vS09OFpaWl+PXXX3UVolYVJt/09HTRtGlT8fPPP4v+/fuLt99+uxgi1Y6C5rt8+XJRtWpVkZaWVlwhalVB8x05cqRo27Ztpm3jxo0TzZo102mcugCI7du351pm/Pjxok6dOpm2ffDBB6JTp046jEw38pNvdmrXri2mT5+u/YB0rCD5fvDBB2LKlCnC399feHl56TQuSfvkGcBilpaWxrlz52jfvr1mm56eHu3bt+fkyZPZ1jl58mSm8gCdOnXKsXxpU5icX5aUlIRSqaRcuXK6ClNrCpvvjBkzcHBwYPDgwcURptYUJt+dO3fi6+vLyJEjcXR0pG7dusyePRuVSlVcYRdaYfJt2rQp586d01wmDg0NZc+ePXTp0qVYYi5ur/p3VlGp1Wri4+Nfie+rwlqzZg2hoaH4+/uXdChSIRmUdACvm0ePHqFSqXB0dMy03dHRkf/++y/bOtHR0dmWj46O1lmc2lSYnF82YcIEXFxcsvyolEaFyffYsWP88ssvBAcHF0OE2lWYfENDQzl48CB9+vRhz5493Lp1i08++QSlUlnqf1AKk++HH37Io0ePaN68OUII0tPTGTFixCtzCbigcvrOiouLIzk5GVNT0xKKrHgsWLCAhIQEevbsWdKh6MTNmzeZOHEiR48excBAdiNeVfIMoFTqzZ07l40bN7J9+3ZMTExKOhyti4+Pp2/fvqxatQo7O7uSDqdYqNVqHBwcWLlyJQ0aNOCDDz5g8uTJrFixoqRD04lDhw4xe/Zsli1bxvnz59m2bRu7d+9m5syZJR2apGXr169n+vTpbNq0CQcHh5IOR+tUKhUffvgh06dPp2bNmiUdjlQEsutezOzs7NDX1ycmJibT9piYGJycnLKt4+TkVKDypU1hcn5uwYIFzJ07l3///RdPT09dhqk1Bc339u3bhIeH0717d802tVoNgIGBAdevX6datWq6DboICvP6Ojs7Y2hoiL6+vmZbrVq1iI6OJi0tDSMjI53GXBSFyXfq1Kn07duXIUOGAFCvXj0SExMZNmwYkydPRk+vbP0tntN3lpWVVZk++7dx40aGDBnC5s2bX4mrFYURHx9PUFAQFy5cYNSoUUDG95UQAgMDA/bv30/btm1LOEopP8rWt84rwMjIiAYNGhAQEKDZplarCQgIwNfXN9s6vr6+mcoDHDhwIMfypU1hcgaYP38+M2fOZO/evTRs2LA4QtWKgubr4eHB5cuXCQ4O1jzeeustzQjKihUrFmf4BVaY17dZs2bcunVL09EFuHHjBs7OzqW68weFyzcpKSlLJ+9551eUweXYX/XvrMLYsGEDAwcOZMOGDXTt2rWkw9EZKyurLN9XI0aMwN3dneDgYJo0aVLSIUr5VcKDUF5LGzduFMbGxmLt2rXi2rVrYtiwYcLGxkZER0cLIYTo27evmDhxoqb88ePHhYGBgViwYIEICQkR/v7+wtDQUFy+fLmkUiiwguY8d+5cYWRkJLZs2SKioqI0j/j4+JJKoUAKmu/LXrVRwAXN9+7du8LS0lKMGjVKXL9+XezatUs4ODiIb775pqRSKJCC5uvv7y8sLS3Fhg0bRGhoqNi/f7+oVq2a6NmzZ0mlUCDx8fHiwoUL4sKFCwIQixYtEhcuXBB37twRQggxceJE0bdvX0350NBQYWZmJr788ksREhIili5dKvT19cXevXtLKoUCKWi+69atEwYGBmLp0qWZvq+ePXtWUikUSEHzfZkcBfxqkh3AErJ48WJRqVIlYWRkJBo3bixOnTql2deqVSvRv3//TOU3bdokatasKYyMjESdOnXE7t27iznioitIzpUrVxZAloe/v3/xB15IBX2NX/SqdQCFKHi+J06cEE2aNBHGxsaiatWqYtasWSI9Pb2Yoy68guSrVCrFtGnTRLVq1YSJiYmoWLGi+OSTT8TTp0+LP/BCCAwMzPbz+DzH/v37i1atWmWp4+3tLYyMjETVqlXFmjVrij3uwipovq1atcq1fGlXmNf3RbID+GpSCFEGrz9IkiRJkiRJOZL3AEqSJEmSJL1mZAdQkiRJkiTpNSM7gJIkSZIkSa8Z2QGUJEmSJEl6zcgOoCRJkiRJ0mtGdgAlSZIkSZJeM7IDKEmSJEmS9JqRHUBJkiRJkqTXjOwASpJUpgwYMAA/P78SO37fvn2ZPXt2vsr26tWLhQsX6jgiSZKkrORKIJIkvTIUCkWu+/39/Rk7dixCCGxsbIonqBdcvHiRtm3bcufOHSwsLPIsf+XKFVq2bElYWBjW1tbFEKEkSVIG2QGUJOmVER0drfn3n3/+yddff83169c12ywsLPLV8dKVIUOGYGBgwIoVK/Jdp1GjRgwYMICRI0fqMDJJkqTM5CVgSZJeGU5OTpqHtbU1CoUi0zYLC4ssl4Bbt27Np59+ypgxY7C1tcXR0ZFVq1aRmJjIwIEDsbS0pHr16vzzzz+ZjnXlyhU6d+6MhYUFjo6O9O3bl0ePHuUYm0qlYsuWLXTv3j3T9mXLllGjRg1MTExwdHTkvffey7S/e/fubNy4sej/OZIkSQUgO4CSJJV5v/76K3Z2dpw5c4ZPP/2Ujz/+mPfff5+mTZty/vx5OnbsSN++fUlKSgLg2bNntG3bFh8fH4KCgti7dy8xMTH07Nkzx2NcunSJ2NhYGjZsqNkWFBTE6NGjmTFjBtevX2fv3r20bNkyU73GjRtz5swZUlNTdZO8JElSNmQHUJKkMs/Ly4spU6ZQo0YNJk2ahImJCXZ2dgwdOpQaNWrw9ddf8/jxYy5dugTAkiVL8PHxYfbs2Xh4eODj48Pq1asJDAzkxo0b2R7jzp076Ovr4+DgoNl29+5dzM3N6datG5UrV8bHx4fRo0dnqufi4kJaWlqmy9uSJEm6JjuAkiSVeZ6enpp/6+vrU758eerVq6fZ5ujoCMCDBw+AjMEcgYGBmnsKLSws8PDwAOD27dvZHiM5ORljY+NMA1U6dOhA5cqVqVq1Kn379mXdunWas4zPmZqaAmTZLkmSpEuyAyhJUplnaGiY6blCoci07XmnTa1WA5CQkED37t0JDg7O9Lh582aWS7jP2dnZkZSURFpammabpaUl58+fZ8OGDTg7O/P111/j5eXFs2fPNGWePHkCgL29vVZylSRJyg/ZAZQkSXpJ/fr1uXr1Km5ublSvXj3Tw9zcPNs63t7eAFy7di3TdgMDA9q3b8/8+fO5dOkS4eHhHDx4ULP/ypUruLq6Ymdnp7N8JEmSXiY7gJIkSS8ZOXIkT548oXfv3pw9e5bbt2+zb98+Bg4ciEqlyraOvb099evX59ixY5ptu3bt4scffyQ4OJg7d+7w22+/oVarcXd315Q5evQoHTt21HlOkiRJL5IdQEmSpJe4uLhw/PhxVCoVHTt2pF69eowZMwYbGxv09HL+2hwyZAjr1q3TPLexsWHbtm20bduWWrVqsWLFCjZs2ECdOnUASElJYceOHQwdOlTnOUmSJL1ITgQtSZKkJcnJybi7u/Pnn3/i6+ubZ/nly5ezfft29u/fXwzRSZIk/T95BlCSJElLTE1N+e2333KdMPpFhoaGLF68WMdRSZIkZSXPAEqSJEmSJL1m5BlASZIkSZKk14zsAEqSJEmSJL1mZAdQkiRJkiTpNSM7gJIkSZIkSa8Z2QGUJEmSJEl6zcgOoCRJkiRJ0mtGdgAlSZIkSZJeM7IDKEmSJEmS9JqRHUBJkiRJkqTXzP8BHaWfeDbMGkcAAAAASUVORK5CYII=", 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", 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\n", " " ], @@ -2025,7 +2460,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 23, "metadata": { "colab": {}, "colab_type": "code", @@ -2036,7 +2471,13 @@ "name": "stderr", "output_type": "stream", "text": [ - "rocketpy - INFO - Starting flight simulation of 'Flight'.\n", + "rocketpy - INFO - Starting flight simulation of 'Flight'.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ "rocketpy - INFO - Entering flight phase 'initial_phase' at t=0.000 s.\n", "rocketpy - INFO - Entering flight phase 'free_flight' at t=0.414 s.\n", "rocketpy - INFO - Simulation completed at time: 5.0000 s\n" @@ -2045,18 +2486,18 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "718d89891f554ea3a64daa6405953b92", + "model_id": "08e21bdccf41456da0b96bb590a1b51c", "version_major": 2, "version_minor": 0 }, - "image/png": 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", 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\n", " " ], diff --git a/docs/reference/plots_prints/aero_surfaces/AeroSurface/AeroSurfacePlots.rst b/docs/reference/plots_prints/aero_surfaces/AeroSurface/AeroSurfacePlots.rst index 80c867393..41131d451 100644 --- a/docs/reference/plots_prints/aero_surfaces/AeroSurface/AeroSurfacePlots.rst +++ b/docs/reference/plots_prints/aero_surfaces/AeroSurface/AeroSurfacePlots.rst @@ -1,5 +1,5 @@ AeroSurface Plots Class ----------------------- -.. autoclass:: rocketpy.plots.aero_surface_plots._AeroSurfacePlots +.. autoclass:: rocketpy.plots.aero_surface_plots._GenericSurfacePlots :members: \ No newline at end of file diff --git a/docs/reference/plots_prints/aero_surfaces/AeroSurface/AeroSurfacePrints.rst b/docs/reference/plots_prints/aero_surfaces/AeroSurface/AeroSurfacePrints.rst index 688c81c36..d70e171b8 100644 --- a/docs/reference/plots_prints/aero_surfaces/AeroSurface/AeroSurfacePrints.rst +++ b/docs/reference/plots_prints/aero_surfaces/AeroSurface/AeroSurfacePrints.rst @@ -1,5 +1,5 @@ AeroSurface Prints Class ------------------------ -.. autoclass:: rocketpy.prints.aero_surface_prints._AeroSurfacePrints +.. autoclass:: rocketpy.prints.aero_surface_prints._GenericSurfacePrints :members: \ No newline at end of file diff --git a/docs/user/center_of_pressure_and_stability.rst b/docs/user/center_of_pressure_and_stability.rst index 69962b581..d1efeff35 100644 --- a/docs/user/center_of_pressure_and_stability.rst +++ b/docs/user/center_of_pressure_and_stability.rst @@ -115,8 +115,8 @@ pressure**: - **Center of pressure (CP).** The point at which the net aerodynamic force can be considered to act. At a small angle to the airflow, a sideways ("normal") force is generated, and the CP is its effective point of application. In - RocketPy this quantity is ``Rocket.cp_position``, an alias of - ``Rocket.aerodynamic_center``. + RocketPy this quantity is ``Rocket.cp_position``, a function of Mach number + (see :ref:`cp_ac_np` below for its exact meaning). The relative position of these two points determines stability: @@ -129,7 +129,7 @@ The relative position of these two points determines stability: If the center of pressure is located ahead of the center of mass the rocket is unstable. -.. figure:: ../../static/rocket/stable-unstable.png +.. figure:: ../static/rocket/stable-unstable.png :align: center :width: 80% @@ -137,6 +137,54 @@ The relative position of these two points determines stability: When the CP is aft of the CM the torque is restoring (left); when the CP is forward of the CM the torque grows the disturbance (right). +.. _cp_ac_np: + +Center of pressure, aerodynamic center and neutral point +--------------------------------------------------------- + +Rocketry speaks of the center of pressure, and for most rockets that is all +that is needed. Strictly, three points are involved, and RocketPy names each +one: + +.. list-table:: + :header-rows: 1 + :widths: 22 44 34 + + * - Point + - What it is + - RocketPy + * - **Center of pressure** + - Where the total aerodynamic force acts: the pitch moment divided by the + normal force, :math:`C_m / C_N`. + - ``Rocket.center_of_pressure(alpha, mach)`` + * - **Aerodynamic center** + - Where the *change* of the force acts when the angle of attack changes a + little, starting from zero angle: + :math:`C_{m,\alpha} / C_{N,\alpha}`. + - ``Rocket.aerodynamic_center``, also available as ``Rocket.cp_position`` + (functions of Mach number) + * - **Neutral point** + - The same as the aerodynamic center, but starting from the angle of + attack the rocket is actually flying at. + - ``Rocket.neutral_point(alpha, mach)`` + +Stability depends on how the force *changes* with the angle, so the point that +decides it is the aerodynamic center (or the neutral point, at an angle). In +practice: + +- **Most rockets**, built from nose cones, fins, tails or a + ``LinearGenericSurface``: the three points are the same, so "center of + pressure" is exact. +- **Lift that is not proportional to the angle**, such as a body-lift term: the + points agree at zero angle and separate as the angle grows. The stability + margin uses the neutral point. +- **A force at zero angle**, such as a deflected control surface or a canted + single fin: the center of pressure is meaningless here, so use + ``Rocket.aerodynamic_center``. + +The rest of this page says "center of pressure" where the first case is meant +or the difference does not matter, and uses the precise name where it does. + .. Role of the fins .. ----------------- @@ -238,7 +286,7 @@ rocket's overall length** rather than in calibers: \frac{(\text{CP position}) - (\text{CM position})}{\text{body length}} \times 100 -.. figure:: ../../static/rocket/cal-per-length.png +.. figure:: ../static/rocket/cal-per-length.png :align: center :width: 80% @@ -251,7 +299,11 @@ normalized by the total length instead of the diameter, as illustrated above. RocketPy exposes the overall length as :attr:`rocketpy.Rocket.length`, defined as the axial span from the nose tip to the aft-most point of the rocket, whether that is an aerodynamic surface -or the motor nozzle if it extends further aft. +or the motor nozzle if it extends further aft. Measuring it needs a nose cone +and at least one of a tail, a fin set or a motor. For a rocket without them, +such as one described only by a :class:`rocketpy.GenericSurface`, give the +``length`` argument when creating the ``Rocket``; otherwise the percentage is +left out of the prints and plots. For Calisto (from :ref:`firstsimulation`), with a length of **2.53 m** and a fineness ratio of approximately 20, the two conventions yield: @@ -304,26 +356,29 @@ distance, in the same calibers, but with the center of pressure taken at the rocket's **actual flight condition**, its Mach number and angle of attack, rather than at rest. -Writing :math:`z_\text{cm}(t)` for the center of mass, :math:`2R` for the body -diameter (one caliber), the two margins have the same form and differ only in -the center-of-pressure reference they subtract: +Writing :math:`z_\text{cm}(t)` for the center of mass, with positions measured +along the rocket toward the nose, and :math:`2R` for the body diameter (one +caliber), the two margins have the same form and differ only in the +center-of-pressure reference they subtract: .. math:: - \text{static margin}(t) = c\,\frac{z_\text{cm}(t) - x_\text{AC}(0)}{2R}, - \qquad - \text{stability margin}(\alpha, M, t) - = c\,\frac{z_\text{cm}(t) - x_\text{NP}(\alpha, M)}{2R}. - -- The static margin uses the **aerodynamic center** :math:`x_\text{AC}(0)`: the - center of pressure linearized about zero angle of attack and evaluated at zero - airspeed (:math:`M = 0`). It is a fixed reference, so the static margin varies - only through the center of mass, that is, with time. -- The stability margin uses the **neutral point** :math:`x_\text{NP}(\alpha, M)`, - the local center of pressure at the actual Mach number and angle of attack. + \text{static margin}(t) &= \frac{z_\text{cm}(t) - z_\text{AC}(M = 0)}{2R}, \\ + \text{stability margin}(\alpha, M, t) &= \frac{z_\text{cm}(t) - z_\text{NP}(\alpha, M)}{2R}. + +- The static margin uses the **aerodynamic center** at zero airspeed, + :math:`z_\text{AC}(M = 0)`: the center of pressure of the rocket at a small + angle of attack. It is a fixed reference, so the static margin varies only + through the center of mass, that is, with time. +- The stability margin uses the **neutral point** :math:`z_\text{NP}(\alpha, M)`: + the aerodynamic center taken at the actual Mach number and angle of attack. Because that reference moves with the flow, the stability margin depends on angle of attack and Mach number as well as time. +For a rocket built from the pre-set surfaces both are simply the center of +pressure (:ref:`cp_ac_np`). Both margins are positive when the center of +pressure is behind the center of mass. + The static margin and the stability margin are the same underlying quantity, evaluated under different conditions: @@ -346,7 +401,8 @@ conditions: - Stability at any chosen flow state * - RocketPy - ``Rocket.static_margin`` (function of ``t``) - - ``Rocket.stability_margin`` (function of ``alpha, M, t``) + - ``Rocket.stability_margin`` (function of ``M, t``, at zero angle of + attack); at another angle, from ``Rocket.neutral_point(alpha, mach)`` The margin varies for up to three independent reasons: @@ -369,8 +425,9 @@ coefficient, for example a Galejs body-lift term. The flight stability margin ---------------------------- -``Flight.stability_margin`` samples the rocket's ``stability_margin`` map at -the angle of attack, Mach number and time realized during the simulated flight: +``Flight.stability_margin`` computes the stability margin at the angle of +attack, Mach number and time of each instant of the simulated flight. +The angle only matters for a rocket with a surface that is nonlinear in it. .. jupyter-execute:: @@ -418,7 +475,7 @@ described by **dynamic stability**. (negative) lowers it. The lateral moment of inertia is adjustable separately, which changes the dynamic response without changing the static margin. The dry mass (in kilograms) and the motor can also be - swapped, for the mass and thrust studies later in this document. + swapped. Everything else, including the aerodynamics, is fixed. """ rocket = Rocket( @@ -463,14 +520,34 @@ spring-mass oscillator: with three governing parameters: - :math:`C_1`, the **corrective (restoring) moment coefficient**, analogous - to a spring constant. It is proportional to the static margin: - :math:`C_1 = \bar q\, A\, C_{N,\alpha}\, (z_\text{cm} - x_\text{cp})`, where - :math:`\bar q` is the dynamic pressure. A larger margin or higher airspeed - produces a stiffer restoring moment. + to a spring constant. It is proportional to the stability margin: + + .. math:: + + C_1 = \bar q\, A\, C_{N,\alpha}\, (z_\text{cm} - z_\text{NP}) + + where :math:`\bar q` is the dynamic pressure and :math:`z_\text{NP}` the + neutral point of :ref:`Part 3 `. A larger margin or + higher airspeed produces a stiffer restoring moment. - :math:`C_2`, the **damping moment coefficient**, analogous to a damping - coefficient. It arises from aerodynamic resistance of the fins to rotation, - together with **jet damping** resulting from mass ejection through the - nozzle. + coefficient. It has an aerodynamic part and a jet-damping part from the + motor: + + .. math:: + + C_2 &= C_{2,\text{aero}} + C_{2,\text{jet}}, \\ + C_{2,\text{aero}} &= \tfrac12 \rho V A \sum_i \frac{A_i}{A}\, + C_{N,\alpha,i}\, (z_i - z_\text{cm})^2, \\ + C_{2,\text{jet}} &= |\dot m|\, (z_\text{n} - z_\text{cm})^2 + \dot I_L. + + The aerodynamic part comes from every surface resisting the rotation, with + :math:`z_i` the position of surface :math:`i`; a surface with a negative + slope, such as a boat tail, takes damping away. The jet part comes from the + exhaust, which leaves the nozzle (at :math:`z_\text{n}`) moving sideways + with the rocket and carries angular momentum away, while the lateral inertia + lost with the consumed propellant gives part of it back + (:math:`\dot I_L < 0`). Jet damping matters at rail exit, where the airspeed + is low; by burnout the aerodynamic part dominates. - :math:`I_L`, the **lateral moment of inertia** about the center of mass, representing the rotational inertia opposing angular acceleration. @@ -492,7 +569,7 @@ response: decays. Rockets are normally *underdamped* (:math:`\zeta < 1`), oscillating with the amplitude shrinking over several cycles. -.. figure:: ../../static/rocket/damped-oscillation.png +.. figure:: ../static/rocket/damped-oscillation.png :align: center :width: 90% @@ -505,9 +582,11 @@ RocketPy exposes each of these quantities on the ``Flight`` object: ``corrective_moment_coefficient`` (:math:`C_1`), ``damping_moment_coefficient`` (:math:`C_2`), ``pitch_natural_frequency``, ``pitch_damping_ratio``, and the corresponding -``yaw_*`` quantities. ``Flight.prints.dynamic_stability()`` summarizes them at -the key ascent instants, rail departure and burnout, together with the roll -rate at burnout: +``yaw_*`` quantities. While the rocket is on the rail it cannot swing, so the +natural frequency and damping ratio are zero up to rail departure. They are +also zero whenever there is no restoring moment (:math:`C_1 \le 0`). +``Flight.prints.dynamic_stability()`` summarizes them at the key ascent +instants, rail departure and burnout, together with the roll rate at burnout: .. jupyter-execute:: @@ -520,6 +599,86 @@ ratio as functions of time, with roll rate overlaid. test_flight.plots.dynamic_stability_data() +Response to a disturbance +------------------------- + +The natural frequency and damping ratio are easier to judge as a curve. +``disturbance_response`` tilts the rocket by a small angle (5 degrees by +default), releases it, and returns the angle over time as it swings back. It +exists in two forms: + +- ``Rocket.disturbance_response`` needs no flight simulation. You choose the + flight condition: + + - ``speed``: the airspeed, in m/s. + - ``time``: the time since ignition, in seconds, which sets the mass, the + inertia and whether the motor is burning. Default is 0. + - ``density``: the air density, in kg/m³. Default is 1.225 (sea level). + - ``speed_of_sound``: in m/s, used to find the Mach number. Default is + 340.29 (sea level). + +- ``Flight.disturbance_response`` reads the airspeed, the air density and the + angle of attack from the flight. You only choose the instant: + + - ``time``: the instant of the flight, in seconds. It must be after the + rocket leaves the rail. + +Both forms also accept: + +- ``disturbance``: the angle the rocket is tilted by, in degrees. Default is 5. +- ``plane``: ``"pitch"`` or ``"yaw"``. The two only differ for a rocket that is + not axisymmetric. Default is ``"pitch"``. +- ``duration``: how long to follow the response, in seconds. By default, long + enough for the oscillation to settle. + +Some uses: + +- **Check the response to a gust at rail exit**, where the rocket is slowest + and a crosswind disturbs it most. +- **Compare designs before flying them**: larger fins, nose ballast or a + different motor change how fast the rocket swings back and how long it keeps + swinging. +- **See what a damping ratio means**: count the swings before the curve + settles. + +The response of the reference flight at rail exit: + +.. jupyter-execute:: + + response = test_flight.disturbance_response( + time=test_flight.out_of_rail_time, + disturbance=3, # degrees + ) + response.plot() + +The same question without a flight, comparing a low and a high airspeed just +after ignition: + +.. jupyter-execute:: + + from rocketpy import Function + + slow = rocket.disturbance_response(speed=25, time=0.4, disturbance=3) + fast = rocket.disturbance_response(speed=60, time=0.4, disturbance=3) + print("25 m/s:", slow.title) + print("60 m/s:", fast.title) + Function.compare_plots( + [(slow, "25 m/s"), (fast, "60 m/s")], + lower=0, + upper=20, + title="Response to a 3° disturbance", + xlabel="Time after the disturbance (s)", + ylabel="Angle (°)", + ) + +.. note:: + + The response holds the airspeed, the air and the rocket's mass fixed, so it + is a snapshot of one instant. During the motor burn the airspeed changes + while the rocket swings, so the real motion differs. To see it, fly the + rocket in a crosswind (:ref:`Part 5 `). The response is + also only valid for small angles. + .. _stability_in_flight: Part 5: Stability in a real flight @@ -571,7 +730,7 @@ force keeps growing without limit: shown = aoa_deg <= 15 axL.plot(aoa_deg[shown], cl[shown], "-o", color="#c0392b", lw=2, ms=4) axL.annotate("stall", xy=(aoa_deg[peak], cl[peak]), - xytext=(aoa_deg[peak] + 1.5, cl[peak] + 0.03), + xytext=(aoa_deg[peak] + 1.5, cl[peak] + 0.005), fontsize=11, fontweight="bold", color="#c0392b", arrowprops=dict(arrowstyle="->", color="#c0392b")) axL.set_title("Real airfoil (NACA 0012 data)", fontweight="bold") @@ -599,11 +758,8 @@ initial slope forever**. So **a high computed rail-exit angle of attack marks a failure, not a survivable condition.** The simulation shows the rocket swinging back into -line even past the angle where a real fin would have stalled. Keep the -out-of-rail velocity high relative to the wind and the rocket stays below the -stall range; the recovery the simulation shows past it **would not happen in -reality**. In the sweeps below, read a large computed angle of attack as a -warning sign, not a number the simulation can be trusted to reproduce. +line even past the angle where a real fin would have stalled. In the examples +below, a large angle of attack is a warning sign. To actually simulate stall, or any other measured nonlinear aerodynamics, provide the coefficients directly with a generic surface (see @@ -672,19 +828,13 @@ wind, and the swing dying away: ax.grid(True) plt.show() +\\ Every stability quantity from earlier parts shows up here. The rocket returns toward zero at all because its **stability margin** (:ref:`stability_margin_part`) is positive. The center of pressure sits behind the center of mass, so the aerodynamic force restores rather than diverges. The *rate* of the wobble is the **natural frequency**. The *speed* it settles at is the **damping ratio** (:ref:`part_dynamic`). -``windy_flight.prints.dynamic_stability()`` reports both for this flight. A -positive margin only guarantees the curve trends back to zero. It says -nothing about how fast or how smoothly, which is exactly the distinction -Part 4 draws. Here the angle of attack swings through several cycles before it -settles, a sign that this rocket is only lightly damped. It recovers either -way, but for a cleaner flight, one that settles after an overshoot or two, it -could do with more damping. Stability across a launch day ----------------------------- @@ -826,121 +976,6 @@ another look before flying. :ref:`stochastic_usage` for the class walkthrough, and :ref:`MRS` for weighting a finished sample toward measured launch-day conditions. -Part 6: How much does the static margin matter? -=============================================== - -The dispersion above shows a Calisto-class rocket that is comfortably stable, -and yet a great deal of design effort in rocketry goes into chasing static -margin. The simulation lets us weigh that margin against the other things a -builder can change, and see **how much it really decides**, following -Thomas Fetter's flight-data study *How Far Does a Rocket Turn Into the -Wind?* (NARCON-2024). - -A rocket launched straight up into a crosswind turns as it climbs, and by the -time the motor burns out its flight path has tilted some degrees away from -vertical. This tilt is the *turn*, and because the launch was vertical it is -**entirely the rocket's response to the wind**, the weathercocking that -stability is meant to hold in check. Sweeping each design parameter on its own across a -realistic range, with the others left at their nominal values, shows how much -each one moves the turn: - -.. jupyter-execute:: - - def turn_at_burnout(flight): - """Flight-path tilt away from vertical at motor burnout, in degrees.""" - return 90 - flight.path_angle(flight.rocket.motor.burn_out_time) - - def vertical_flight(rocket, wind, rail=5.2): - return Flight( - rocket=rocket, environment=windy_site(wind), - rail_length=rail, inclination=90, heading=0, terminate_on_apogee=True, - ) - - def sweep(values, make_flight): - flights = [make_flight(v) for v in values] - return np.array([turn_at_burnout(f) for f in flights]), flights - - # Each lever swept alone across a realistic range; others nominal, 5 m/s wind. - winds = np.linspace(0, 14, 9) # crosswind, m/s - rails = np.linspace(1.2, 9.0, 8) # rail length sets the exit velocity - cgs = np.linspace(-0.25, 0.9, 10) # sets the static margin - masses = np.linspace(9, 30, 8) # dry mass, kg - - turn_wind, _ = sweep(winds, lambda w: vertical_flight(build_rocket(), w)) - turn_rail, rail_f = sweep(rails, lambda r: vertical_flight(build_rocket(), 5, rail=r)) - turn_cg, cg_f = sweep(cgs, lambda c: vertical_flight(build_rocket(c), 5)) - turn_mass, _ = sweep(masses, lambda m: vertical_flight(build_rocket(mass=m), 5)) - - exit_v = np.array([f.out_of_rail_velocity for f in rail_f]) - margins = np.array([f.rocket.static_margin(0) for f in cg_f]) - - panels = [ - (winds, turn_wind, "crosswind (m/s)", "wind speed", "#c0392b"), - (exit_v, turn_rail, "exit velocity (m/s)", "exit velocity (rail length)", "#e67e22"), - (margins, turn_cg, "static margin (cal)", "static margin", "#2980b9"), - (masses, turn_mass, "dry mass (kg)", "mass", "#27ae60"), - ] - ymax = max(y.max() for _, y, *_ in panels) - - fig, axs = plt.subplots(2, 2, figsize=(9.5, 7), sharey=True) - for ax, (x, y, xlabel, title, color) in zip(axs.flat, panels): - ax.fill_between(x, 0, y, color=color, alpha=0.12) - ax.plot(x, y, "-o", color=color, lw=2.4, ms=6, mfc=color, mec="white", mew=0.8) - ax.annotate(f"swing {y[-1] - y[0]:+.1f}°", # signed: low end -> high end - xy=(0.04, 0.92), xycoords="axes fraction", ha="left", va="top", - fontsize=11, fontweight="bold", color=color) - ax.set_title(title, fontweight="bold") - ax.set_xlabel(xlabel) - ax.grid(True, alpha=0.3) - ax.set_ylim(0, ymax * 1.12) - axs[0, 0].set_ylabel("turn at burnout (deg)") - axs[1, 0].set_ylabel("turn at burnout (deg)") - fig.suptitle("What moves the turn into the wind? (each lever alone; others nominal, " - "5 m/s wind)", fontsize=12, fontweight="bold") - fig.tight_layout() - plt.show() - -Each panel is labeled with its *swing*, meaning how many degrees the turn -changes as that parameter goes from the low end of its range to the high end. - -The wind dominates, mass comes next, and a higher exit velocity has a -moderate effect the other way, lowering the turn. - -**The static margin is the weakest of the four**: across a change in margin -the turn barely moves, and it levels off at high margins, holding steady well -past the over-stable range. For a Calisto-class rocket the margin is simply -not what decides how far it weathercocks. A rocket that turns hard into the -wind is easy to **misjudge as over- or super-stable**. - -What static margin it does instead is *correction*. -Keeping the center of pressure behind the center of mass is what lets a -disturbance correct itself at all (:ref:`stability_margin_part` and -:ref:`part_dynamic`), and the rail-exit angle of attack still has to stay -below the stall range. Beyond that, **a larger margin does little for a -flight like this one**. - -Part 7: Pitch and yaw planes -============================ - -A rocket with evenly spaced fins, like Calisto, is **axisymmetric**. Its -geometry does not change under rotation about the body axis, so its -stability is the same in every plane, and a single margin describes it -fully. For these rockets, ``Rocket.is_axisymmetric`` returns ``True``, and -the rest of this section does not apply. - -Some configurations are **not** axisymmetric: canards on a single axis, -off-center payloads, fins arranged asymmetrically. For these, stability -differs between the **pitch** plane and the **yaw** plane, and RocketPy -computes each one independently: - -- pitch: ``aerodynamic_center``, ``static_margin``, ``stability_margin``; -- yaw: ``aerodynamic_center_yaw``, ``static_margin_yaw``, ``stability_margin_yaw``. - -For an axisymmetric rocket, the two planes coincide. When they do not, -RocketPy issues a warning, because the unqualified ``static_margin`` then -describes the pitch plane only. In that case, the plotting and print -methods report both planes. - Helper code =========== @@ -1026,7 +1061,7 @@ small factories: (negative) lowers it. The lateral moment of inertia is adjustable separately, which changes the dynamic response without changing the static margin. The dry mass (in kilograms) and the motor can also be - swapped, for the mass and thrust studies later in this document. + swapped. Everything else, including the aerodynamics, is fixed. """ rocket = Rocket( @@ -1057,8 +1092,3 @@ small factories: type="custom_atmosphere", wind_u=wind_speed, wind_v=0 ) return site - -The three helpers that measure the turn and sweep one parameter at a time -(``turn_at_burnout``, ``vertical_flight`` and ``sweep``) are shown inline where -they are used, in Part 6 above. - diff --git a/docs/user/environment/1-atm-models/forecast.rst b/docs/user/environment/1-atm-models/forecast.rst index 92f5b6b9e..a58f05623 100644 --- a/docs/user/environment/1-atm-models/forecast.rst +++ b/docs/user/environment/1-atm-models/forecast.rst @@ -179,6 +179,8 @@ If you have a HIRESW-compatible dataset from another provider (or a local copy), you can still load it explicitly by passing the path/URL in ``file`` and an appropriate mapping in ``dictionary``. +.. code-block:: python + env_hrrr = Environment( date=now_plus_twelve, latitude=32.988528, diff --git a/docs/user/environment/1-atm-models/soundings.rst b/docs/user/environment/1-atm-models/soundings.rst index 4cf82543f..443d1acf5 100644 --- a/docs/user/environment/1-atm-models/soundings.rst +++ b/docs/user/environment/1-atm-models/soundings.rst @@ -8,71 +8,3 @@ These are profiles of temperature, pressure, humidity, and wind speed and direct measured by weather balloons or similar devices. -Wyoming Upper Air Soundings ---------------------------- - -The University of Wyoming - College of Engineering - Department of Atmospheric -Sciences has a comprehensive collection of atmospheric soundings on their website, -accessible `here `_. - -For this example, we will use the sounding from 83779 SBMT Marte Civ Observations -at 04 Feb 2019, which can be accessed using this URL: -http://weather.uwyo.edu/cgi-bin/sounding?region=samer&TYPE=TEXT%3ALIST&YEAR=2019&MONTH=02&FROM=0500&TO=0512&STNM=83779 - - -Initialize a new Environment instance: - -.. jupyter-execute:: - - from rocketpy import Environment - - url = "http://weather.uwyo.edu/cgi-bin/sounding?region=samer&TYPE=TEXT%3ALIST&YEAR=2019&MONTH=02&FROM=0500&TO=0512&STNM=83779" - - env = Environment() - env.set_atmospheric_model(type="wyoming_sounding", file=url) - env.plots.atmospheric_model() - -.. note:: - - The ``wyoming_sounding`` does not require the ``date`` parameter to be set, \ - as the data is already provided in the URL. - - -NOAA's Ruc Soundings --------------------- - -.. important:: - - From September 30th, 2024, this model is no longer available since NOAA has \ - discontinued the Ruc Soundings public service. The following message is \ - displayed on the website: \ - "On Monday, September 30, a number of legacy websites were permanently removed. \ - These sites were no longer being maintained and did not meet security and \ - design requirements mandated by NOAA. They were intended for research \ - purposes and are not designed for operational use, such as for commercial \ - purposes or the safety of life and property." - -Another option for upper air soundings is `NOAA's Ruc Soundings `_. -This service allows users to download virtual soundings from numerical weather -prediction models such as GFS, RAP, and NAM, and also real soundings from the -Integrated Global Radiosonde Archive (IGRA). - -These options can be retrieved as a text file in GSD format. However, -RocketPy no longer provides a dedicated ``set_atmospheric_model`` type for -NOAA RUC Soundings, since NOAA has discontinued the OPENDAP service. - -.. note:: - - Select ROABs as the initial data source, specify the station through its \ - WMO-ID, and opt for the ASCII (GSD format) button. - -If you need to use RUC-sounding-like data in RocketPy, convert it to one of the -supported workflows: - -- Use :ref:`custom_atmosphere` after parsing the text data. -- Use :ref:`reanalysis` or :ref:`forecast` with NetCDF/OPeNDAP sources. - -.. note:: - - The leading `r` in the URL string is used to indicate a raw string, which \ - is useful when dealing with backslashes in URLs. \ No newline at end of file diff --git a/docs/user/first_simulation.rst b/docs/user/first_simulation.rst index 85b418705..959a34c11 100644 --- a/docs/user/first_simulation.rst +++ b/docs/user/first_simulation.rst @@ -595,13 +595,18 @@ The stability margin over the flight: test_flight.plots.stability_margin_data() -The dynamic-stability quantities (natural frequency, damping ratio and the -attitude frequency response): +The dynamic-stability quantities (natural frequency and damping ratio): .. jupyter-execute:: test_flight.plots.dynamic_stability_data() +And the four of them together, next to the angles of attack they are read at: + +.. jupyter-execute:: + + test_flight.plots.stability_summary() + Visualizing the Trajectory in Google Earth ------------------------------------------ diff --git a/docs/user/function.rst b/docs/user/function.rst index 4b2d1541d..1de0262f3 100644 --- a/docs/user/function.rst +++ b/docs/user/function.rst @@ -85,18 +85,23 @@ plotted as follows: .. jupyter-execute:: - # Plot the source with standard 2d shepard interpolation + # The points cover a regular grid, so they are interpolated on it f.plot() .. important:: For datasets higher than one dimension (more than one input), the - ``Function`` class supports interpolation ``linear``, ``shepard``, ``rbf`` - and ``regular_grid``. - - The ``regular_grid`` interpolation requires a complete Cartesian grid and - must be provided as ``(axes, grid_data)``. See the ``Function`` API - documentation for details. + ``Function`` class supports the interpolations ``linear``, ``shepard`` and + ``rbf``. + + When the dataset holds every combination of the values of its inputs (a + regular grid, like a table of a coefficient against angle of attack and + Mach number), the ``Function`` notices it and interpolates on the grid, + which is faster and more accurate. Nothing has to be asked for, and + ``Function.is_regular_grid`` tells whether it happened. On a grid the + methods are ``linear`` (the default), ``nearest``, ``slinear``, ``cubic``, + ``quintic`` and ``pchip``. Ask for ``shepard`` or ``rbf`` to have such a + dataset treated as scattered points. CSV File ^^^^^^^^ @@ -187,7 +192,7 @@ In this section we are going to delve deeper on ``Function`` creation and its pa - source: the ``Function`` data source. We have explored this parameter in the section above; - inputs: a list of strings containing each input variable name. If the source only has one input, may be abbreviated as a string (e.g. "speed (m/s)"); - outputs: a list of strings containing each output variable name. If the source only has one output, may be abbreviated as a string (e.g. "total energy (J)"); -- interpolation: a string that is the interpolation method to be used if the source is a dataset. For N-D datasets, supported options are ``linear``, ``shepard``, ``rbf`` and ``regular_grid``. Defaults to ``spline`` for 1-D and ``shepard`` for N-D datasets; +- interpolation: a string that is the interpolation method to be used if the source is a dataset. For N-D datasets, supported options are ``linear``, ``shepard`` and ``rbf``, and on a regular grid ``linear``, ``nearest``, ``slinear``, ``cubic``, ``quintic`` and ``pchip``. Defaults to ``spline`` for 1-D, ``shepard`` for N-D datasets and ``linear`` on a regular grid; - extrapolation: a string that is the extrapolation method to be used if the source is a dataset. Defaults to ``constant``; - title: the title to be shown in the plots. diff --git a/docs/user/index.rst b/docs/user/index.rst index 218fb9e4f..27b303532 100644 --- a/docs/user/index.rst +++ b/docs/user/index.rst @@ -46,6 +46,6 @@ RocketPy's User Guide :maxdepth: 2 :caption: Further Analysis + Center of Pressure and Stability Function - Utilities - Center of Pressure and Stability \ No newline at end of file + Utilities \ No newline at end of file diff --git a/docs/user/rocket/generic_surface.rst b/docs/user/rocket/generic_surface.rst index ffe210be8..7e22ea9e4 100644 --- a/docs/user/rocket/generic_surface.rst +++ b/docs/user/rocket/generic_surface.rst @@ -75,37 +75,53 @@ drag :math:`D` and the (wind-frame) side force :math:`Q`: Relating the two frames ~~~~~~~~~~~~~~~~~~~~~~~~ -The two are the same force, related by the angle-of-attack/sideslip rotation -:math:`\mathbf{M}_{BW}`, which transforms the wind frame into the body frame: +The two are the same force, written along different axes. Let +:math:`\hat{\mathbf{u}} = (u_x, u_y, u_z)` be the direction of the rocket's +velocity relative to the air, in the body frame, and +:math:`h = \sqrt{u_y^2 + u_z^2}`. The three wind-frame forces act along: -.. math:: - \vec{\mathbf{F}}_B=\mathbf{M}_{BW}\cdot\begin{bmatrix}Q\\-L\\-D\end{bmatrix}_W +- **drag**, against the velocity: :math:`-\hat{\mathbf{u}}`; +- **lift**, perpendicular to the velocity and lying in the body + :math:`y_B z_B` plane: :math:`(0,\ -u_z,\ u_y)/h`; +- **side force**, perpendicular to both: + :math:`(h,\ -u_x u_y/h,\ -u_x u_z/h)`. -where +The velocity direction follows from the angle of attack and the sideslip angle +(defined in `Angles of attack and sideslip`_ below): .. math:: - \mathbf{M}_{BW} = \begin{bmatrix} - 1 & 0 & 0 \\ - 0 & \cos(\alpha) & \sin(\alpha) \\ - 0 & -\sin(\alpha) & \cos(\alpha) - \end{bmatrix} - \begin{bmatrix} - \cos(\beta) & 0 & \sin(\beta) \\ - 0 & 1 & 0 \\ - -\sin(\beta) & 0 & \cos(\beta) - \end{bmatrix} - -The force coefficients follow the same rotation. In the wind frame they are the + \hat{\mathbf{u}} \parallel + \begin{bmatrix} + \sin\beta\cos\alpha \\ \sin\alpha\cos\beta \\ \cos\alpha\cos\beta + \end{bmatrix} + +with the sign of :math:`\cos\alpha`, so that it also holds when the rocket flies +tail first. + +The force coefficients follow the same relation. In the wind frame they are the lift :math:`C_L`, side :math:`C_Q` and drag :math:`C_D`; in the body frame the normal :math:`C_N`, side :math:`C_Y` and axial :math:`C_A`: .. math:: \begin{aligned} - C_N &= \cos\alpha\, C_L + \sin\alpha\,(\sin\beta\, C_Q + \cos\beta\, C_D) \\ - C_Y &= \cos\beta\, C_Q - \sin\beta\, C_D \\ - C_A &= -\sin\alpha\, C_L + \cos\alpha\,(\sin\beta\, C_Q + \cos\beta\, C_D) + C_N &= \frac{u_z}{h}\, C_L + \frac{u_x u_y}{h}\, C_Q + u_y\, C_D \\ + C_Y &= h\, C_Q - u_x\, C_D \\ + C_A &= -\frac{u_y}{h}\, C_L + \frac{u_x u_z}{h}\, C_Q + u_z\, C_D \end{aligned} +With no sideslip (:math:`\beta = 0`) these are the familiar +:math:`C_N = \cos\alpha\, C_L + \sin\alpha\, C_D` and +:math:`C_A = -\sin\alpha\, C_L + \cos\alpha\, C_D`; with no angle of attack +(:math:`\alpha = 0`), :math:`C_Y = \cos\beta\, C_Q - \sin\beta\, C_D` and +:math:`C_A = \sin\beta\, C_Q + \cos\beta\, C_D`. + +.. note:: + The angle of attack and the sideslip angle used by RocketPy are each measured + in one body plane. They are not the two angles of a rotation sequence, so the + conversion is built from the velocity direction rather than by chaining a + rotation by :math:`\alpha` and a rotation by :math:`\beta`, which would only + be exact with one of the two angles at zero. + At small angles these reduce to :math:`C_N \approx C_L`, :math:`C_Y \approx C_Q` and :math:`C_A \approx C_D`. @@ -116,14 +132,12 @@ coefficients the force is obtained directly, with no rotation: .. math:: \vec{\mathbf{F}}_B =\begin{bmatrix}Y\\-N\\-A\end{bmatrix}_B= \overline{q}\cdot A_{ref}\cdot\begin{bmatrix}C_Y\\-C_N\\-C_A\end{bmatrix}_B -while **wind-frame** coefficients are rotated into the body frame first: - -.. math:: - \vec{\mathbf{F}}_B =\mathbf{M}_{BW}\cdot\overline{q}\cdot A_{ref}\cdot\begin{bmatrix}C_Q\\-C_L\\-C_D\end{bmatrix}_W - where :math:`\bar{q}` is the dynamic pressure and :math:`A_{ref}` the reference area (commonly the rocket's cross-sectional area). +**Wind-frame** coefficients are first converted to the body-frame ones +with the relations above. + Moments ~~~~~~~ @@ -162,6 +176,21 @@ and the total angle of attack is .. math:: \alpha_{\text{tot}} = \arccos\left(\frac{\mathbf{\vec{V}}\cdot\mathbf{z_B}}{||\mathbf{\vec{V}}||\cdot||\mathbf{z_B}||}\right) +The direction the crossflow comes from, around the rocket's axis, is the **roll +angle of the wind**: + +.. math:: + \phi = \operatorname{atan2}\left(V_y,\ V_x\right) + +The pair :math:`(\alpha, \beta)` and the pair +:math:`(\alpha_{\text{tot}}, \phi)` describe the same flow direction, like +the two coordinates of a point on a map given as east/north or as +distance/bearing: + +.. math:: + \tan\alpha = \tan\alpha_{\text{tot}}\,\sin\phi, \qquad + \tan\beta = \tan\alpha_{\text{tot}}\,\cos\phi + .. note:: When the simulation is done, the total angle of attack is accessed through the :attr:`rocketpy.Flight.angle_of_attack` attribute. The partial angles of @@ -223,19 +252,22 @@ Constructor parameters Commonly the rocket's diameter. - ``coefficients`` (dict): the force and moment coefficients, by name (detailed in `Coefficients`_ below). -- ``center_of_pressure`` (tuple, optional): the point where the surface's forces - and moments are applied, in the surface's local frame. Default ``(0, 0, 0)``. - See `Moment reference point`_. +- ``center_of_pressure`` (tuple or list, optional): the point where the + surface's forces and moments are applied, as ``(x, y, z)`` in meters. It is + measured from the position the surface is added to the rocket at, with ``z`` + along the rocket's centerline, positive toward the nose. Default + ``(0, 0, 0)``. See `Moment reference point`_. - ``name`` (str, optional): a name for the surface. Default ``"Generic Surface"``. - ``reynolds_length`` (int or float, optional): length scale, in meters, of the Reynolds number fed to the coefficients. Default ``None`` (uses ``reference_length``). - ``interpolation`` (str or dict, optional): how tabulated coefficients are - interpolated between their data points. Default ``None``. See - :ref:`generic_surface_interpolation`. + interpolated between their data points. Default ``None``, which uses + ``"linear"``. See :ref:`generic_surface_interpolation`. - ``extrapolation`` (str or dict, optional): how tabulated coefficients behave - outside their tabulated range. Default ``None``. See + outside their tabulated range. Default ``None``, which holds the value at + the nearest end of the table (``"constant"``). See :ref:`generic_surface_interpolation`. - ``force_convention`` (str, optional): the frame the force coefficients are given in, ``"body"`` or ``"wind"``. Default ``None`` (inferred from the @@ -257,27 +289,75 @@ its value. The body-frame coefficient names are: - ``cn``: Yawing moment coefficient. - ``cl``: Rolling moment coefficient. -Alternatively, you can supply the force coefficients in the **wind frame** as -``cL`` (lift), ``cQ`` (side) and ``cD`` (drag) in place of ``cN``/``cY``/``cA`` -(the moment coefficients ``cm``/``cn``/``cl`` are shared by both frames). By -default the frame is inferred from the names you pass; set ``force_convention`` -(``"body"`` or ``"wind"``) to state it explicitly. Whichever frame you choose, -all nine coefficients remain available as attributes (``surface.cN``, -``surface.cL``, ...), converted on demand from the ones you provided using the -rotation described in `Relating the two frames`_ above. +Alternatively, you can supply the force coefficients in the **wind frame**: + +- ``cL`` (lift), ``cQ`` (side) and ``cD`` (drag) take the place of ``cN``, + ``cY`` and ``cA``. +- The moment coefficients ``cm``, ``cn`` and ``cl`` are the same in both frames. + +By default the frame is inferred from the names you pass. Set +``force_convention`` to ``"body"`` or ``"wind"`` to state it explicitly. + +Whichever frame you choose, all nine coefficients are available as attributes +afterwards (``surface.cN``, ``surface.cL``, ...). The ones you did not provide +are converted using the relations in `Relating the two frames`_ above. Only one coefficient is required, and any combination can be provided; the ones you omit are treated as zero. -Each coefficient is a function of the same seven independent variables: - -- Angle of attack (:math:`\alpha`) in radians. -- Side slip angle (:math:`\beta`) in radians. -- Mach number (:math:`Ma`). -- Reynolds number (:math:`Re`). -- Pitch rate (:math:`q^{*}`), non-dimensional (reduced). -- Yaw rate (:math:`r^{*}`), non-dimensional (reduced). -- Roll rate (:math:`p^{*}`), non-dimensional (reduced). +.. _coefficient_variables: + +Each coefficient is a function of the same seven independent variables. When +you give a coefficient, you name the variables it uses with these names: + +.. list-table:: + :header-rows: 1 + :widths: 22 78 + + * - Name + - Variable + * - ``alpha`` + - Angle of attack (:math:`\alpha`), in radians. + * - ``beta`` + - Side slip angle (:math:`\beta`), in radians. + * - ``mach`` + - Mach number (:math:`Ma`). + * - ``reynolds`` + - Reynolds number (:math:`Re`). + * - ``pitch_rate`` + - Pitch rate (:math:`q^{*}`), non-dimensional (reduced). + * - ``yaw_rate`` + - Yaw rate (:math:`r^{*}`), non-dimensional (reduced). + * - ``roll_rate`` + - Roll rate (:math:`p^{*}`), non-dimensional (reduced). + +The angles can also be given in other forms. RocketPy converts them for you: + +.. list-table:: + :header-rows: 1 + :widths: 22 78 + + * - Name + - Variable + * - ``alpha_deg`` + - Angle of attack, in degrees. + * - ``beta_deg`` + - Side slip angle, in degrees. + * - ``alpha_total`` + - Total angle of attack (:math:`\alpha_{\text{tot}}`), in radians: the + angle between the rocket's axis and the air. See :ref:`totalangle`. + * - ``alpha_total_deg`` + - Total angle of attack, in degrees. + * - ``phi`` + - Roll angle of the wind (:math:`\phi`), in radians: the direction around + the body the air comes from. See :ref:`totalangle`. + * - ``phi_deg`` + - Roll angle of the wind, in degrees. + +These are all the accepted names. A coefficient cannot use the same angle under +two names, such as ``alpha`` and ``alpha_deg`` together. A +:class:`rocketpy.ControllableGenericSurface` adds the names of its own control +variables. .. important:: The angular rates are the conventional **non-dimensional reduced rates**, not @@ -295,17 +375,96 @@ Each coefficient is a function of the same seven independent variables: Once evaluated, the coefficients are turned into body-frame forces and moments exactly as described in `Wind Frame and Body Frame`_ above (wind-frame inputs -are rotated into the body frame first). +are converted to the body frame first). + +Each coefficient value can be given in any of these forms: + +.. list-table:: + :header-rows: 1 + :widths: 30 70 + + * - Form + - Example + * - a number (constant) + - ``"cA": 0.4`` + * - a function + - ``"cN": lambda alpha, mach: 2 * alpha`` + * - a ``.csv`` file with a header + - ``"cN": "cN.csv"`` + * - a list or numpy array of data points + - ``"cA": ([[0, 0.4], [1, 0.6]], ["mach"])`` + * - values on a regular grid + - ``"cN": ({"alpha": alphas, "mach": machs}, values)`` + * - a :class:`rocketpy.Function` + - ``"cA": Function(points, "mach", "cA")`` + * - one file for several coefficients + - ``GenericSurface.from_csv("aero.csv", area, length)`` + * - any of the above with its variables named + - ``"cN": (source, ["alpha", "mach"])`` + +Every coefficient must say which of the seven variables it uses. A function says +it through the names of its arguments, a ``.csv`` file through its header and a +:class:`rocketpy.Function` through the names of its inputs. When the source does +not carry names (a list of points, a ``.csv`` file without a header, a function +whose arguments are named otherwise), give the coefficient as a pair: the source, +then the list of its variables in order, as in the last two rows above. + +Angles in degrees +^^^^^^^^^^^^^^^^^ + +The angles are in radians. If your data is in degrees, as most wind tunnel +reports and aerodynamics programs give it, add ``_deg`` to the name of the +variable: ``alpha_deg``, ``beta_deg``, ``alpha_total_deg`` or ``phi_deg``. This +works wherever a variable is named: the header of a ``.csv`` file, the list of +variables given with a table, the grid form, the input of a +:class:`rocketpy.Function` and the argument of a function: -Each coefficient value can be given three ways: a single number for a constant, -a callable function of the seven variables, or a path to a ``.csv`` file of -tabulated data. +.. code-block:: python + + coefficients = { + "cN": "cN_rasaero.csv", # header: alpha_deg, mach, cN + "cm": (moment_points, ["alpha_deg", "mach"]), + "cA": lambda alpha_deg, mach: 0.4 + 0.002 * alpha_deg**2, + } + +RocketPy converts the angle before reading the source, so nothing else changes. +A table whose values of an angle given in radians go beyond what such an angle +can be (about 3.14) raises a warning, since it almost surely is in degrees. + +.. note:: + This is about the angle a table is tabulated against. A *slope* given per + degree, such as a ``cN_alpha`` of a :class:`rocketpy.LinearGenericSurface` in + 1/deg, must be multiplied by ``180 / pi`` to give it per radian. + +.. important:: + RocketPy never guesses the variable of a table. A one-column table given + without a name raises an error, so that a drag curve tabulated against Mach + can never be read against the angle of attack by mistake. Defining a coefficient as a callable ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -A coefficient can be any callable that takes the seven independent variables and -returns the value: +A coefficient can be any function that returns its value. Name its arguments +after the variables it uses, in any order, and leave out the ones it does not +use: + +.. code-block:: python + + def normal_force_coefficient(alpha, mach): + return (2 + 0.5 * mach) * alpha + +The accepted argument names are all the ones listed in +:ref:`the tables above `, including the angles in +degrees and the total angle of attack: + +.. code-block:: python + + def axial_force_coefficient(alpha_total_deg, mach): + return 0.4 + 0.1 * mach + 0.002 * alpha_total_deg**2 + +A function that takes the seven main variables (``alpha``, ``beta``, ``mach``, +``reynolds``, ``pitch_rate``, ``yaw_rate`` and ``roll_rate``), in that order, +may name its arguments freely: .. code-block:: python @@ -313,29 +472,41 @@ returns the value: ... return value -Any algorithm can be implemented inside to compute the coefficient. +Any algorithm can be implemented inside to compute the coefficient. Arguments +with a default value are not counted as variables, so a function can carry the +constants of your model, and ``functools.partial`` can set them: + +.. code-block:: python + + def normal_force_coefficient(alpha, mach, slope=2.0): + return (slope + 0.5 * mach) * alpha + + coefficients = {"cN": functools.partial(normal_force_coefficient, slope=2.4)} Defining a coefficient from a CSV file ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ A coefficient can also be tabulated in a ``.csv`` file. The file must have a header naming its columns. The independent-variable columns are optional, but -those present must use these exact names: +those present must be named after the variables: -- ``alpha``: Angle of attack. -- ``beta``: Side slip angle. +- ``alpha``: Angle of attack (``alpha_deg`` for degrees). +- ``beta``: Side slip angle (``beta_deg`` for degrees). +- ``alpha_total`` and ``phi``: total angle of attack and roll angle of the + wind (``alpha_total_deg``, ``phi_deg`` for degrees), see :ref:`totalangle`. - ``mach``: Mach number. - ``reynolds``: Reynolds number. -- ``pitch_rate``: Pitch rate. -- ``yaw_rate``: Yaw rate. -- ``roll_rate``: Roll rate. +- ``pitch_rate``: Pitch rate (reduced). +- ``yaw_rate``: Yaw rate (reduced). +- ``roll_rate``: Roll rate (reduced). The **last** column holds the coefficient value; it **must** have a header, but -the header name can be anything. +the header name can be anything. Spaces after the commas and quotes around the +names are fine. .. important:: Not all independent-variable columns need to be present, but the columns that - are present must be named exactly as above. They can be in any order. + are present must be named as above. They can be in any order. An example ``.csv`` file, tabulated against angle of attack and Mach: @@ -363,6 +534,138 @@ An example ``.csv`` file, tabulated against angle of attack and Mach: creating the surface so the Reynolds number the simulation feeds your table matches the one it was built against. +.. _totalangle: + +Coefficients against the total angle of attack +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Most aerodynamic data for rockets (wind tunnel reports, CFD sweeps, programs +such as RASAero) gives the normal force, the axial force and +the pitch moment against the Mach number and the **total** angle of attack, which +is never negative. To use such data as it is, name its variable ``alpha_total`` +(or ``alpha_total_deg``). No other option is needed: + +.. code-block:: python + + surface = GenericSurface( + reference_area=rocket.area, + reference_length=2 * rocket.radius, + coefficients={ + "cN": "cN_vs_total_angle.csv", # header: alpha_total_deg, mach, cN + "cm": "cm_vs_total_angle.csv", # header: alpha_total_deg, mach, cm + "cA": lambda alpha_total, mach: 0.4 + 0.1 * mach + alpha_total**2, + }, + ) + +What a coefficient given against ``alpha_total`` means depends on the +coefficient: + +- ``cN`` (normal force) and ``cm`` (pitch moment) act in the plane that holds + the rocket's axis and the wind. RocketPy splits them between the pitch and the + yaw plane, :math:`C_N\sin\phi` and :math:`-C_N\cos\phi` (and the same for + the moment), so the force always pushes along the crossflow whatever direction + the wind comes from. +- ``cL`` (lift) works the same way, in the wind frame. With the drag ``cD`` it + gives the normal force in that plane and the axial force. +- ``cA`` (axial force), ``cD`` (drag) and ``cl`` (roll moment) have no direction + across the axis and are used as they are. This also holds for the rocket's + ``power_off_drag`` and ``power_on_drag``. + +Three rules apply to a ``cN``, ``cL`` or ``cm`` given against ``alpha_total``: + +- It must be zero at zero total angle (see the note below). +- Leave out ``cY``, ``cQ`` and ``cn``. The part in the other plane comes from + the split, and there is no side force or yaw moment in the plane of the wind. +- It cannot also depend on ``alpha`` or ``beta``. + +A coefficient that depends on the direction the wind comes from around the +body can take the roll angle of the wind ``phi`` as well. It is then used as +given, so write the split yourself: + +.. code-block:: python + + def strength(alpha_total, phi): + return 2 * alpha_total * (1 + 0.1 * np.cos(4 * phi)) + + coefficients = { + "cN": lambda alpha_total, phi: strength(alpha_total, phi) * np.sin(phi), + "cY": lambda alpha_total, phi: -strength(alpha_total, phi) * np.cos(phi), + } + +Data against the total angle of attack is known in the literature as the +*aeroballistic* frame. The built-in nose cone, tail and fin sets work this way +internally. + +.. note:: + At zero total angle of attack there is no crossflow and its direction is not + defined, so ``cN``, ``cL`` and ``cm`` must be zero there, as they are for any rocket + with rotational symmetry. RocketPy checks this when the surface is built and + raises an error otherwise. A table must therefore start at 0 degrees: a table + whose first row is at 2 degrees holds that value all the way down to zero, + which would make the force flip sign across zero angle and the stability + slope meaningless. + +Several coefficients from one file +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Aerodynamic data usually comes as one table with a column per variable and a +column per coefficient: + +.. code-block:: + + alpha_deg, mach, cN, cA, cm + -2, 0.3, -0.085, 0.42, 0.260 + 0, 0.3, 0.000, 0.42, 0.000 + 2, 0.3, 0.085, 0.42, -0.260 + +:meth:`rocketpy.GenericSurface.from_csv` builds the surface from such a file in +one step. Every coefficient is read against all the variable columns: + +.. code-block:: python + + surface = GenericSurface.from_csv( + "aero.csv", + reference_area=rocket.area, + reference_length=2 * rocket.radius, + ) + +Any other argument of the class (``center_of_pressure``, ``name``, +``active_during``, ...) can be passed along. The same method exists on +:class:`rocketpy.LinearGenericSurface`, with derivative columns such as +``cN_alpha`` and ``cm_q``, and on :class:`rocketpy.ControllableGenericSurface`, +where a control can be one of the variable columns. + +A file written by another program has its own column names. Translate them with +``columns``; the columns you do not list are ignored: + +.. code-block:: python + + power_off = GenericSurface.from_csv( + "export.csv", + reference_area=rocket.area, + reference_length=2 * rocket.radius, + columns={ + "Mach": "mach", + "Alpha": "alpha_deg", + "CN": "cN", + "CA Power-Off": "cA", + }, + active_during="power_off", + ) + +.. warning:: + RocketPy's ``alpha`` is measured in one plane and takes both signs. Many + programs tabulate against the *total* angle of attack, which is never + negative. Name that column ``alpha_total`` (or ``alpha_total_deg``), see + `Coefficients against the total angle of attack`_. Read as ``alpha``, such a table would give no restoring force when + the rocket pitches the other way; RocketPy warns when a table looks like this. + +.. note:: + Programs often report the center of pressure as a position instead of a + pitch moment coefficient. With :math:`x_{cp}` measured from the point the + surface is placed at, positive toward the nose, the moment coefficient about + that point is :math:`C_m = C_N \, x_{cp} / L_{ref}`. + Adding the surface to the rocket ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ @@ -430,68 +733,82 @@ data rather than full tables: the surface builds each coefficient by summing its derivatives times the independent variables. For every one of the six coefficients (``cN``, ``cY``, ``cA``, ``cm``, ``cn``, -``cl``), you provide a constant term and one derivative per independent variable: +``cl``), you provide a constant term and one derivative per angle and per +rotation rate: -- :math:`C_{0}`: the coefficient value at the reference condition. +- :math:`C_{0}`: the coefficient value at zero angle of attack, zero sideslip + and zero rates. - :math:`C_{\alpha}=\frac{dC}{d\alpha}`: derivative with respect to angle of attack. - :math:`C_{\beta}=\frac{dC}{d\beta}`: derivative with respect to side slip angle. -- :math:`C_{Ma}=\frac{dC}{dMa}`: derivative with respect to Mach number. -- :math:`C_{Re}=\frac{dC}{dRe}`: derivative with respect to Reynolds number. - :math:`C_{q}=\frac{dC}{dq}`: derivative with respect to pitch rate. - :math:`C_{r}=\frac{dC}{dr}`: derivative with respect to yaw rate. - :math:`C_{p}=\frac{dC}{dp}`: derivative with respect to roll rate. -Just like the plain generic surface, each of these terms is itself a function of -all seven independent variables, and may be a constant, a callable, or a -tabulated ``.csv`` file. +Just like the plain generic surface, each of these terms may itself depend on +the Mach number, the Reynolds number or any other of the seven independent +variables, and may be a constant, a callable, or a tabulated ``.csv`` file. +There is no separate Mach or Reynolds derivative: a normal-force slope that +changes with Mach is given as ``cN_alpha`` tabulated against Mach. How the coefficients are assembled ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -The derivatives are first combined into **forcing** coefficients, which depend on -the steady flow state (angles, Mach, Reynolds): +Each coefficient is the sum of a **forcing** part, which follows the angle of +attack and the sideslip angle: .. math:: \begin{aligned} - C_{Nf} &= C_{N0} + C_{N\alpha}\cdot\alpha + C_{N\beta}\cdot\beta + C_{NMa}\cdot Ma + C_{NRe}\cdot Re \\ - C_{Yf} &= C_{Y0} + C_{Y\alpha}\cdot\alpha + C_{Y\beta}\cdot\beta + C_{YMa}\cdot Ma + C_{YRe}\cdot Re \\ - C_{Af} &= C_{A0} + C_{A\alpha}\cdot\alpha + C_{A\beta}\cdot\beta + C_{AMa}\cdot Ma + C_{ARe}\cdot Re \\ - C_{mf} &= C_{m0} + C_{m\alpha}\cdot\alpha + C_{m\beta}\cdot\beta + C_{mMa}\cdot Ma + C_{mRe}\cdot Re \\ - C_{nf} &= C_{n0} + C_{n\alpha}\cdot\alpha + C_{n\beta}\cdot\beta + C_{nMa}\cdot Ma + C_{nRe}\cdot Re \\ - C_{lf} &= C_{l0} + C_{l\alpha}\cdot\alpha + C_{l\beta}\cdot\beta + C_{lMa}\cdot Ma + C_{lRe}\cdot Re + C_{Nf} &= C_{N0} + C_{N\alpha}\cdot\alpha + C_{N\beta}\cdot\beta \\ + C_{Yf} &= C_{Y0} + C_{Y\alpha}\cdot\alpha + C_{Y\beta}\cdot\beta \\ + C_{Af} &= C_{A0} + C_{A\alpha}\cdot\alpha + C_{A\beta}\cdot\beta \\ + C_{mf} &= C_{m0} + C_{m\alpha}\cdot\alpha + C_{m\beta}\cdot\beta \\ + C_{nf} &= C_{n0} + C_{n\alpha}\cdot\alpha + C_{n\beta}\cdot\beta \\ + C_{lf} &= C_{l0} + C_{l\alpha}\cdot\alpha + C_{l\beta}\cdot\beta \end{aligned} -and **damping** coefficients, which depend on the rotation rates: +and a **damping** part, which follows the non-dimensional rotation rates +:math:`p^{*}` (roll), :math:`q^{*}` (pitch) and :math:`r^{*}` (yaw) defined in +`Coefficients`_ above: .. math:: \begin{aligned} - C_{Nd} &= C_{N_{q}}\cdot q + C_{N_{r}}\cdot r + C_{N_{p}}\cdot p \\ - C_{Yd} &= C_{Y_{q}}\cdot q + C_{Y_{r}}\cdot r + C_{Y_{p}}\cdot p \\ - C_{Ad} &= C_{A_{q}}\cdot q + C_{A_{r}}\cdot r + C_{A_{p}}\cdot p \\ - C_{md} &= C_{m_{q}}\cdot q + C_{m_{r}}\cdot r + C_{m_{p}}\cdot p \\ - C_{nd} &= C_{n_{q}}\cdot q + C_{n_{r}}\cdot r + C_{n_{p}}\cdot p \\ - C_{ld} &= C_{l_{q}}\cdot q + C_{l_{r}}\cdot r + C_{l_{p}}\cdot p + C_{Nd} &= C_{N_{p}}\cdot p^{*} + C_{N_{q}}\cdot q^{*} + C_{N_{r}}\cdot r^{*} \\ + C_{Yd} &= C_{Y_{p}}\cdot p^{*} + C_{Y_{q}}\cdot q^{*} + C_{Y_{r}}\cdot r^{*} \\ + C_{Ad} &= C_{A_{p}}\cdot p^{*} + C_{A_{q}}\cdot q^{*} + C_{A_{r}}\cdot r^{*} \\ + C_{md} &= C_{m_{p}}\cdot p^{*} + C_{m_{q}}\cdot q^{*} + C_{m_{r}}\cdot r^{*} \\ + C_{nd} &= C_{n_{p}}\cdot p^{*} + C_{n_{q}}\cdot q^{*} + C_{n_{r}}\cdot r^{*} \\ + C_{ld} &= C_{l_{p}}\cdot p^{*} + C_{l_{q}}\cdot q^{*} + C_{l_{r}}\cdot r^{*} \end{aligned} -The body-frame forces and moments then follow, the damping terms scaled by the -reduced-rate factor :math:`\frac{L_{ref}}{2V}`: +so that, for example, :math:`C_m = C_{mf} + C_{md}`. The two parts are added: a +derivative that opposes the motion, such as the pitch damping :math:`C_{m_q}` of +a stable rocket, is a negative number. + +Every derivative may itself vary with the Mach number, the Reynolds number or any +other of the seven variables, exactly like the coefficients of a +:class:`rocketpy.GenericSurface`. + +The body-frame forces and moments then follow as for any generic surface: .. math:: \begin{aligned} - N &= \overline{q}\cdot A_{ref}\cdot C_{Nf} + \overline{q}\cdot A_{ref}\cdot \frac{L_{ref}}{2V} C_{Nd} \\ - Y &= \overline{q}\cdot A_{ref}\cdot C_{Yf} + \overline{q}\cdot A_{ref}\cdot \frac{L_{ref}}{2V} C_{Yd} \\ - A &= \overline{q}\cdot A_{ref}\cdot C_{Af} + \overline{q}\cdot A_{ref}\cdot \frac{L_{ref}}{2V} C_{Ad} \\ - M_{m} &= \overline{q}\cdot A_{ref}\cdot L_{ref}\cdot C_{mf} + \overline{q}\cdot A_{ref}\cdot L_{ref}\cdot \frac{L_{ref}}{2V} C_{md} \\ - M_{n} &= \overline{q}\cdot A_{ref}\cdot L_{ref}\cdot C_{nf} + \overline{q}\cdot A_{ref}\cdot L_{ref}\cdot \frac{L_{ref}}{2V} C_{nd} \\ - M_{l} &= \overline{q}\cdot A_{ref}\cdot L_{ref}\cdot C_{lf} + \overline{q}\cdot A_{ref}\cdot L_{ref}\cdot \frac{L_{ref}}{2V} C_{ld} + N &= \overline{q}\cdot A_{ref}\cdot C_N &\qquad M_m &= \overline{q}\cdot A_{ref}\cdot L_{ref}\cdot C_m \\ + Y &= \overline{q}\cdot A_{ref}\cdot C_Y &\qquad M_n &= \overline{q}\cdot A_{ref}\cdot L_{ref}\cdot C_n \\ + A &= \overline{q}\cdot A_{ref}\cdot C_A &\qquad M_l &= \overline{q}\cdot A_{ref}\cdot L_{ref}\cdot C_l \end{aligned} +After the surface is created, the whole coefficients are available as +``surface.cN``, ``surface.cm`` and so on, and the two parts as ``surface.cNf`` and +``surface.cNd``, ``surface.cmf`` and ``surface.cmd`` and so on. All of them are +functions of the seven variables. + Defining a linear generic surface ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ A linear generic surface takes the **same parameters** as -:class:`rocketpy.GenericSurface`, only the -``coefficients`` dictionary is different. Each key follows the pattern +:class:`rocketpy.GenericSurface`, plus ``axisymmetric`` (see +:ref:`lineargenericsurface_axisymmetric`). Only the ``coefficients`` dictionary +is different. Each key follows the pattern ``_``. For example ``cN_alpha`` is :math:`C_{N\alpha}`, ``cm_q`` is :math:`C_{m_q}`, and ``cN_0`` is the constant term :math:`C_{N0}`. Any term you omit is zero. @@ -518,48 +835,36 @@ An example defining **all** the coefficient derivatives: "cN_0": "cN_0.csv", "cN_alpha": "cN_alpha.csv", "cN_beta": "cN_beta.csv", - "cN_Ma": "cN_Ma.csv", - "cN_Re": "cN_Re.csv", "cN_q": "cN_q.csv", "cN_r": "cN_r.csv", "cN_p": "cN_p.csv", "cY_0": "cY_0.csv", "cY_alpha": "cY_alpha.csv", "cY_beta": "cY_beta.csv", - "cY_Ma": "cY_Ma.csv", - "cY_Re": "cY_Re.csv", "cY_q": "cY_q.csv", "cY_r": "cY_r.csv", "cY_p": "cY_p.csv", "cA_0": "cA_0.csv", "cA_alpha": "cA_alpha.csv", "cA_beta": "cA_beta.csv", - "cA_Ma": "cA_Ma.csv", - "cA_Re": "cA_Re.csv", "cA_q": "cA_q.csv", "cA_r": "cA_r.csv", "cA_p": "cA_p.csv", "cm_0": "cm_0.csv", "cm_alpha": "cm_alpha.csv", "cm_beta": "cm_beta.csv", - "cm_Ma": "cm_Ma.csv", - "cm_Re": "cm_Re.csv", "cm_q": "cm_q.csv", "cm_r": "cm_r.csv", "cm_p": "cm_p.csv", "cn_0": "cn_0.csv", "cn_alpha": "cn_alpha.csv", "cn_beta": "cn_beta.csv", - "cn_Ma": "cn_Ma.csv", - "cn_Re": "cn_Re.csv", "cn_q": "cn_q.csv", "cn_r": "cn_r.csv", "cn_p": "cn_p.csv", "cl_0": "cl_0.csv", "cl_alpha": "cl_alpha.csv", "cl_beta": "cl_beta.csv", - "cl_Ma": "cl_Ma.csv", - "cl_Re": "cl_Re.csv", "cl_q": "cl_q.csv", "cl_r": "cl_r.csv", "cl_p": "cl_p.csv", @@ -567,6 +872,96 @@ An example defining **all** the coefficient derivatives: ) rocket.add_surfaces(linear_generic_surface, position=(0,0,0)) +.. _lineargenericsurface_axisymmetric: + +Writing an axisymmetric rocket with derivatives +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. note:: + This section is only about :class:`rocketpy.LinearGenericSurface`, where + each plane has its own derivatives. A :class:`rocketpy.GenericSurface` whose + ``cN`` and ``cm`` are given against ``alpha_total`` is already axisymmetric: + the same data is used in every plane, with nothing more to write (see + :ref:`totalangle`). + +Stability derivatives are usually reported for one plane only: the normal +force slope :math:`C_{N\alpha}`, the pitch moment slope :math:`C_{m\alpha}` +and the pitch damping :math:`C_{m_q}`. A linear generic surface has separate +derivatives for the yaw plane, and a plane without derivatives produces no +force. + +For a rocket that behaves the same in every plane (evenly spaced fins, no +canards on a single axis), give the pitch-plane derivatives and pass +``axisymmetric=True``. The yaw-plane ones are filled in for you: + +.. code-block:: python + + rocket_aero = LinearGenericSurface( + reference_area=rocket.area, + reference_length=2 * rocket.radius, + coefficients={ + "cA_0": 0.5, + "cN_alpha": 12.0, + "cm_alpha": -30.0, + "cN_q": 40.0, + "cm_q": -800.0, + "cl_p": -9.0, + }, + axisymmetric=True, + ) + rocket.add_full_body_aerodynamics(rocket_aero) + assert rocket.is_axisymmetric + +With ``axisymmetric=True``: + +- Do not give any yaw-plane derivative (``cY_*``, ``cQ_*`` or ``cn_*``). +- Do not give a sideways force or moment at zero angle (``cN_0``, ``cm_0``, + ``cN_p``, ``cm_p``): it would point in one direction. +- A pitch-plane derivative may depend on ``mach``, ``reynolds``, the rates and + ``alpha_total``, but not on ``alpha``, ``beta`` or ``phi``, which single out + one plane. +- The axial and roll derivatives are used as given. + +Writing both planes yourself +^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +Without ``axisymmetric=True`` you give both planes. For an axisymmetric rocket, +the yaw derivatives are the pitch ones with two sign changes, which come from +the directions of the body axes: + +.. math:: + \begin{aligned} + C_{Y\beta} &= -C_{N\alpha} &\qquad C_{Y_r} &= C_{N_q} \\ + C_{n\beta} &= -C_{m\alpha} &\qquad C_{n_r} &= C_{m_q} + \end{aligned} + +The same rocket as above, written for both planes: + +.. code-block:: python + + rocket_aero = LinearGenericSurface( + reference_area=rocket.area, + reference_length=2 * rocket.radius, + coefficients={ + "cA_0": 0.5, + "cN_alpha": 12.0, "cY_beta": -12.0, + "cm_alpha": -30.0, "cn_beta": 30.0, + "cN_q": 40.0, "cY_r": 40.0, + "cm_q": -800.0, "cn_r": -800.0, + "cl_p": -9.0, + }, + ) + +``rocket.is_axisymmetric`` tells you whether the signs are right: it is +``False`` for a rocket written with ``cY_beta = +12``. + +.. note:: + A linear surface treats the angle of attack and the sideslip angle + separately, so with both at once it differs slightly from a rocket that + responds to the total angle of attack (under a tenth of a percent at 3 + degrees in each plane). For data against the total angle of attack, use a + :class:`rocketpy.GenericSurface` (see :ref:`totalangle`). + .. _generic_surface_interpolation: @@ -634,13 +1029,12 @@ A practical rule of thumb: use ``"linear"`` against Mach (transonic kinks) and about smooth derivatives. .. note:: - Multi-dimensional CSV tables that form a strict Cartesian grid are read with - a :class:`scipy.interpolate.RegularGridInterpolator`. The ``interpolation`` - argument still applies: it is mapped onto the interpolator's method, with - ``"spline"`` becoming ``"cubic"`` and ``"akima"`` becoming the - shape-preserving ``"pchip"`` (``"linear"`` stays linear). Smooth methods need - enough samples per axis (``"cubic"`` needs at least 4), otherwise SciPy - raises. + A table over two or more variables that holds every combination of their + values (in a file, a list or an array) is interpolated on that regular + grid. The ``interpolation`` argument still applies: ``"spline"`` becomes the + grid method ``"cubic"`` and ``"akima"`` the shape-preserving ``"pchip"`` + (``"linear"`` stays linear). The smooth methods need at least 4 values of + each variable; with fewer, ``"linear"`` is used and a warning says so. Choosing an extrapolation method ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ @@ -706,6 +1100,13 @@ This is also how a full-vehicle model captures the powered/coasting drag difference: build one ``"power_on"`` and one ``"power_off"`` surface and add them together (see :ref:`fullbodyaerodynamics`). +The flight switches such surfaces on and off by time. The stability analysis +(``aerodynamic_center``, the margins, the dynamic stability numbers) describes +one phase at a time: the coasting rocket by default, or the powered one after +``rocket.stability_phase = "power_on"``. A warning says so whenever a surface is +left out. :meth:`rocketpy.Rocket.to_coefficients` lumps each phase with its own +surfaces regardless of that setting. + .. _fullbodyaerodynamics: @@ -751,13 +1152,34 @@ You can also collapses an assembled rocket into a single stability-derivative model about its center of dry mass: - :meth:`rocketpy.Rocket.to_coefficients` returns the coefficient curves as a - dict split into ``"power_off"`` and ``"power_on"`` sets (only the drag differs - between them), each mapping a coefficient name to a :class:`rocketpy.Function` - of Mach. + dict split into ``"power_off"`` and ``"power_on"`` sets, each mapping a + coefficient name to a :class:`rocketpy.Function` of Mach. Every one of the 36 + derivatives of the linear model that is not zero is kept, so canted fins keep + their roll forcing ``cl_0`` and a rocket that is not axisymmetric keeps the + terms that couple its pitch and yaw planes. - :meth:`rocketpy.Rocket.to_surface` wraps those into a ready-to-use pair of :class:`rocketpy.LinearGenericSurface` objects, one gated to each motor phase -- the inverse of :meth:`~rocketpy.Rocket.add_full_body_aerodynamics`. +.. note:: + The slopes a generic surface reports for the stability analysis (its + ``cN_alpha``, ``cm_alpha``, ``cY_beta``, ``cn_beta`` and the center of + pressure built from them) are taken at zero angle of attack and sideslip, + zero rotation rates, Reynolds number 0 and, for a controllable surface, + zero control. A table that changes with the Reynolds number is therefore + linearized at its low-Reynolds edge; the flight itself always reads the + table at the actual Reynolds number. + +Both take ``model="table"`` to keep the curves instead of the slopes: the six +coefficients are then read on a grid of angle of attack, sideslip and Mach +(``angles`` and ``machs`` set the grid, by default every 2 degrees up to 30 and +every 0.05 up to Mach 3) and the surfaces are :class:`rocketpy.GenericSurface` +objects. A rocket that behaves the same in every plane is swept over the total +angle of attack only, which is exact for the built-in surfaces at any angle. +The damping is carried by the same rate terms as the linear model, read at +zero angle; pass ``rates=False`` to leave it out and get the rocket as a wind +tunnel sees it, held still. + .. code-block:: python coefficients = rocket.to_coefficients() # {"power_off": {...}, "power_on": {...}} @@ -767,10 +1189,44 @@ stability-derivative model about its center of dry mass: bare.add_full_body_aerodynamics(surfaces, overwrite=True) .. important:: - The extracted model is a **linear summary tabulated only against Mach**: the - derivatives are taken at zero angle of attack, zero sideslip and zero rates, - so incidence/rate nonlinearity, Reynolds dependence and control-surface - dependence are dropped. These are exactly the assumptions of the built-in - Barrowman surfaces (nose cones, fins, and tails), so a rocket built only from - those is reproduced exactly. + By default the extracted model is a **linear summary tabulated only against + Mach**: the derivatives are taken at zero angle of attack, zero sideslip and + zero rates, at zero Reynolds number and with every control held where it is. + These are exactly the assumptions of the built-in Barrowman surfaces (nose + cones, fins, and tails), so a rocket built only from those is reproduced + exactly. See :ref:`aero_cp_stability` for the extraction math and its limitations. + +For a rocket carrying a generic surface that depends on more than that, three +optional arguments of both methods keep the dependence: + +.. code-block:: python + + import numpy as np + + coefficients = rocket.to_coefficients( + model="table", + # read the coefficients at these Reynolds numbers (based on the + # rocket's diameter); they gain "reynolds" as an input + reynolds=[1e5, 1e6, 1e7], + # keep a control of a ControllableGenericSurface as an input + controls={"deflection": np.radians([-10, 0, 10])}, + # read the damping at every angle of the table, not only at zero + rates="at_each_angle", + ) + cN = coefficients["power_off"]["cN"] + cN(0.05, 0.0, 0.6, 1e6, 0.1) # alpha, beta, mach, reynolds, deflection + +- ``reynolds`` also takes a single number, which sets the Reynolds number the + coefficients are read at without adding an input. It works with both models. +- ``controls`` works with both models in ``to_coefficients``. In + ``to_surface`` it needs ``model="table"``, and the surfaces returned are + :class:`rocketpy.ControllableGenericSurface` objects with those controls. + When two surfaces of the rocket use the same control name, each is kept as + its own input, named ``_``. +- ``rates="at_each_angle"`` needs ``model="table"``. + +Each value listed multiplies the number of points computed, and +``controls`` and ``rates="at_each_angle"`` make an axisymmetric rocket be +swept over both angles, so keep the lists short and pass a coarser ``angles`` +when it takes too long. diff --git a/docs/user/rocket/rocket_usage.rst b/docs/user/rocket/rocket_usage.rst index e934bbe43..970408393 100644 --- a/docs/user/rocket/rocket_usage.rst +++ b/docs/user/rocket/rocket_usage.rst @@ -72,18 +72,33 @@ gases, the drag coefficient is lower than when the motor is off. If you do not have a drag curve for when the motor is on, you can use the same drag curve for both cases. -These curves are used to calculate the drag coefficient of the rocket at any -given time. +These curves give the rocket's drag coefficient when it flies straight into +the air, with zero angle of attack. When the rocket flies at an angle to the +air, the simulation applies this drag along the rocket's centerline and scales +it by the cosine of the angle of attack. The force therefore fades to zero when +the rocket is sideways to the air and brakes the rocket when it moves tail +first. The extra drag at an angle comes from the forces on the nose cone, fins +and tail, which push the rocket sideways and partly against the air. In a 3-DOF +simulation, which does not model the rocket's attitude, the drag acts against +the velocity instead. -The drag curves can be defined in two ways: +.. note:: + The same scaling applies to a drag coefficient that changes with the angle + of attack: the value you give at each angle is multiplied by the cosine of + that angle and applied along the centerline. + +A drag curve can be given as: -1. Passing in the path to the drag curve CSV file as a string; -2. Passing in a function that returns the drag coefficient given the Mach - number. +1. a number, for a constant drag coefficient; +2. the path to a CSV file as a string; +3. a list of points, ``[[mach, cd], ...]``; +4. a function that returns the drag coefficient given the Mach number, such + as ``lambda mach: ...``; +5. a :class:`rocketpy.Function`. -Curves defined in CSV files must have the first column as the Mach number -and the second column as the drag coefficient. -Here is an example of a drag curve file: +CSV files and lists of points must have the Mach number in the first column +and the drag coefficient in the second. Here is an example of a drag curve +file: .. code-block:: @@ -99,6 +114,26 @@ Here is an example of a drag curve file: 0.9, 0.45696342 1.0, 0.62744566 +.. note:: + A drag curve may also depend on more than the Mach number. A function can + take any of ``alpha``, ``beta``, ``mach``, ``reynolds``, ``pitch_rate``, + ``yaw_rate`` and ``roll_rate`` as arguments (for example + ``lambda alpha, mach: ...``), and a CSV file can have a header naming its + columns after them, with the drag coefficient in the last column. The angles + are in radians, the Reynolds number is based on the rocket's diameter, and + the three rates are non-dimensional: the rotation rate in rad/s times the + rocket's diameter, divided by twice the airspeed. These are the same + variables used by :ref:`generic surfaces `. + + For a drag that grows with the angle between the rocket and the air, whatever + side the wind comes from, use the total angle of attack ``alpha_total`` (or + ``alpha_total_deg``), for example ``lambda alpha_total, mach: ...``. ``alpha`` + alone is the angle in one plane only and misses a sideslip. + + If what you have are the coefficients of the whole rocket (such as lift, + drag and pitch moment against angle of attack), use + :meth:`rocketpy.Rocket.add_full_body_aerodynamics` instead. + .. tip:: Getting a drag curve can be a challenging task. To get really accurate drag curves, you can use CFD software or wind tunnel data. @@ -181,9 +216,15 @@ With the motor defined, you can add it to the rocket: 3. Adding Aerodynamic Surfaces ------------------------------ -The third step is to add aerodynamic surfaces (i.e. nose cone, fins and tail) -to the rocket. These surfaces are used to calculate the rocket's aerodynamic -forces and moments. +The third step is to add aerodynamic surfaces to the rocket. These surfaces are +used to calculate the rocket's aerodynamic forces and moments. They can be the +rocket's parts, described by their geometry (nose cone, fins and tail, whose +coefficients RocketPy computes), or surfaces described directly by their +aerodynamic coefficients: a :class:`rocketpy.GenericSurface` (coefficient +tables or functions) or a :class:`rocketpy.LinearGenericSurface` (coefficient +slopes). Coefficients for the whole rocket, for example from a wind tunnel or +another program, go in through :meth:`rocketpy.Rocket.add_full_body_aerodynamics`; +see :ref:`genericsurfaces` for the details. Differently from the motor, the aerodynamic surfaces do not need to be defined before being added to the rocket. They can be defined and added @@ -218,10 +259,13 @@ to the rocket in one step: For more information on adding aerodynamic surfaces, see: - - :class:`rocketpy.Rocket.add_nose` - - :class:`rocketpy.Rocket.add_trapezoidal_fins` - - :class:`rocketpy.Rocket.add_elliptical_fins` - - :class:`rocketpy.Rocket.add_tail` + - :meth:`rocketpy.Rocket.add_nose` + - :meth:`rocketpy.Rocket.add_trapezoidal_fins` + - :meth:`rocketpy.Rocket.add_elliptical_fins` + - :meth:`rocketpy.Rocket.add_free_form_fins` + - :meth:`rocketpy.Rocket.add_tail` + - :meth:`rocketpy.Rocket.add_surfaces` (any surface, including generic ones) + - :meth:`rocketpy.Rocket.add_full_body_aerodynamics` Now we can see a representation of the rocket, this will guarantee that the rocket has been constructed correctly: @@ -493,6 +537,10 @@ First, lets guarantee that the rocket is stable, by plotting the static margin: If it is unreasonably **high**, your rocket is **super stable** and the simulation will most likely **fail**. +The stability margin at a given Mach number and time is read from +``calisto.stability_margin(mach, time)``. It is the margin with the rocket +flying straight into the air, at zero angle of attack. + The lets check all the information available about the rocket: .. jupyter-execute:: From f315df7c96805176dca2f6d9b3496bf4687f625b Mon Sep 17 00:00:00 2001 From: MateusStano Date: Sun, 4 Oct 2026 20:33:58 -0300 Subject: [PATCH 15/22] ENH: rate derivatives on GenericSurface A GenericSurface takes a rate derivative such as cm_q next to a coefficient, to add damping to tabulated data: it is multiplied by its reduced rate and added to the coefficient. Documented in the generic surface guide. --- docs/user/rocket/generic_surface.rst | 64 ++++++++++++++ .../rocket/aero_surface/generic_surface.py | 88 +++++++++++++------ .../aero_surface/test_generic_surfaces.py | 72 +++++++++++++++ 3 files changed, 195 insertions(+), 29 deletions(-) diff --git a/docs/user/rocket/generic_surface.rst b/docs/user/rocket/generic_surface.rst index 7e22ea9e4..808c02348 100644 --- a/docs/user/rocket/generic_surface.rst +++ b/docs/user/rocket/generic_surface.rst @@ -305,6 +305,9 @@ are converted using the relations in `Relating the two frames`_ above. Only one coefficient is required, and any combination can be provided; the ones you omit are treated as zero. +Damping can be added to any of them with a rate derivative such as ``cm_q`` +(see :ref:`generic_surface_damping`). + .. _coefficient_variables: Each coefficient is a function of the same seven independent variables. When @@ -666,6 +669,67 @@ A file written by another program has its own column names. Translate them with surface is placed at, positive toward the nose, the moment coefficient about that point is :math:`C_m = C_N \, x_{cp} / L_{ref}`. +.. _generic_surface_damping: + +Adding damping to tabulated coefficients +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Aerodynamic data often comes in two parts: the coefficients as tables against +the angle of attack and the Mach number, and the damping as a few separate +numbers, such as the pitch damping :math:`C_{m_q}`. To use both, give the +damping as a **rate derivative** next to the coefficient it belongs to: + +.. code-block:: python + + surface = GenericSurface( + reference_area=rocket.area, + reference_length=2 * rocket.radius, + coefficients={ + "cN": "cN.csv", # header: alpha_deg, mach, cN + "cm": "cm.csv", # header: alpha_deg, mach, cm + "cm_q": -800, # pitch damping + "cn_r": -800, # yaw damping + "cl_p": "cl_p_vs_mach.csv" # roll damping against Mach + }, + ) + +The name of a rate derivative is the name of the coefficient followed by the +rate it multiplies: + +.. list-table:: + :header-rows: 1 + :widths: 22 78 + + * - Suffix + - Rate + * - ``_p`` + - Roll rate, as in ``cl_p``. + * - ``_q`` + - Pitch rate, as in ``cm_q`` and ``cN_q``. + * - ``_r`` + - Yaw rate, as in ``cn_r`` and ``cY_r``. + +Each derivative is multiplied by its reduced rate and added to the coefficient, +for example :math:`C_m = C_m(\alpha, Ma) + C_{m_q}\, q^{*}`. Some rules: + +- A derivative that opposes the motion, as damping does, is a **negative** + number. +- The rates are the reduced rates (see the note in `Coefficients`_), so the + derivatives are per unit of :math:`q L_{ref} / (2V)`, not per rad/s. +- A derivative can be a number or depend on any variable, most often the Mach + number. A table with one unnamed column is read against Mach. +- The derivatives are in the body frame: ``cN_q``, ``cY_r`` and ``cA_q`` exist, + ``cL_q`` does not. The moment derivatives are the same in both frames, so + ``cm_q`` can be used with ``cL`` and ``cD``. +- Each plane takes its own derivative. For a rocket that behaves the same in + every plane, give ``cn_r`` equal to ``cm_q`` (and ``cY_r`` equal to + ``cN_q``). This also holds for data against ``alpha_total``. +- A coefficient that already depends on a rate, through a ``pitch_rate`` column + for example, cannot take the derivative for that rate too. + +Only the rate derivatives are accepted. A slope against the angle, such as +``cm_alpha``, belongs to a :class:`rocketpy.LinearGenericSurface`. + Adding the surface to the rocket ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ diff --git a/rocketpy/rocket/aero_surface/generic_surface.py b/rocketpy/rocket/aero_surface/generic_surface.py index 29771c953..b350e418c 100644 --- a/rocketpy/rocket/aero_surface/generic_surface.py +++ b/rocketpy/rocket/aero_surface/generic_surface.py @@ -139,6 +139,8 @@ class GenericSurface: # Force-coefficient names in each frame. Moments (cm/cn/cl) are frame-shared. _WIND_FORCE_NAMES = ("cL", "cQ", "cD") _BODY_FORCE_NAMES = ("cN", "cY", "cA") + # Rate derivatives, such as ``cm_q``: the reduced rate each suffix multiplies + _RATE_DERIVATIVES = {"p": "roll_rate", "q": "pitch_rate", "r": "yaw_rate"} def __init__( self, @@ -171,32 +173,17 @@ def __init__( coefficients : dict The force and moment coefficients, by name. Any you leave out are 0. - - ``cN``: normal force coefficient (body frame). - - ``cY``: side force coefficient (body frame). - - ``cA``: axial force coefficient (body frame). - - ``cm``: pitch moment coefficient. - - ``cn``: yaw moment coefficient. - - ``cl``: roll moment coefficient. - - The wind-frame ``cL`` (lift), ``cQ`` (side force) and ``cD`` (drag) - can be given instead of ``cN``, ``cY`` and ``cA`` (see - ``force_convention``). The moments are taken about - ``center_of_pressure``. - - Most wind-tunnel reports and aerodynamics programs give the data - against the total angle of attack. Name the variable - ``alpha_total`` and that is all, for example - ``lambda alpha_total, mach: ...``. A normal force ``cN``, a lift - ``cL`` or a pitch moment ``cm`` given that way acts in the plane - that holds the rocket's axis and the wind, and is split between the - pitch and yaw planes for you. Three rules apply to it: - - - It must be zero at zero total angle, where the air has no - direction across the rocket, so a table must start at 0 degrees. - - Leave out ``cY``, ``cQ`` and ``cn``: the part in the other plane - comes from the split. - - If it also depends on ``phi``, it is used as given: write the - split yourself (``cN = f * sin(phi)``, ``cY = -f * cos(phi)``). + - ``cN``, ``cY``, ``cA``: normal, side and axial force coefficients, + in the body frame. The wind-frame ``cL`` (lift), ``cQ`` (side + force) and ``cD`` (drag) can be given instead (see + ``force_convention``). + - ``cm``, ``cn``, ``cl``: pitch, yaw and roll moment coefficients, + taken about ``center_of_pressure``. + - A rate derivative, to add damping: a body-frame coefficient's name + followed by ``_p``, ``_q`` or ``_r`` (roll, pitch or yaw rate), + such as ``cm_q``. It is multiplied by the reduced rate and added + to the coefficient, so damping is a negative number (see + :ref:`generic_surface_damping`). Each coefficient can depend on these variables: @@ -205,6 +192,9 @@ def __init__( - ``alpha_total``, ``phi``: total angle of attack (the angle between the rocket's axis and the air) and roll angle of the wind, in radians, or ``alpha_total_deg``, ``phi_deg`` in degrees. + A ``cN``, ``cL`` or ``cm`` given against ``alpha_total`` alone is + split between the pitch and yaw planes for you, and must be zero + at zero angle (see :ref:`totalangle`). - ``mach``: Mach number. - ``reynolds``: Reynolds number (see ``reynolds_length``). - ``pitch_rate``, ``yaw_rate``, ``roll_rate``: angular rates in @@ -831,7 +821,10 @@ def _build_coefficients( ) # Kept as given, so saving the surface needs no pickling self._input_coefficients = { - name: self._as_coefficient(value, name) + # A rate derivative given as an unnamed curve is against Mach + name: self._as_coefficient( + value, name, "mach" if self._rate_of(name) else None + ) for name, value in coefficients.items() } # Input in the plane of the wind, then in the wind frame, becomes @@ -853,11 +846,48 @@ def _build_coefficients( if self._xcp is not None: self._carry_moments_to_center_of_pressure() + def _rate_of(self, name): + """The reduced rate a rate derivative such as ``cm_q`` multiplies, or + ``None`` when ``name`` is not a rate derivative.""" + coefficient, _, suffix = name.partition("_") + if coefficient in GenericSurface._get_default_coefficients(): + return self._RATE_DERIVATIVES.get(suffix) + return None + def _complete_body_coefficients(self, coefficients): - """Last step before the body-frame coefficients are stored. Nothing to - do here; the linear surface fills in its yaw plane.""" + """Last step before the body-frame coefficients are stored: add each + rate derivative to its coefficient, ``cm + cm_q * pitch_rate``. (The + linear surface keeps its derivatives and fills in its yaw plane.)""" + coefficients = dict(coefficients) + for name in [name for name in coefficients if self._rate_of(name)]: + target, rate = name.partition("_")[0], self._rate_of(name) + derivative = coefficients.pop(name) + base = self._as_coefficient(coefficients.get(target, 0), target) + if rate in base.depends_on: + raise ValueError( + f"{target} already depends on {rate}, so {name} cannot be " + "given too. Give the rate dependence in one place." + ) + used = [ + var + for var in self.independent_vars + if var == rate or var in base.depends_on or var in derivative.depends_on + ] + coefficients[target] = self._with_rate_term(base, derivative, rate, used) return coefficients + @staticmethod + def _with_rate_term(base, derivative, rate, used): + """``base + derivative * rate`` as a Function of the variables + ``used``.""" + read_base, read_derivative = base.evaluator(used), derivative.evaluator(used) + at = used.index(rate) + return _as_function( + lambda *args: read_base(*args) + read_derivative(*args) * args[at], + used, + base.name, + ) + def _carry_moments_to_center_of_pressure(self): """With a center of pressure that varies with Mach the force is applied at the surface's ``z = 0``, so its moment about the center of pressure diff --git a/tests/unit/rocket/aero_surface/test_generic_surfaces.py b/tests/unit/rocket/aero_surface/test_generic_surfaces.py index b44560173..05b626232 100644 --- a/tests/unit/rocket/aero_surface/test_generic_surfaces.py +++ b/tests/unit/rocket/aero_surface/test_generic_surfaces.py @@ -1381,3 +1381,75 @@ def test_roll_angle_of_the_wind_is_zero_flying_exactly_tail_first(): from rocketpy.rocket.aero_surface._helpers import total_angle_and_roll assert total_angle_and_roll(np.pi, np.pi) == pytest.approx((np.pi, 0.0)) + + +def test_rate_derivatives_add_damping_to_a_table(): + """A rate derivative given next to a coefficient is multiplied by its + reduced rate and added to it: the same result as a linear surface with the + same slopes, also after saving and loading.""" + table = [ + [alpha, mach, -3 * alpha * (1 + 0.1 * mach)] + for alpha in np.linspace(-0.3, 0.3, 7) + for mach in (0.0, 1.0) + ] + surface = GenericSurface( + 1.0, + 1.0, + { + "cm": (table, ["alpha", "mach"]), + "cm_q": -800, + "cN": lambda alpha: 2 * alpha, + "cN_q": [[0, 40], [1, 50]], # an unnamed curve is against Mach + "cl_p": -9, + }, + ) + linear = LinearGenericSurface( + 1.0, + 1.0, + {"cm_alpha": -3.15, "cm_q": -800, "cN_alpha": 2, "cN_q": 45, "cl_p": -9}, + ) + state = (0.1, 0.0, 0.5, 0, 0.01, 0.0, 0.002) + for name in ("cN", "cm", "cl"): + assert getattr(surface, name)(*state) == pytest.approx( + getattr(linear, name)(*state), abs=1e-12 + ) + # Without rotation the table is read as it is, and so is its slope + assert surface.cm(0.1, 0, 0.5, 0, 0, 0, 0) == pytest.approx(-0.315) + assert surface.cm_alpha(0, 0, 0.5, 0, 0, 0, 0) == pytest.approx(-3.15) + assert surface.cm.depends_on == ("alpha", "mach", "pitch_rate") + + loaded = json.loads(json.dumps(surface, cls=RocketPyEncoder), cls=RocketPyDecoder) + assert "cm_q" in loaded.to_dict()["coefficients"] + assert loaded.cm(*state) == surface.cm(*state) + + +def test_rate_derivatives_work_with_the_other_input_forms(): + """Damping can be added to wind-frame coefficients and to data against the + total angle of attack, where each plane takes its own derivative.""" + wind = GenericSurface( + 1.0, 1.0, {"cL": lambda alpha: 2 * alpha, "cD": 0.4, "cm_q": -800} + ) + assert wind.force_convention == "wind" + assert wind.cm(0, 0, 0.5, 0, 0.01, 0, 0) == pytest.approx(-8.0) + + total = GenericSurface( + 1.0, + 1.0, + {"cm": lambda alpha_total: -20 * alpha_total, "cm_q": -800, "cn_r": -800}, + ) + assert total.cm(0.1, 0, 0, 0, 0.01, 0, 0) == pytest.approx(-2.0 - 8.0, rel=1e-3) + assert total.cn(0, 0.1, 0, 0, 0, 0.01, 0) == pytest.approx(2.0 - 8.0, rel=1e-3) + + +def test_rate_derivative_names_are_checked(): + """Only the rate derivatives of the body-frame coefficients are taken, and a + coefficient gets its rate dependence from one place.""" + with pytest.raises(ValueError, match="cm already depends on pitch_rate"): + GenericSurface( + 1.0, + 1.0, + {"cm": lambda alpha, pitch_rate: -3 * alpha - 5 * pitch_rate, "cm_q": -8}, + ) + for name in ("cm_alpha", "cN_0", "cL_q"): + with pytest.raises(ValueError, match="Invalid coefficient name"): + GenericSurface(1.0, 1.0, {name: 1.0}) From 841ad04d1543c999ed3a7e3d81c45f40b15744ba Mon Sep 17 00:00:00 2001 From: MateusStano Date: Sun, 4 Oct 2026 20:33:58 -0300 Subject: [PATCH 16/22] DOC: remove a broken link from the stability guide --- docs/user/center_of_pressure_and_stability.rst | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/docs/user/center_of_pressure_and_stability.rst b/docs/user/center_of_pressure_and_stability.rst index d1efeff35..42441236a 100644 --- a/docs/user/center_of_pressure_and_stability.rst +++ b/docs/user/center_of_pressure_and_stability.rst @@ -268,9 +268,7 @@ within a target range also does not by itself guarantee good flight behavior. turns into the wind and drifts further downwind ("weathercocking"). Aim for enough margin to keep the rocket reliably stable, rather than the largest margin achievable. The flight studies in :ref:`stability_in_flight` examine - how much this actually affects a real flight, and the - :ref:`practical studies ` show that the altitude usually - blamed on over-stability is really the cost of the added nose weight. + how much this actually affects a real flight. .. _percent_of_length: From a6fb24f316355737ae5b2795983ce78c7c5ccf5b Mon Sep 17 00:00:00 2001 From: MateusStano Date: Mon, 5 Oct 2026 19:28:44 -0300 Subject: [PATCH 17/22] ENH: switch aerodynamic surfaces on and off with event commands A surface's `active_during` now accepts only "always", "power_on" and "power_off". The function form `active_during(t, flight)` is removed: to switch a surface at any other moment, an event calls one of two new commands from its callback, context.event.commands.activate_surface(surface) context.event.commands.deactivate_surface(surface) and the solver restarts at the switch. A surface that only appears later in the flight is built with the new `active=False` argument, so that it starts each flight switched off. The flight stores each switch with its time, so `surface.is_active(t, flight)` is still a plain question about time and the outputs computed after the flight see the surfaces that were on at each moment. The surfaces themselves are not modified, so another flight of the same rocket starts from their own `active` setting. The stability analysis leaves out a surface built with `active=False`. --- docs/user/event_usage.rst | 27 ++++ docs/user/rocket/generic_surface.rst | 63 ++++++--- rocketpy/rocket/_helpers.py | 6 +- .../controllable_generic_surface.py | 27 ++-- .../rocket/aero_surface/generic_surface.py | 116 +++++++++-------- .../aero_surface/linear_generic_surface.py | 28 ++-- rocketpy/rocket/rocket.py | 15 ++- rocketpy/simulation/events/commands.py | 64 ++++++++- rocketpy/simulation/events/event.py | 4 +- rocketpy/simulation/events/event_execution.py | 31 +++++ rocketpy/simulation/flight.py | 3 + tests/integration/simulation/test_event.py | 74 ++++++++++- .../test_controllable_generic_surface.py | 27 ++-- .../aero_surface/test_generic_surfaces.py | 78 +++++------ tests/unit/rocket/test_stability_rework.py | 15 +++ .../simulation/test_aerodynamic_drag_force.py | 6 +- .../simulation/test_surface_activation.py | 121 ++++++++++++++++++ 17 files changed, 540 insertions(+), 165 deletions(-) create mode 100644 tests/unit/simulation/test_surface_activation.py diff --git a/docs/user/event_usage.rst b/docs/user/event_usage.rst index 29ba0ef5f..27e9c5235 100644 --- a/docs/user/event_usage.rst +++ b/docs/user/event_usage.rst @@ -780,6 +780,7 @@ Available commands include: - ``event.commands.set_dynamics(dynamics, **phase_kwargs)``: Fly the rest of the flight with a different set of equations of motion. - ``event.commands.start_flight_phase(phase_name=None, lag=0)``: Start a new flight phase. - ``event.commands.terminate_flight()``: Request to end the flight simulation immediately after the current step. +- ``event.commands.activate_surface(surface) / deactivate_surface(surface)``: Make an aerodynamic surface start or stop producing force. **event.commands.disable() / enable()** Disable or re-enable the event that is currently running. This is useful for @@ -1072,6 +1073,32 @@ Available commands include: print(f"Trigger time: {log['trigger_time']:.4f} s") print(f"Flight time reported by callback: {log['flight_time']:.4f} s") +**event.commands.activate_surface(surface) / deactivate_surface(surface)** + Make an aerodynamic surface start or stop producing force from this moment + on. Use it for a part of the rocket that appears or disappears during the + flight. The surface must already be one of the rocket's aerodynamic surfaces. A surface that should + only appear later is built with ``active=False``, so that it starts the + flight switched off. + + The switch lasts for the rest of this flight only. The surface itself is not + modified, so another flight of the same rocket starts from its ``active`` + setting again. + + .. code-block:: python + + def switch_off(context): + context.event.commands.deactivate_surface(my_surface) + + switch_event = Event( + callback=switch_off, + trigger=lambda context: context.state.vz < 0, # past apogee + trigger_only_once=True, + name="Switch surface off", + ) + + .. seealso:: + See :ref:`active_during` for a complete example. + See also -------- diff --git a/docs/user/rocket/generic_surface.rst b/docs/user/rocket/generic_surface.rst index 808c02348..c4e147111 100644 --- a/docs/user/rocket/generic_surface.rst +++ b/docs/user/rocket/generic_surface.rst @@ -238,6 +238,7 @@ it a reference area and length, the coefficients, and a few optional settings: extrapolation="constant", force_convention="body", active_during="always", + active=True, ) Constructor parameters @@ -272,8 +273,11 @@ Constructor parameters - ``force_convention`` (str, optional): the frame the force coefficients are given in, ``"body"`` or ``"wind"``. Default ``None`` (inferred from the coefficient names). -- ``active_during`` (str or callable, optional): when the surface produces - aerodynamic force during the flight. Default ``"always"``. See +- ``active_during`` (str, optional): the motor phase the surface produces + aerodynamic force in: ``"always"``, ``"power_on"`` or ``"power_off"``. + Default ``"always"``. See :ref:`active_during`. +- ``active`` (bool, optional): whether the surface starts the flight switched + on. Default ``True``. An event can switch it on or off during the flight. See :ref:`active_during`. Coefficients @@ -1137,18 +1141,19 @@ an extreme condition beyond your data. Activation Window ----------------- -By default a surface produces aerodynamic force throughout the flight. The -``active_during`` argument restricts it to part of the flight. This is useful -for a surface that only exists (or only matters) during a phase. It is accepted -by both :class:`rocketpy.GenericSurface` and -:class:`rocketpy.LinearGenericSurface`, and accepts: +By default a surface produces aerodynamic force throughout the flight. There +are two ways to restrict it to part of the flight. Both are accepted by +:class:`rocketpy.GenericSurface`, :class:`rocketpy.LinearGenericSurface` and +:class:`rocketpy.ControllableGenericSurface`. + +By motor phase +~~~~~~~~~~~~~~ + +The ``active_during`` argument ties the surface to the motor burn. It accepts: - ``"always"`` (default): the surface always contributes force. - ``"power_on"``: only while the motor is burning (up to burnout). - ``"power_off"``: only after the motor has burned out. -- a callable ``active_during(t, flight)`` returning ``True`` when the surface is - active at time ``t`` (in seconds) of the given :class:`rocketpy.Flight`, for - any custom window. .. code-block:: python @@ -1164,12 +1169,38 @@ This is also how a full-vehicle model captures the powered/coasting drag difference: build one ``"power_on"`` and one ``"power_off"`` surface and add them together (see :ref:`fullbodyaerodynamics`). -The flight switches such surfaces on and off by time. The stability analysis -(``aerodynamic_center``, the margins, the dynamic stability numbers) describes -one phase at a time: the coasting rocket by default, or the powered one after -``rocket.stability_phase = "power_on"``. A warning says so whenever a surface is -left out. :meth:`rocketpy.Rocket.to_coefficients` lumps each phase with its own -surfaces regardless of that setting. +At any other moment, with an event +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +To switch a surface on or off at any other moment, such as apogee, a given +altitude or a sensor reading, use an :class:`rocketpy.Event`. Its callback +switches the surface with one of two commands: + +- ``context.event.commands.deactivate_surface(surface)``: the surface stops + producing force from that moment on. +- ``context.event.commands.activate_surface(surface)``: the surface starts + producing force from that moment on. + +For example, to switch a surface off at apogee: + +.. code-block:: python + + def switch_off(context): + context.event.commands.deactivate_surface(my_surface) + + switch_event = Event( + callback=switch_off, + trigger=lambda context: context.state.vz < 0, + trigger_only_once=True, + ) + + flight = Flight(..., custom_events=[switch_event]) + +A surface that only appears later in the flight is built with ``active=False``, +so that it starts the flight switched off, and an event switches it on with +``activate_surface``. + +See :ref:`eventusage` for how to write the trigger of an event. .. _fullbodyaerodynamics: diff --git a/rocketpy/rocket/_helpers.py b/rocketpy/rocket/_helpers.py index 46ee201cd..3e04b6bc0 100644 --- a/rocketpy/rocket/_helpers.py +++ b/rocketpy/rocket/_helpers.py @@ -28,9 +28,8 @@ def stability_surfaces(rocket, phase=None): during ``phase`` (``"power_on"`` or ``"power_off"``; the rocket's ``stability_phase`` when ``None``), as ``(surface, position)`` pairs. - A surface is left out only when its ``active_during`` names the other - phase. One with a custom activation window cannot be evaluated without - a flight, so it is kept. + A surface is left out when its ``active_during`` names the other phase, + or when it starts the flight switched off (``active=False``). """ phase = rocket.stability_phase if phase is None else phase if phase not in ("power_on", "power_off"): @@ -42,6 +41,7 @@ def stability_surfaces(rocket, phase=None): (surface, position) for surface, position in rocket.aerodynamic_surfaces if getattr(surface, "active_during", "always") != other + and getattr(surface, "active", True) ] diff --git a/rocketpy/rocket/aero_surface/controllable_generic_surface.py b/rocketpy/rocket/aero_surface/controllable_generic_surface.py index f76588435..5d7b6ef28 100644 --- a/rocketpy/rocket/aero_surface/controllable_generic_surface.py +++ b/rocketpy/rocket/aero_surface/controllable_generic_surface.py @@ -84,11 +84,10 @@ class ControllableGenericSurface(GenericSurface): y coordinate of ``cp``, in meters. ControllableGenericSurface.cpz : float z coordinate of ``cp``, in meters. - ControllableGenericSurface.active_during : str or callable - When the surface produces force, as given. - ControllableGenericSurface.is_active : callable - ``is_active(t, flight)``: whether the surface produces force at time - ``t`` of the flight. + ControllableGenericSurface.active_during : str + The motor phase the surface produces force in, as given. + ControllableGenericSurface.active : bool + Whether the surface starts each flight switched on. ControllableGenericSurface.force_convention : str Frame the force coefficients were given in: ``"body"`` or ``"wind"``. ControllableGenericSurface.independent_vars : list of str @@ -144,6 +143,7 @@ def __init__( interpolation=None, active_during="always", force_convention=None, + active=True, ): """Create an aerodynamic surface whose coefficients also depend on controls, such as a canard deflection. @@ -218,16 +218,22 @@ def __init__( or a dict keyed by coefficient name. ``None`` (the default) uses ``"linear"`` for tables built here and leaves a pre-built :class:`Function` unchanged. - active_during : str or callable, optional - When this surface produces force during a simulation: ``"always"`` + active_during : str, optional + The motor phase this surface produces force in: ``"always"`` (default), ``"power_on"`` (only while the motor burns, e.g. jet - vanes), ``"power_off"`` (only after burnout), or a function - ``active_during(t, flight)`` returning ``True`` when the surface is - active at time ``t`` (in seconds). + vanes) or ``"power_off"`` (only after burnout). To switch a surface + on or off at any other moment, use an event: see ``active`` below. force_convention : str, optional The frame the force coefficients are given in: ``"body"`` for ``cN``/``cY``/``cA`` or ``"wind"`` for ``cL``/``cQ``/``cD``. ``None`` (the default) works it out from the names. + active : bool, optional + Whether the surface starts each flight switched on. Default is + ``True``. Use ``False`` for a surface that only appears later in + the flight, and switch it on from an event with + ``context.event.commands.activate_surface(surface)``. A surface + that is on from the start is switched off the same way, with + ``deactivate_surface``. Raises ------ @@ -260,6 +266,7 @@ def __init__( interpolation=interpolation, force_convention=force_convention, active_during=active_during, + active=active, ) # ``self.prints``/``self.plots`` are the generic ones wired by the base. diff --git a/rocketpy/rocket/aero_surface/generic_surface.py b/rocketpy/rocket/aero_surface/generic_surface.py index b350e418c..b7ce9af7d 100644 --- a/rocketpy/rocket/aero_surface/generic_surface.py +++ b/rocketpy/rocket/aero_surface/generic_surface.py @@ -19,7 +19,6 @@ AeroCoefficient, build_independent_vars, ) -from rocketpy.tools import from_hex_decode, to_hex_encode class GenericSurface: @@ -54,11 +53,10 @@ class GenericSurface: y coordinate of ``cp``, in meters. GenericSurface.cpz : float z coordinate of ``cp``, in meters. - GenericSurface.active_during : str or callable - When the surface produces force, as given. - GenericSurface.is_active : callable - ``is_active(t, flight)``: whether the surface produces force at time - ``t`` of the flight. + GenericSurface.active_during : str + The motor phase the surface produces force in, as given. + GenericSurface.active : bool + Whether the surface starts each flight switched on. GenericSurface.force_convention : str Frame the force coefficients were given in: ``"body"`` or ``"wind"``. GenericSurface.independent_vars : list of str @@ -154,6 +152,7 @@ def __init__( extrapolation=None, force_convention=None, active_during="always", + active=True, ): """Create an aerodynamic surface from its aerodynamic coefficients. @@ -279,19 +278,26 @@ def __init__( default) picks the frame from the coefficient names. Whichever frame you use, the body-frame and wind-frame coefficients are all available as attributes afterwards. - active_during : str or callable, optional - When this surface produces aerodynamic force during a simulation. - Use it to model a surface that is only present in part of the flight, - such as jet vanes that only work while the motor burns, or a base - drag that only appears after burnout. Accepts: + active_during : str, optional + The motor phase this surface produces aerodynamic force in. Use it + to model a surface that only matters in part of the flight, such as + jet vanes that only work while the motor burns, or a base drag that + only appears after burnout. Accepts: - ``"always"`` (default): the surface always contributes force. - ``"power_on"``: only while the motor is burning (up to the motor's burn-out time). - ``"power_off"``: only after the motor has burned out. - - a function ``active_during(t, flight)`` returning ``True`` when - the surface is active at time ``t`` (in seconds) of the given - :class:`Flight`. Use this for any custom window. + + To switch a surface on or off at any other moment, such as apogee, + use an event: see ``active`` below. + active : bool, optional + Whether the surface starts each flight switched on. Default is + ``True``. Use ``False`` for a surface that only appears later in + the flight, and switch it on from an event with + ``context.event.commands.activate_surface(surface)``. A surface + that is on from the start is switched off the same way, with + ``deactivate_surface``. Raises ------ @@ -324,8 +330,8 @@ def __init__( ) self._set_center_of_pressure(center_of_pressure) self.name = name - self.active_during = active_during - self.is_active = self._activation_check(active_during) + self.active_during = self._validate_active_during(active_during) + self.active = bool(active) self._rotation_surface_to_body = self._default_surface_rotation() @@ -396,27 +402,43 @@ def _set_center_of_pressure(self, value): self.cpx, self.cpy, self.cpz = x, y, 0.0 if xcp else z self.cp = (self.cpx, self.cpy, self.cpz) - @staticmethod - def _activation_check(active_during): - """Turn an ``active_during`` policy into the ``is_active(t, flight)`` - function the flight calls every step to skip inactive surfaces. - - A callable is used as it is; ``"always"``, ``"power_on"`` and - ``"power_off"`` become small functions of the time ``t`` (s) and the - ``flight``. Anything else raises a ``ValueError``, so a typo is caught - when the surface is built instead of leaving it active. + def is_active(self, t, flight): + """Return whether the surface produces force at time ``t`` of a flight. + + Parameters + ---------- + t : float + Time in seconds. + flight : Flight + The flight being simulated. + + Returns + ------- + bool + ``False`` outside the motor phase given by ``active_during``. + Otherwise the last switch an event of this flight made up to ``t``, + or the surface's own ``active`` setting when there is none. """ - if callable(active_during): + if self.active_during != "always": + powered = t < flight.rocket.motor.burn_out_time + if powered != (self.active_during == "power_on"): + return False + for time, active in reversed(flight._surface_switches.get(self, ())): + if t >= time: + return active + return self.active + + @staticmethod + def _validate_active_during(active_during): + """Return ``active_during`` if it is one of the accepted values, so a + typo is caught when the surface is built instead of leaving it active.""" + if active_during in ("always", "power_on", "power_off"): return active_during - if active_during == "always": - return lambda t, flight: True - if active_during == "power_on": - return lambda t, flight: t < flight.rocket.motor.burn_out_time - if active_during == "power_off": - return lambda t, flight: t >= flight.rocket.motor.burn_out_time raise ValueError( - "`active_during` must be one of 'always', 'power_on', 'power_off' " - f"or a callable(t, flight) -> bool; got {active_during!r}." + "`active_during` must be one of 'always', 'power_on' or " + f"'power_off'; got {active_during!r}. To switch a surface on or off " + "at another moment of the flight, use an event with the " + "`activate_surface` and `deactivate_surface` commands." ) @property @@ -1164,13 +1186,6 @@ def from_csv( def _arguments_from_dict(cls, data): """The constructor arguments stored by :meth:`to_dict`. Subclasses extend it with their own arguments.""" - # A pickled function is restored, or "always" if that is not possible - active_during = data.get("active_during", "always") - if active_during not in ("always", "power_on", "power_off"): - try: - active_during = from_hex_decode(active_during) - except (TypeError, ValueError): - active_during = "always" arguments = { "reference_area": data["reference_area"], "reference_length": data["reference_length"], @@ -1179,7 +1194,8 @@ def _arguments_from_dict(cls, data): "name": data.get("name", "Generic Surface"), "reynolds_length": data.get("reynolds_length"), "force_convention": data.get("force_convention", "body"), - "active_during": active_during, + "active_during": data.get("active_during", "always"), + "active": data.get("active", True), } return arguments @@ -1196,23 +1212,14 @@ def to_dict(self, include_outputs=False, **kwargs): # pylint: disable=unused-ar Not used: a surface has no results to save. It is accepted so that every RocketPy object is saved the same way. Default False. **kwargs - ``allow_pickle`` (bool, default True): whether a custom - ``active_during`` function may be saved as pickled text. When it is - not allowed, the surface is saved as active ``"always"``. + Not used. Accepted so that every RocketPy object is saved the same + way. Returns ------- dict The arguments needed to rebuild the surface with :meth:`from_dict`. """ - # A function can only be saved by pickling it - active_during = self.active_during - if callable(active_during): - active_during = ( - to_hex_encode(active_during) - if kwargs.get("allow_pickle", True) - else "always" - ) x, y, z = self.center_of_pressure return { "reference_area": self.reference_area, @@ -1223,7 +1230,8 @@ def to_dict(self, include_outputs=False, **kwargs): # pylint: disable=unused-ar "center_of_pressure": (x, y, self._xcp or z), "name": self.name, "force_convention": self.force_convention, - "active_during": active_during, + "active_during": self.active_during, + "active": self.active, } @classmethod diff --git a/rocketpy/rocket/aero_surface/linear_generic_surface.py b/rocketpy/rocket/aero_surface/linear_generic_surface.py index 789bbdff6..f789ad350 100644 --- a/rocketpy/rocket/aero_surface/linear_generic_surface.py +++ b/rocketpy/rocket/aero_surface/linear_generic_surface.py @@ -72,11 +72,10 @@ class LinearGenericSurface(GenericSurface): y coordinate of ``cp``, in meters. LinearGenericSurface.cpz : float z coordinate of ``cp``, in meters. - LinearGenericSurface.active_during : str or callable - When the surface produces force, as given. - LinearGenericSurface.is_active : callable - ``is_active(t, flight)``: whether the surface produces force at time - ``t`` of the flight. + LinearGenericSurface.active_during : str + The motor phase the surface produces force in, as given. + LinearGenericSurface.active : bool + Whether the surface starts each flight switched on. LinearGenericSurface.force_convention : str Frame the force coefficients were given in: ``"body"`` or ``"wind"``. LinearGenericSurface.independent_vars : list of str @@ -108,6 +107,7 @@ def __init__( force_convention=None, active_during="always", axisymmetric=False, + active=True, ): """Create a linear aerodynamic surface from its coefficient derivatives. @@ -238,15 +238,15 @@ def __init__( ``cN_alpha = cL_alpha + cD_0``, ``cY_beta = cQ_beta - cD_0``, ``cA_alpha = cD_alpha - cL_0`` and ``cA_beta = cD_beta + cQ_0``. At zero angle this reduces to ``cN = cL``, ``cY = cQ``, ``cA = cD``. - active_during : str or callable, optional - When this surface produces aerodynamic force during a simulation: + active_during : str, optional + The motor phase this surface produces aerodynamic force in: - ``"always"`` (default): the surface always contributes force. - ``"power_on"``: only while the motor is burning. - ``"power_off"``: only after the motor has burned out. - - a function ``active_during(t, flight)`` returning ``True`` when - the surface is active at time ``t`` (in seconds) of the given - :class:`Flight`. + + To switch a surface on or off at any other moment, such as apogee, + use an event: see ``active`` below. axisymmetric : bool, optional Set it to ``True`` when the data describes a rocket (or a part) that behaves the same in every plane through its axis, such as a rocket @@ -265,6 +265,13 @@ def __init__( Default is ``False``: the two planes are used as given, and a plane without derivatives produces no force. + active : bool, optional + Whether the surface starts each flight switched on. Default is + ``True``. Use ``False`` for a surface that only appears later in + the flight, and switch it on from an event with + ``context.event.commands.activate_surface(surface)``. A surface + that is on from the start is switched off the same way, with + ``deactivate_surface``. Raises ------ @@ -296,6 +303,7 @@ def __init__( interpolation=interpolation, force_convention=force_convention, active_during=active_during, + active=active, ) self.compute_all_coefficients() diff --git a/rocketpy/rocket/rocket.py b/rocketpy/rocket/rocket.py index f48272c38..b97b69b00 100644 --- a/rocketpy/rocket/rocket.py +++ b/rocketpy/rocket/rocket.py @@ -1052,7 +1052,9 @@ def evaluate_center_of_pressure(self): A surface active during only one motor phase (its ``active_during``) is counted only when that phase is the rocket's ``stability_phase`` - (``"power_off"`` by default); a warning says so when one is left out. + (``"power_off"`` by default), and a surface that starts the flight + switched off (``active=False``) is not counted. A warning says so when + one is left out. Returns ------- @@ -1068,15 +1070,18 @@ def evaluate_center_of_pressure(self): self._total_side_coeff_der.set_source(lambda mach: 0) self._aerodynamic_center_yaw.set_source(lambda mach: 0) - # Surfaces active only in the other motor phase are left out. This - # method runs once per configuration, so the notice is shown once. + # Surfaces active only in the other motor phase, or switched off, are + # left out. This method runs once per configuration, so the notice is + # shown once. surfaces = stability_surfaces(self) if len(surfaces) != len(self.aerodynamic_surfaces): warnings.warn( "The aerodynamic center, the margins and the lumped coefficients " f"describe the rocket during '{self.stability_phase}': surfaces " - "active only in the other motor phase are left out. Set " - "`rocket.stability_phase` to 'power_on' or 'power_off' to choose.", + "active only in the other motor phase, or that start the flight " + "switched off (`active=False`), are left out. Set " + "`rocket.stability_phase` to 'power_on' or 'power_off' to choose " + "the phase.", stacklevel=2, ) diff --git a/rocketpy/simulation/events/commands.py b/rocketpy/simulation/events/commands.py index f5642f4e5..0629c5f85 100644 --- a/rocketpy/simulation/events/commands.py +++ b/rocketpy/simulation/events/commands.py @@ -21,6 +21,7 @@ def reset(self): self.new_flight_phase_lag = 0 self._terminate = False self.terminate_phase_name = None + self.surface_switches = [] def disable(self): self._disabled = True @@ -105,19 +106,72 @@ def start_flight_phase(self, phase_name=None, lag=0): def terminate_flight(self): self._terminate = True + def activate_surface(self, surface): + """Make an aerodynamic surface produce force from this moment on. + + Use it for a surface that only exists from some point of the flight. + Build that surface with ``active=False`` so that it starts the flight + switched off, add it to the rocket as usual, and call this from the + callback of an event:: + + def switch_on(context): + context.event.commands.activate_surface(my_surface) + + A surface with ``active_during="power_on"`` or ``"power_off"`` still + only produces force during that motor phase once it is switched on. + + Parameters + ---------- + surface : GenericSurface + The surface to switch on. It must already be one of the rocket's + aerodynamic surfaces. + + See Also + -------- + deactivate_surface : Switch a surface off. + """ + self.surface_switches.append((surface, True)) + + def deactivate_surface(self, surface): + """Make an aerodynamic surface stop producing force from this moment on. + + Use it for a surface that stops existing at some point of the flight. + Call it from the callback of an event:: + + def switch_off(context): + context.event.commands.deactivate_surface(my_surface) + + The change lasts for the rest of this flight only. The surface itself + is not modified, so another flight of the same rocket starts with it + switched on again. + + Parameters + ---------- + surface : GenericSurface + The surface to switch off. It must be one of the rocket's + aerodynamic surfaces. + + See Also + -------- + activate_surface : Switch a surface on. + """ + self.surface_switches.append((surface, False)) + @property def changes_trajectory(self): """Whether these commands change what happens at/after the trigger. Returns ``True`` when the queued commands start a new flight phase, - switch the equations of motion, or terminate the flight. In all three - cases the trajectory past the trigger is no longer valid, so during time - overshoot the simulation must be rolled back to the exact trigger - crossing before the commands are applied. Pure scheduling changes - (enabling/disabling or adding events) do not require a rollback. + switch the equations of motion, terminate the flight, or switch an + aerodynamic surface on or off. In all these cases the trajectory past + the trigger is no longer valid, so during time overshoot the simulation + must be rolled back to the exact trigger crossing before the commands + are applied. Pure scheduling changes (enabling/disabling or adding + events) do not require a rollback. """ return ( self.new_flight_phase is not None or self.new_dynamics is not None or self._terminate + or bool(self.surface_switches) ) diff --git a/rocketpy/simulation/events/event.py b/rocketpy/simulation/events/event.py index 9a4af37cc..e2e9dd3c8 100644 --- a/rocketpy/simulation/events/event.py +++ b/rocketpy/simulation/events/event.py @@ -164,8 +164,8 @@ def altitude(context): Set to ``True`` when the callback changes anything that affects the equations of motion. This includes changing an attribute of any simulation object, and using the ``set_dynamics``, - ``start_flight_phase``, or ``terminate_flight`` commands. Defaults to - ``False``. + ``start_flight_phase``, or ``terminate_flight`` commands. + Defaults to ``False``. name : str, optional Human-readable identifier used in logs and debugging. Defaults to ``"Custom Event"``. diff --git a/rocketpy/simulation/events/event_execution.py b/rocketpy/simulation/events/event_execution.py index cdbf241b2..706cd5d50 100644 --- a/rocketpy/simulation/events/event_execution.py +++ b/rocketpy/simulation/events/event_execution.py @@ -128,6 +128,9 @@ def apply_event_commands( t_apply = event_results.exact_time apply_exact_time_result(flight, event_results) + # Before a new phase is started, so its solver begins with these surfaces + apply_surface_switches(flight, event_results, time=t_apply) + apply_new_phase_or_dynamics( flight, event_results, phase, phase_index, node_index, time=t_apply ) @@ -145,6 +148,34 @@ def apply_event_commands( flight.rocket._refresh_aerodynamics() +def apply_surface_switches(flight, event_results, time): + """Record the surfaces an event switched on or off, with the time. + + Parameters + ---------- + flight : Flight + Flight instance being updated. + event_results : Commands + The commands the event queued. + time : float + The time the switches happen at, in seconds. + + Raises + ------ + ValueError + If a surface is not one of the rocket's aerodynamic surfaces. + """ + for surface, active in event_results.surface_switches: + if all(surface is not known for known, _ in flight.rocket.aerodynamic_surfaces): + raise ValueError( + f"Cannot switch the surface {getattr(surface, 'name', surface)!r} " + "on or off: it is not one of the rocket's aerodynamic " + "surfaces. Add it to the rocket before the flight, with " + "`active=False` if it should start switched off." + ) + flight._surface_switches.setdefault(surface, []).append((time, active)) + + def apply_rollback_command(flight, time, state): """Apply a rollback request returned by an event trigger. diff --git a/rocketpy/simulation/flight.py b/rocketpy/simulation/flight.py index 326ac61e6..85c232157 100644 --- a/rocketpy/simulation/flight.py +++ b/rocketpy/simulation/flight.py @@ -797,6 +797,9 @@ def __init_equations_of_motion(self): self.u_dot_parachute = PARACHUTE_DYNAMICS.bind(self) self.udot_rail1 = RAIL_DYNAMICS.bind(self) self.udot_rail2 = self.u_dot_generalized + + # The switches events make during the flight: surface -> [(time, active)] + self._surface_switches = {} normalized_simulation_mode = "".join(self.simulation_mode.split()).upper() if normalized_simulation_mode == "3DOF": diff --git a/tests/integration/simulation/test_event.py b/tests/integration/simulation/test_event.py index 138d3fe65..05622da76 100644 --- a/tests/integration/simulation/test_event.py +++ b/tests/integration/simulation/test_event.py @@ -4,7 +4,14 @@ import numpy as np import pytest -from rocketpy import Environment, Event, Flight, Rocket, SolidMotor +from rocketpy import ( + Environment, + Event, + Flight, + GenericSurface, + Rocket, + SolidMotor, +) from rocketpy.simulation.events import Commands, exact_time_solvers from rocketpy.simulation.events.event_builders import ( apogee_callback, @@ -676,3 +683,68 @@ def test_a_phase_wider_than_the_canonical_state_flies(): # while the phases before it have no such state with pytest.raises(KeyError, match="not defined in this flight phase"): solution.value_at(0, "heading") + + +def test_an_event_switches_a_surface_off_and_another_on_mid_flight( + calisto_robust, example_plain_env +): + """A surface switched by an event produces force only while switched on, + during the flight and in the outputs worked out after it.""" + rocket = calisto_robust + area, length = rocket.area, 2 * rocket.radius + fairing = GenericSurface(area, length, {"cA": 0.3}, name="Fairing") + brake = GenericSurface(area, length, {"cA": 5.0}, name="Brake", active=False) + rocket.add_surfaces([fairing, brake], positions=[0.0, 0.0]) + switch_time = 8.0 + + def switch(context): + context.event.commands.deactivate_surface(fairing) + context.event.commands.activate_surface(brake) + + switch_event = Event( + callback=switch, + trigger=switch_time, + sampling_rate=100, + trigger_only_once=True, + name="Switch surfaces", + ) + + def fly(events): + return Flight( + rocket=rocket, + environment=example_plain_env, + rail_length=5.2, + inclination=85, + heading=0, + terminate_on_apogee=True, + custom_events=events, + ) + + plain = fly([]) + switched = fly([switch_event]) + + # the event fired once, at its first sampling time past the switch time + assert len(switch_event.triggered_times) == 1 + fired = switch_event.triggered_times[0] + assert fired == pytest.approx(switch_time, abs=0.011) + + # which surfaces produce force before and after it + assert fairing.is_active(fired - 0.5, switched) is True + assert brake.is_active(fired - 0.5, switched) is False + assert fairing.is_active(fired + 0.5, switched) is False + assert brake.is_active(fired + 0.5, switched) is True + + # the surfaces themselves are untouched, so the plain flight flown first + # and any later one start from their own settings + assert fairing.active is True + assert brake.active is False + assert brake.is_active(fired + 0.5, plain) is False + + # the brake has far more drag than the fairing, so the rocket slows down + # faster once it is on: same flight up to the switch, lower apogee after + assert switched.z(fired - 0.5) == pytest.approx(plain.z(fired - 0.5), rel=1e-4) + assert switched.apogee < plain.apogee - 10.0 + # and the outputs worked out after the flight show the extra drag only + # from the switch on (az is more negative with the brake on) + assert switched.az(fired - 0.5) == pytest.approx(plain.az(fired - 0.5), rel=1e-2) + assert switched.az(fired + 0.5) < plain.az(fired + 0.5) - 1.0 diff --git a/tests/unit/rocket/aero_surface/test_controllable_generic_surface.py b/tests/unit/rocket/aero_surface/test_controllable_generic_surface.py index 5503934e1..5aebe3917 100644 --- a/tests/unit/rocket/aero_surface/test_controllable_generic_surface.py +++ b/tests/unit/rocket/aero_surface/test_controllable_generic_surface.py @@ -113,31 +113,22 @@ def test_active_during_preset_round_trips_through_dict(): assert restored.active_during == "power_on" -def test_active_during_callable_round_trips_through_dict(): - """A custom activation function is pickled through to_dict/from_dict and - restored to a working callable.""" +def test_starting_switched_off_round_trips_through_dict(): + """Whether the surface starts each flight switched on is saved with it.""" surface = ControllableGenericSurface( reference_area=1, reference_length=0.2, coefficients={}, - active_during=lambda t, flight: t < 1.0, + active=False, ) restored = ControllableGenericSurface.from_dict(surface.to_dict()) - assert callable(restored.active_during) - assert restored.active_during(0.5, None) is True - assert restored.active_during(2.0, None) is False - - -def test_active_during_callable_dropped_when_pickling_disabled(): - """With allow_pickle=False a custom function cannot be stored, so it saves - as the 'always' preset rather than a broken reference.""" - surface = ControllableGenericSurface( - reference_area=1, - reference_length=0.2, - coefficients={}, - active_during=lambda t, flight: t < 1.0, + assert restored.active is False + assert ( + ControllableGenericSurface.from_dict( + ControllableGenericSurface(1, 0.2, {}).to_dict() + ).active + is True ) - assert surface.to_dict(allow_pickle=False)["active_during"] == "always" def test_controls_and_coefficients_round_trip_through_dict(): diff --git a/tests/unit/rocket/aero_surface/test_generic_surfaces.py b/tests/unit/rocket/aero_surface/test_generic_surfaces.py index 05b626232..31c0a5a16 100644 --- a/tests/unit/rocket/aero_surface/test_generic_surfaces.py +++ b/tests/unit/rocket/aero_surface/test_generic_surfaces.py @@ -363,32 +363,34 @@ def test_reynolds_uses_reynolds_length_not_reference_length(): assert reynolds_seen != pytest.approx(rho_atm * speed * ref_length / mu) -def _fake_flight(burn_out_time): - """Minimal stand-in exposing only what ``is_active`` reads - (``flight.rocket.motor.burn_out_time``), so no real Flight is built.""" - return SimpleNamespace( - rocket=SimpleNamespace(motor=SimpleNamespace(burn_out_time=burn_out_time)) +def _active_at(surface, t, burn_out_time=3.0): + """Whether ``surface`` produces force at time ``t`` of a flight whose motor + burns out at ``burn_out_time``. Uses a stand-in flight exposing only what + ``is_active`` reads, so no real Flight is built.""" + flight = SimpleNamespace( + rocket=SimpleNamespace(motor=SimpleNamespace(burn_out_time=burn_out_time)), + _surface_switches={}, ) + return surface.is_active(t, flight) def test_active_during_defaults_to_always(): - """By default a surface is active at every time.""" + """By default a surface is switched on and active at every time.""" gs = GenericSurface(REFERENCE_AREA, REFERENCE_LENGTH, {"cN": 1}) - flight = _fake_flight(burn_out_time=3.0) assert gs.active_during == "always" - assert gs.is_active(0.0, flight) is True - assert gs.is_active(5.0, flight) is True + assert gs.active is True + assert _active_at(gs, 0.0) is True + assert _active_at(gs, 5.0) is True def test_active_during_power_on_gates_at_burnout(): - """A power-on surface is active up to (not including) burnout.""" + """A power-on surface is active only before burnout.""" gs = GenericSurface( REFERENCE_AREA, REFERENCE_LENGTH, {"cN": 1}, active_during="power_on" ) - flight = _fake_flight(burn_out_time=3.0) - assert gs.is_active(2.999, flight) is True - assert gs.is_active(3.0, flight) is False - assert gs.is_active(4.0, flight) is False + assert _active_at(gs, 2.999) is True + assert _active_at(gs, 3.0) is False + assert _active_at(gs, 4.0) is False def test_active_during_power_off_gates_at_burnout(): @@ -396,31 +398,28 @@ def test_active_during_power_off_gates_at_burnout(): gs = GenericSurface( REFERENCE_AREA, REFERENCE_LENGTH, {"cN": 1}, active_during="power_off" ) - flight = _fake_flight(burn_out_time=3.0) - assert gs.is_active(2.999, flight) is False - assert gs.is_active(3.0, flight) is True - assert gs.is_active(4.0, flight) is True + assert _active_at(gs, 2.999) is False + assert _active_at(gs, 3.0) is True + assert _active_at(gs, 4.0) is True -def test_active_during_accepts_callable(): - """A custom predicate receives (t, flight) and drives activation.""" - seen = [] +def test_a_surface_built_switched_off_produces_no_force(): + """``active=False`` keeps the surface out until an event switches it on.""" + gs = GenericSurface(REFERENCE_AREA, REFERENCE_LENGTH, {"cN": 1}, active=False) + assert gs.active is False + assert _active_at(gs, 0.0) is False + assert _active_at(gs, 5.0) is False - def only_after_one_second(t, flight): - seen.append((t, flight)) - return t > 1.0 - gs = GenericSurface( - REFERENCE_AREA, - REFERENCE_LENGTH, - {"cN": 1}, - active_during=only_after_one_second, - ) - flight = _fake_flight(burn_out_time=3.0) - assert gs.is_active(0.5, flight) is False - assert gs.is_active(2.0, flight) is True - # The predicate was called with the time and the flight object. - assert seen[0] == (0.5, flight) +def test_active_during_no_longer_accepts_a_function(): + """Switching at any other moment is done by an event, and the error says so.""" + with pytest.raises(ValueError, match="activate_surface"): + GenericSurface( + REFERENCE_AREA, + REFERENCE_LENGTH, + {"cN": 1}, + active_during=lambda t, flight: t > 1.0, + ) def test_active_during_invalid_value_raises(): @@ -436,13 +435,14 @@ def test_active_during_invalid_value_raises(): def test_generic_surface_round_trips_through_encoder(): """A GenericSurface survives the full .rpy encode/decode: coefficients, - reynolds_length and a custom activation function are all restored.""" + reynolds_length and whether it starts switched on are all restored.""" gs = GenericSurface( reference_area=1.0, reference_length=0.2, coefficients={"cN": lambda mach: 2 * mach, "cm": 0.1}, reynolds_length=4.0, - active_during=lambda t, flight: t < 3.0, + active_during="power_off", + active=False, ) restored = _rpy_round_trip(gs) @@ -450,8 +450,8 @@ def test_generic_surface_round_trips_through_encoder(): assert restored.reynolds_length == 4.0 assert restored.cN(0, 0, 0.5, 0, 0, 0, 0) == pytest.approx(1.0) assert restored.cm(0, 0, 0, 0, 0, 0, 0) == pytest.approx(0.1) - assert restored.active_during(1.0, None) is True - assert restored.active_during(5.0, None) is False + assert restored.active_during == "power_off" + assert restored.active is False def test_linear_generic_surface_round_trips_through_encoder(): diff --git a/tests/unit/rocket/test_stability_rework.py b/tests/unit/rocket/test_stability_rework.py index 917fd4cd5..05b7becb2 100644 --- a/tests/unit/rocket/test_stability_rework.py +++ b/tests/unit/rocket/test_stability_rework.py @@ -1491,6 +1491,21 @@ def test_closed_form_damping_follows_the_stability_phase(): assert expected == pytest.approx(0.5 * rocket.area * arm_sum) +def test_a_surface_that_starts_switched_off_is_left_out_of_the_stability_sum(): + """It is not part of the rocket until an event switches it on.""" + area, length = math.pi * 0.0635**2, 0.127 + coefficients = {"cN_alpha": 2.0, "cY_beta": -2.0} + switched_off = LinearGenericSurface(area, length, coefficients, active=False) + switched_on = LinearGenericSurface(area, length, coefficients) + + without = stability_surfaces(_rocket_with(switched_off, -0.8)) + with_it = stability_surfaces(_rocket_with(switched_on, -0.8)) + + assert switched_off not in [surface for surface, _ in without] + assert switched_on in [surface for surface, _ in with_it] + assert len(with_it) == len(without) + 1 + + def _oscillator_rocket(): """A rocket of built-in surfaces with a generic surface that adds nothing.""" nothing = LinearGenericSurface(math.pi * 0.0635**2, 0.127, {}) diff --git a/tests/unit/simulation/test_aerodynamic_drag_force.py b/tests/unit/simulation/test_aerodynamic_drag_force.py index ba65f514b..aa6197032 100644 --- a/tests/unit/simulation/test_aerodynamic_drag_force.py +++ b/tests/unit/simulation/test_aerodynamic_drag_force.py @@ -141,7 +141,8 @@ def test_axial_drag_follows_the_air_moving_along_the_axis(angle_deg, equations): ), position=-0.5, ) - flight = SimpleNamespace(env=env, rocket=rocket) # all the derivative reads + # all the derivative reads + flight = SimpleNamespace(env=env, rocket=rocket, _surface_switches={}) speed, angle = 80.0, np.radians(angle_deg) upright = [1.0, 0.0, 0.0, 0.0] # the body axis is the inertial z axis velocity = [speed * np.sin(angle), 0.0, speed * np.cos(angle)] @@ -190,7 +191,8 @@ def test_3dof_drag_acts_against_the_velocity(velocity): ), position=0, ) - flight = SimpleNamespace(env=env, rocket=rocket) # all the derivative reads + # all the derivative reads + flight = SimpleNamespace(env=env, rocket=rocket, _surface_switches={}) upright = [1.0, 0.0, 0.0, 0.0] state = [0.0, 0.0, 1000.0, *velocity, *upright, 0.0, 0.0, 0.0] diff --git a/tests/unit/simulation/test_surface_activation.py b/tests/unit/simulation/test_surface_activation.py new file mode 100644 index 000000000..d95f69b4d --- /dev/null +++ b/tests/unit/simulation/test_surface_activation.py @@ -0,0 +1,121 @@ +from types import SimpleNamespace + +import pytest + +from rocketpy import GenericSurface +from rocketpy.simulation.events import Event +from rocketpy.simulation.events.commands import Commands +from rocketpy.simulation.events.event_execution import apply_event_commands + + +def _surface(name, **kwargs): + return GenericSurface(1.0, 1.0, {}, name=name, **kwargs) + + +def _flight(*surfaces, burn_out_time=3.0): + """A stand-in flight of a rocket holding the surfaces: only what + activation reads, so no real Flight is built.""" + rocket = SimpleNamespace( + aerodynamic_surfaces=[(surface, None) for surface in surfaces], + motor=SimpleNamespace(burn_out_time=burn_out_time), + ) + return SimpleNamespace(rocket=rocket, _surface_switches={}) + + +def _switch(flight, time, **commands): + """Run an event that switches surfaces at ``time``, through the usual + command path. ``commands`` are ``on=[...]`` and ``off=[...]``.""" + event = Event(callback=lambda context: None, name="Switch") + for surface in commands.get("off", ()): + event.commands.deactivate_surface(surface) + for surface in commands.get("on", ()): + event.commands.activate_surface(surface) + apply_event_commands( + flight, + event, + event.commands, + phase=SimpleNamespace(), + phase_index=0, + node_index=0, + command_time=time, + ) + + +def test_a_surface_starting_switched_off_is_never_active(): + deployable = _surface("deployable", active=False) + flight = _flight(deployable) + + assert deployable.is_active(0.0, flight) is False + assert deployable.is_active(10.0, flight) is False + + +def test_switching_takes_effect_from_its_time_on(): + fixed = _surface("fixed") + removable = _surface("removable") + deployable = _surface("deployable", active=False) + flight = _flight(fixed, removable, deployable) + + _switch(flight, 5.0, off=[removable], on=[deployable]) + + assert fixed.is_active(8.0, flight) is True + assert removable.is_active(5.0, flight) is False + assert deployable.is_active(5.0, flight) is True + + +def test_earlier_times_keep_what_was_active_then(): + """The flight's outputs are worked out after it ends, at every stored time.""" + removable = _surface("removable") + flight = _flight(removable) + + _switch(flight, 5.0, off=[removable]) + _switch(flight, 9.0, on=[removable]) + + assert removable.is_active(4.999, flight) is True + assert removable.is_active(5.0, flight) is False + assert removable.is_active(8.999, flight) is False + assert removable.is_active(9.0, flight) is True + + +def test_a_switched_on_surface_still_follows_its_motor_phase(): + base = _surface("base drag", active_during="power_off", active=False) + flight = _flight(base, burn_out_time=3.0) + + _switch(flight, 1.0, on=[base]) + + assert base.is_active(2.0, flight) is False # on, but the motor still burns + assert base.is_active(3.0, flight) is True + + +def test_switching_does_not_change_the_surface_itself(): + """Another flight of the same rocket starts from the surface's own setting.""" + removable = _surface("removable") + first = _flight(removable) + _switch(first, 5.0, off=[removable]) + + assert removable.active is True + assert removable.is_active(10.0, _flight(removable)) is True + + +def test_switching_a_surface_the_rocket_does_not_have_is_an_error(): + flight = _flight(_surface("fixed")) + + with pytest.raises(ValueError, match="stray.*not one of the rocket"): + _switch(flight, 1.0, on=[_surface("stray")]) + + +def test_surface_commands_are_queued_and_mark_a_trajectory_change(): + commands = Commands() + removable, deployable = _surface("removable"), _surface("deployable") + assert commands.changes_trajectory is False + + commands.deactivate_surface(removable) + commands.activate_surface(deployable) + + assert commands.surface_switches == [(removable, False), (deployable, True)] + # the forces change, so the solver has to restart from the switch + assert commands.changes_trajectory is True + + commands.reset() + + assert commands.surface_switches == [] + assert commands.changes_trajectory is False From ee92cdc4130184d697e3d73daeedd566aee5f9c9 Mon Sep 17 00:00:00 2001 From: MateusStano Date: Mon, 5 Oct 2026 19:47:01 -0300 Subject: [PATCH 18/22] MNT: give the generic surfaces one argument order, keyword-only after name GenericSurface, LinearGenericSurface and ControllableGenericSurface now take their shared arguments in the same order, with each class's own argument last (`axisymmetric`, `controls`). ControllableGenericSurface had `interpolation`/`extrapolation` and `force_convention`/ `active_during` swapped relative to the other two. Every argument after `name` is keyword-only. The first five arguments are the ones released on master and can still be passed by position. --- .../controllable_generic_surface.py | 41 ++++++++++--------- .../rocket/aero_surface/generic_surface.py | 1 + .../aero_surface/linear_generic_surface.py | 17 ++++---- 3 files changed, 31 insertions(+), 28 deletions(-) diff --git a/rocketpy/rocket/aero_surface/controllable_generic_surface.py b/rocketpy/rocket/aero_surface/controllable_generic_surface.py index 5d7b6ef28..89fcfbbe1 100644 --- a/rocketpy/rocket/aero_surface/controllable_generic_surface.py +++ b/rocketpy/rocket/aero_surface/controllable_generic_surface.py @@ -137,13 +137,14 @@ def __init__( coefficients, center_of_pressure=(0, 0, 0), name="Controllable Generic Surface", - controls=("deflection",), + *, reynolds_length=None, - extrapolation=None, interpolation=None, - active_during="always", + extrapolation=None, force_convention=None, + active_during="always", active=True, + controls=("deflection",), ): """Create an aerodynamic surface whose coefficients also depend on controls, such as a canard deflection. @@ -190,24 +191,11 @@ def __init__( the moment coefficients. Default ``(0, 0, 0)``. name : str, optional Name of the surface. Default ``"Controllable Generic Surface"``. - controls : iterable of str, optional - Names of the controls, such as a canard deflection angle. Each name - becomes an extra input to every coefficient, after the seven flow - variables and in this order, and a key in :attr:`control_state`. The - values start at 0 and are set with :meth:`set_control`. Default - ``("deflection",)``. reynolds_length : int, float, optional Length scale, in meters, of the Reynolds number passed to the coefficients. Set it to the length your Reynolds-dependent data was tabulated against (for example the rocket's body length). ``None`` (the default) uses ``reference_length`` (the diameter). - extrapolation : str or dict, optional - What tabulated coefficients do outside their data range: - ``"constant"`` holds the nearest edge value, ``"natural"`` keeps - following the curve, ``"zero"`` returns 0. Give one string for all - coefficients or a dict keyed by coefficient name. ``None`` (the - default) uses ``"constant"`` for tables built here and leaves a - pre-built :class:`Function` unchanged. interpolation : str or dict, optional How tabulated coefficients read values between points: a 1-D table accepts ``"linear"``, ``"akima"``, ``"spline"`` and ``"polynomial"``; @@ -218,15 +206,22 @@ def __init__( or a dict keyed by coefficient name. ``None`` (the default) uses ``"linear"`` for tables built here and leaves a pre-built :class:`Function` unchanged. + extrapolation : str or dict, optional + What tabulated coefficients do outside their data range: + ``"constant"`` holds the nearest edge value, ``"natural"`` keeps + following the curve, ``"zero"`` returns 0. Give one string for all + coefficients or a dict keyed by coefficient name. ``None`` (the + default) uses ``"constant"`` for tables built here and leaves a + pre-built :class:`Function` unchanged. + force_convention : str, optional + The frame the force coefficients are given in: ``"body"`` for + ``cN``/``cY``/``cA`` or ``"wind"`` for ``cL``/``cQ``/``cD``. ``None`` + (the default) works it out from the names. active_during : str, optional The motor phase this surface produces force in: ``"always"`` (default), ``"power_on"`` (only while the motor burns, e.g. jet vanes) or ``"power_off"`` (only after burnout). To switch a surface on or off at any other moment, use an event: see ``active`` below. - force_convention : str, optional - The frame the force coefficients are given in: ``"body"`` for - ``cN``/``cY``/``cA`` or ``"wind"`` for ``cL``/``cQ``/``cD``. ``None`` - (the default) works it out from the names. active : bool, optional Whether the surface starts each flight switched on. Default is ``True``. Use ``False`` for a surface that only appears later in @@ -234,6 +229,12 @@ def __init__( ``context.event.commands.activate_surface(surface)``. A surface that is on from the start is switched off the same way, with ``deactivate_surface``. + controls : iterable of str, optional + Names of the controls, such as a canard deflection angle. Each name + becomes an extra input to every coefficient, after the seven flow + variables and in this order, and a key in :attr:`control_state`. The + values start at 0 and are set with :meth:`set_control`. Default + ``("deflection",)``. Raises ------ diff --git a/rocketpy/rocket/aero_surface/generic_surface.py b/rocketpy/rocket/aero_surface/generic_surface.py index b7ce9af7d..b59407c5c 100644 --- a/rocketpy/rocket/aero_surface/generic_surface.py +++ b/rocketpy/rocket/aero_surface/generic_surface.py @@ -147,6 +147,7 @@ def __init__( coefficients, center_of_pressure=(0, 0, 0), name="Generic Surface", + *, reynolds_length=None, interpolation=None, extrapolation=None, diff --git a/rocketpy/rocket/aero_surface/linear_generic_surface.py b/rocketpy/rocket/aero_surface/linear_generic_surface.py index f789ad350..be1d96341 100644 --- a/rocketpy/rocket/aero_surface/linear_generic_surface.py +++ b/rocketpy/rocket/aero_surface/linear_generic_surface.py @@ -101,13 +101,14 @@ def __init__( coefficients, center_of_pressure=(0, 0, 0), name="Generic Linear Surface", + *, reynolds_length=None, interpolation=None, extrapolation=None, force_convention=None, active_during="always", - axisymmetric=False, active=True, + axisymmetric=False, ): """Create a linear aerodynamic surface from its coefficient derivatives. @@ -247,6 +248,13 @@ def __init__( To switch a surface on or off at any other moment, such as apogee, use an event: see ``active`` below. + active : bool, optional + Whether the surface starts each flight switched on. Default is + ``True``. Use ``False`` for a surface that only appears later in + the flight, and switch it on from an event with + ``context.event.commands.activate_surface(surface)``. A surface + that is on from the start is switched off the same way, with + ``deactivate_surface``. axisymmetric : bool, optional Set it to ``True`` when the data describes a rocket (or a part) that behaves the same in every plane through its axis, such as a rocket @@ -265,13 +273,6 @@ def __init__( Default is ``False``: the two planes are used as given, and a plane without derivatives produces no force. - active : bool, optional - Whether the surface starts each flight switched on. Default is - ``True``. Use ``False`` for a surface that only appears later in - the flight, and switch it on from an event with - ``context.event.commands.activate_surface(surface)``. A surface - that is on from the start is switched off the same way, with - ``deactivate_surface``. Raises ------ From b1b2effe0fe011112f3a9179e877928e7b72506b Mon Sep 17 00:00:00 2001 From: MateusStano Date: Mon, 5 Oct 2026 19:47:21 -0300 Subject: [PATCH 19/22] DOC: explain which drag a rocket with generic surfaces uses Add a "Which drag is used" section to the generic surfaces guide: the rocket's own drag curves always apply, every surface's axial force is added on top, and `overwrite=True` in `add_full_body_aerodynamics` removes the existing surfaces and zeroes both drag curves. Warn that full-vehicle data that already includes drag is counted twice otherwise. Also fix a typo ("collapses" to "collapse"). --- docs/user/rocket/generic_surface.rst | 30 +++++++++++++++++++++++++++- 1 file changed, 29 insertions(+), 1 deletion(-) diff --git a/docs/user/rocket/generic_surface.rst b/docs/user/rocket/generic_surface.rst index c4e147111..3fb163a1b 100644 --- a/docs/user/rocket/generic_surface.rst +++ b/docs/user/rocket/generic_surface.rst @@ -1240,10 +1240,38 @@ lowers the base drag). To capture this, build two surfaces, set each one's ``active_during`` to ``"power_on"`` and ``"power_off"``, and pass them as a list; each then produces force only during its phase. +Which drag is used +~~~~~~~~~~~~~~~~~~ + +The drag of a rocket in flight is the sum of two things: + +- **The rocket's own drag curves**, ``power_on_drag`` before burnout and + ``power_off_drag`` after it. They are always used. +- **The axial force of every surface**, which is added on top. The predefined + surfaces (nose cone, fins, tail) have no axial coefficient, so they add no + drag. A generic surface adds drag only if it is given a ``cA`` (or ``cD``) + coefficient. + +A full-vehicle surface follows the same rule, since it is an ordinary generic +surface. What changes is the ``overwrite`` argument: + +- ``overwrite=False`` (default): the surface is added to what the rocket + already has. Its forces, drag included, add to the rocket's drag curves and + to the other surfaces. +- ``overwrite=True``: every surface already on the rocket is removed and both + drag curves are set to zero, so the new surface is the only source of + aerodynamic forces. + +.. warning:: + If your full-vehicle data already includes drag, use ``overwrite=True``, or + build the rocket with ``power_on_drag=0`` and ``power_off_drag=0``. + Otherwise the drag is counted twice: once from the rocket's curves and once + from the surface. + Extracting a rocket's coefficients ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -You can also collapses an assembled rocket into a single +You can also collapse an assembled rocket into a single stability-derivative model about its center of dry mass: - :meth:`rocketpy.Rocket.to_coefficients` returns the coefficient curves as a From 1fbf86b0cafa4e4444c386f54952b8a2f23f8e1c Mon Sep 17 00:00:00 2001 From: MateusStano Date: Mon, 5 Oct 2026 20:07:09 -0300 Subject: [PATCH 20/22] STY: fix pylint warnings - Remove unused imports and the unused `plane` argument of `_draw_center_of_mass_and_pressure`. - Give every `to_dict` override the `include_outputs` argument of its base class. - Explain and disable the warnings that misread a deliberate pattern: `_BaseFin` initializes `GenericSurface` later, `Fin` and `Fins` are still abstract, and `u_dot_parachute` keeps the shared `t` argument. - Disable too-many-lines in `rocket.py` and `flight.py`, too-many-locals in `full_body_coefficients` and too-many-statements in `AeroCoefficient.__init__`. - Allow names like `M1_cm` in `.pylintrc`. - Tests: move imports to the top, use dict literals, drop unused variables and arguments. --- .pylintrc | 1 + rocketpy/plots/flight_plots.py | 2 +- rocketpy/plots/rocket_plots.py | 4 +- rocketpy/rocket/_helpers.py | 2 +- .../rocket/aero_surface/aero_coefficient.py | 2 +- rocketpy/rocket/aero_surface/air_brakes.py | 2 +- .../rocket/aero_surface/fins/_base_fin.py | 4 +- .../aero_surface/fins/elliptical_fins.py | 8 +-- rocketpy/rocket/aero_surface/fins/fin.py | 3 +- rocketpy/rocket/aero_surface/fins/fins.py | 5 +- .../aero_surface/fins/free_form_fins.py | 8 +-- .../aero_surface/fins/trapezoidal_fins.py | 8 +-- .../rocket/aero_surface/generic_surface.py | 3 +- rocketpy/rocket/aero_surface/nose_cone.py | 4 +- rocketpy/rocket/aero_surface/rail_buttons.py | 2 +- rocketpy/rocket/aero_surface/tail.py | 4 +- rocketpy/rocket/rocket.py | 4 +- rocketpy/simulation/flight.py | 3 +- .../simulation/helpers/flight_derivatives.py | 4 +- rocketpy/stochastic/stochastic_model.py | 1 - tests/fixtures/rockets/rocket_fixtures.py | 13 ++-- tests/integration/simulation/test_event.py | 60 +++++++++---------- .../aero_surface/test_aero_coefficient.py | 2 +- .../test_barrowman_generic_equivalence.py | 4 +- .../test_controllable_generic_surface.py | 2 +- .../aero_surface/test_generic_surfaces.py | 12 ++-- .../aero_surface/test_individual_fins.py | 42 ++++++++++--- .../test_surface_coefficient_completeness.py | 6 -- .../test_generic_calisto_equivalence.py | 1 - tests/unit/rocket/test_rocket.py | 3 +- tests/unit/rocket/test_stability_rework.py | 28 ++++----- tests/unit/simulation/test_dynamics.py | 10 ++-- tests/unit/simulation/test_event.py | 11 ++-- tests/unit/simulation/test_flight.py | 19 +++--- tests/unit/simulation/test_solution.py | 15 +++-- .../unit/stochastic/test_stochastic_rocket.py | 20 +++---- 36 files changed, 166 insertions(+), 156 deletions(-) diff --git a/.pylintrc b/.pylintrc index feb85e897..87ceb2f77 100644 --- a/.pylintrc +++ b/.pylintrc @@ -253,6 +253,7 @@ good-names=FlightPhases, good-names-rgxs= ^[a-z][0-9]?$, # Single lowercase characters, possibly followed by a single digit ^[A-Z][0-9]{0,2}$, # Single uppercase characters, possibly followed by one or two digits ^[A-Z]+_\d+(_dot)?$, # Uppercase characters followed by underscore and digits, optionally followed by _dot + ^[A-Z][0-9]_[a-z]+$, # An equation symbol with a lowercase suffix, such as M1_cm ^(dry_|propellant_)[A-Z]+_\d+$, # Variables starting with 'dry_' or 'propellant_', followed by uppercase characters, underscore, and digits ^[a-z]+_ISA$, # Lowercase words ending with '_ISA' ^plot(1D|2D)$, # Variables starting with 'plot' followed by '1D' or '2D' diff --git a/rocketpy/plots/flight_plots.py b/rocketpy/plots/flight_plots.py index 8ccb1d4a1..93a43d241 100644 --- a/rocketpy/plots/flight_plots.py +++ b/rocketpy/plots/flight_plots.py @@ -1293,7 +1293,7 @@ def _plot_angles(self, ax, t_lower, t_upper): # stay in view tops.append(1.5 * float(np.percentile(values, 95))) tops.append(1.15 * float(values[0])) - top = max(1.0, max(tops, default=1.0)) + top = max([1.0, *tops]) ax.axhline(0, color="0.6", linewidth=0.8) ax.set_xlim(t_lower, t_upper) ax.set_ylim(-top, top) diff --git a/rocketpy/plots/rocket_plots.py b/rocketpy/plots/rocket_plots.py index e77a2cfa2..e736ead07 100644 --- a/rocketpy/plots/rocket_plots.py +++ b/rocketpy/plots/rocket_plots.py @@ -337,7 +337,7 @@ def _draw_on_plane(self, ax, vis_args, plane): last_radius, last_x = self._draw_tubes(ax, drawn_surfaces, vis_args) self._draw_motor(last_radius, last_x, ax, vis_args) self._draw_rail_buttons(ax, vis_args) - self._draw_center_of_mass_and_pressure(ax, plane) + self._draw_center_of_mass_and_pressure(ax) self._draw_sensors(ax, self.rocket.sensors, plane) title = "Rocket Representation" @@ -761,7 +761,7 @@ def _draw_rail_buttons(self, ax, vis_args): except IndexError: pass - def _draw_center_of_mass_and_pressure(self, ax, plane="xz"): + def _draw_center_of_mass_and_pressure(self, ax): """Draws the center of mass and center of pressure of the rocket. The red dot is the (linear) aerodynamic center, conventionally labeled diff --git a/rocketpy/rocket/_helpers.py b/rocketpy/rocket/_helpers.py index 3e04b6bc0..59cf8b573 100644 --- a/rocketpy/rocket/_helpers.py +++ b/rocketpy/rocket/_helpers.py @@ -1172,7 +1172,7 @@ def slug(text): return axes -def full_body_coefficients( +def full_body_coefficients( # pylint: disable=too-many-locals rocket, machs=None, force_convention="body", diff --git a/rocketpy/rocket/aero_surface/aero_coefficient.py b/rocketpy/rocket/aero_surface/aero_coefficient.py index 6ff3af412..37b852242 100644 --- a/rocketpy/rocket/aero_surface/aero_coefficient.py +++ b/rocketpy/rocket/aero_surface/aero_coefficient.py @@ -105,7 +105,7 @@ class AeroCoefficient: ) _EXTRAPOLATIONS = ("constant", "natural", "zero") - def __init__( + def __init__( # pylint: disable=too-many-statements self, source, depends_on=None, diff --git a/rocketpy/rocket/aero_surface/air_brakes.py b/rocketpy/rocket/aero_surface/air_brakes.py index 0f45a6d28..5d2386220 100644 --- a/rocketpy/rocket/aero_surface/air_brakes.py +++ b/rocketpy/rocket/aero_surface/air_brakes.py @@ -198,7 +198,7 @@ def all_info(self): self.info() self.plots.drag_coefficient_curve() - def to_dict(self, **kwargs): # pylint: disable=unused-argument + def to_dict(self, include_outputs=False, **kwargs): # pylint: disable=unused-argument return { "drag_coefficient_curve": self.drag_coefficient, "reference_area": self.reference_area, diff --git a/rocketpy/rocket/aero_surface/fins/_base_fin.py b/rocketpy/rocket/aero_surface/fins/_base_fin.py index 3839e0ad9..1f1aa5704 100644 --- a/rocketpy/rocket/aero_surface/fins/_base_fin.py +++ b/rocketpy/rocket/aero_surface/fins/_base_fin.py @@ -18,7 +18,9 @@ class _BaseFin(_BarrowmanSurface): Handles shared initialization logic and common properties. """ - def __init__( + # GenericSurface.__init__ runs later, in _build_surface, once the fin shape + # class has set up its geometry + def __init__( # pylint: disable=super-init-not-called self, name, rocket_radius, root_chord, span, airfoil=None, cant_angle=0 ): """ diff --git a/rocketpy/rocket/aero_surface/fins/elliptical_fins.py b/rocketpy/rocket/aero_surface/fins/elliptical_fins.py index 31f4cbf3c..49bd29a93 100644 --- a/rocketpy/rocket/aero_surface/fins/elliptical_fins.py +++ b/rocketpy/rocket/aero_surface/fins/elliptical_fins.py @@ -211,11 +211,9 @@ def evaluate_center_of_pressure(self): cpz = 0.288 * self.root_chord self._set_center_of_pressure((0, 0, cpz)) - def to_dict(self, **kwargs): - data = super().to_dict(**kwargs) - data.update( - self.geometry.get_data(include_outputs=kwargs.get("include_outputs", False)) - ) + def to_dict(self, include_outputs=False, **kwargs): + data = super().to_dict(include_outputs=include_outputs, **kwargs) + data.update(self.geometry.get_data(include_outputs=include_outputs)) return data @classmethod diff --git a/rocketpy/rocket/aero_surface/fins/fin.py b/rocketpy/rocket/aero_surface/fins/fin.py index 61910efdf..48c8eb1b1 100644 --- a/rocketpy/rocket/aero_surface/fins/fin.py +++ b/rocketpy/rocket/aero_surface/fins/fin.py @@ -7,7 +7,8 @@ from rocketpy.rocket.aero_surface.fins._base_fin import _BaseFin -class Fin(_BaseFin): +# Still abstract: each fin shape class provides the center of pressure +class Fin(_BaseFin): # pylint: disable=abstract-method """Abstract class that holds common methods for the individual fin classes. Cannot be instantiated. diff --git a/rocketpy/rocket/aero_surface/fins/fins.py b/rocketpy/rocket/aero_surface/fins/fins.py index a75db1ae4..40c9ce7d9 100644 --- a/rocketpy/rocket/aero_surface/fins/fins.py +++ b/rocketpy/rocket/aero_surface/fins/fins.py @@ -1,9 +1,8 @@ -import numpy as np - from rocketpy.rocket.aero_surface.fins._base_fin import _BaseFin -class Fins(_BaseFin): +# Still abstract: each fin shape class provides the center of pressure +class Fins(_BaseFin): # pylint: disable=abstract-method """Abstract class that holds common methods for the fin classes. Cannot be instantiated. diff --git a/rocketpy/rocket/aero_surface/fins/free_form_fins.py b/rocketpy/rocket/aero_surface/fins/free_form_fins.py index 549d0ad80..caba13a21 100644 --- a/rocketpy/rocket/aero_surface/fins/free_form_fins.py +++ b/rocketpy/rocket/aero_surface/fins/free_form_fins.py @@ -201,11 +201,9 @@ def evaluate_center_of_pressure(self): def shape_points(self): return self.geometry.shape_points - def to_dict(self, **kwargs): - data = super().to_dict(**kwargs) - data.update( - self.geometry.get_data(include_outputs=kwargs.get("include_outputs", False)) - ) + def to_dict(self, include_outputs=False, **kwargs): + data = super().to_dict(include_outputs=include_outputs, **kwargs) + data.update(self.geometry.get_data(include_outputs=include_outputs)) return data @classmethod diff --git a/rocketpy/rocket/aero_surface/fins/trapezoidal_fins.py b/rocketpy/rocket/aero_surface/fins/trapezoidal_fins.py index 8a3d140e4..5f6c3fe96 100644 --- a/rocketpy/rocket/aero_surface/fins/trapezoidal_fins.py +++ b/rocketpy/rocket/aero_surface/fins/trapezoidal_fins.py @@ -262,11 +262,9 @@ def evaluate_center_of_pressure(self): ) self._set_center_of_pressure((0, 0, cpz)) - def to_dict(self, **kwargs): - data = super().to_dict(**kwargs) - data.update( - self.geometry.get_data(include_outputs=kwargs.get("include_outputs", False)) - ) + def to_dict(self, include_outputs=False, **kwargs): + data = super().to_dict(include_outputs=include_outputs, **kwargs) + data.update(self.geometry.get_data(include_outputs=include_outputs)) return data @classmethod diff --git a/rocketpy/rocket/aero_surface/generic_surface.py b/rocketpy/rocket/aero_surface/generic_surface.py index b59407c5c..344ee6d1b 100644 --- a/rocketpy/rocket/aero_surface/generic_surface.py +++ b/rocketpy/rocket/aero_surface/generic_surface.py @@ -1,5 +1,4 @@ import csv -import math import numpy as np @@ -383,7 +382,7 @@ def _set_center_of_pressure(self, value): f"{value!r}." ) from exc numeric = isinstance(z, (int, float, np.number)) - if hasattr(self, "cm") and (not numeric or self._xcp is not None): + if hasattr(self, "cm") and (not numeric or getattr(self, "_xcp", None)): raise ValueError( "A center of pressure that varies with Mach is folded into the " "moment coefficients when the surface is built: create a new " diff --git a/rocketpy/rocket/aero_surface/nose_cone.py b/rocketpy/rocket/aero_surface/nose_cone.py index cb16504ca..835c63346 100644 --- a/rocketpy/rocket/aero_surface/nose_cone.py +++ b/rocketpy/rocket/aero_surface/nose_cone.py @@ -582,7 +582,7 @@ def all_info(self): self.prints.all() self.plots.all() - def to_dict(self, **kwargs): + def to_dict(self, include_outputs=False, **kwargs): data = { "_length": self._input_length, "_kind": self._kind, @@ -592,7 +592,7 @@ def to_dict(self, **kwargs): "_power": self._power, "name": self.name, } - if kwargs.get("include_outputs", False): + if include_outputs: clalpha = self.clalpha if kwargs.get("discretize", False): clalpha = clalpha.set_discrete(0, 4, 50, mutate_self=False) diff --git a/rocketpy/rocket/aero_surface/rail_buttons.py b/rocketpy/rocket/aero_surface/rail_buttons.py index a6fc75b56..880c701e0 100644 --- a/rocketpy/rocket/aero_surface/rail_buttons.py +++ b/rocketpy/rocket/aero_surface/rail_buttons.py @@ -81,7 +81,7 @@ def __init__( def angular_position_rad(self): return np.radians(self.angular_position) - def to_dict(self, **kwargs): # pylint: disable=unused-argument + def to_dict(self, include_outputs=False, **kwargs): # pylint: disable=unused-argument return { "buttons_distance": self.buttons_distance, "angular_position": self.angular_position, diff --git a/rocketpy/rocket/aero_surface/tail.py b/rocketpy/rocket/aero_surface/tail.py index 7a20a16f0..7b56e6b14 100644 --- a/rocketpy/rocket/aero_surface/tail.py +++ b/rocketpy/rocket/aero_surface/tail.py @@ -244,7 +244,7 @@ def all_info(self): self.prints.all() self.plots.all() - def to_dict(self, **kwargs): + def to_dict(self, include_outputs=False, **kwargs): data = { "top_radius": self._top_radius, "bottom_radius": self._bottom_radius, @@ -253,7 +253,7 @@ def to_dict(self, **kwargs): "name": self.name, } - if kwargs.get("include_outputs", False): + if include_outputs: clalpha = self.clalpha if kwargs.get("discretize", False): clalpha = clalpha.set_discrete(0, 4, 50, mutate_self=False) diff --git a/rocketpy/rocket/rocket.py b/rocketpy/rocket/rocket.py index b97b69b00..d495f4567 100644 --- a/rocketpy/rocket/rocket.py +++ b/rocketpy/rocket/rocket.py @@ -1,3 +1,4 @@ +# pylint: disable=too-many-lines import inspect import math import warnings @@ -40,10 +41,7 @@ TrapezoidalFins, ) from rocketpy.rocket.aero_surface.aero_coefficient import AeroCoefficient -from rocketpy.rocket.aero_surface.fins.elliptical_fin import EllipticalFin -from rocketpy.rocket.aero_surface.fins.free_form_fin import FreeFormFin from rocketpy.rocket.aero_surface.fins.free_form_fins import FreeFormFins -from rocketpy.rocket.aero_surface.fins.trapezoidal_fin import TrapezoidalFin from rocketpy.rocket.aero_surface.linear_generic_surface import LinearGenericSurface from rocketpy.rocket.components import Components, position_vector from rocketpy.rocket.parachute import Parachute diff --git a/rocketpy/simulation/flight.py b/rocketpy/simulation/flight.py index 85c232157..944a2387e 100644 --- a/rocketpy/simulation/flight.py +++ b/rocketpy/simulation/flight.py @@ -1,3 +1,4 @@ +# pylint: disable=too-many-lines import warnings from copy import deepcopy from functools import cached_property @@ -797,7 +798,7 @@ def __init_equations_of_motion(self): self.u_dot_parachute = PARACHUTE_DYNAMICS.bind(self) self.udot_rail1 = RAIL_DYNAMICS.bind(self) self.udot_rail2 = self.u_dot_generalized - + # The switches events make during the flight: surface -> [(time, active)] self._surface_switches = {} diff --git a/rocketpy/simulation/helpers/flight_derivatives.py b/rocketpy/simulation/helpers/flight_derivatives.py index f61a7e420..2a96b2ae7 100644 --- a/rocketpy/simulation/helpers/flight_derivatives.py +++ b/rocketpy/simulation/helpers/flight_derivatives.py @@ -948,7 +948,9 @@ def u_dot_generalized(flight, t, u, post_processing=False): return u_dot -def u_dot_parachute(flight, t, u, post_processing=False, *, parachute): +def u_dot_parachute( # pylint: disable=unused-argument + flight, t, u, post_processing=False, *, parachute +): """Compute the parachute descent derivative. Only position and velocity actually move under a parachute. The attitude and diff --git a/rocketpy/stochastic/stochastic_model.py b/rocketpy/stochastic/stochastic_model.py index 5e3e491d5..eb2deeab0 100644 --- a/rocketpy/stochastic/stochastic_model.py +++ b/rocketpy/stochastic/stochastic_model.py @@ -7,7 +7,6 @@ import numpy as np -from rocketpy.mathutils.function import Function from rocketpy.stochastic.custom_sampler import CustomSampler from ..tools import get_distribution diff --git a/tests/fixtures/rockets/rocket_fixtures.py b/tests/fixtures/rockets/rocket_fixtures.py index ac2e6c418..67136fd20 100644 --- a/tests/fixtures/rockets/rocket_fixtures.py +++ b/tests/fixtures/rockets/rocket_fixtures.py @@ -92,17 +92,18 @@ def _generic_surface_from_barrowman(surface): """ clalpha = surface.clalpha # normal-force-curve slope, a Function of Mach - # Coefficient callables must accept the full 7-variable argument tuple. The - # slope ``clalpha`` is a Function of Mach only; the fin roll coefficients - # ``cl_0``/``cl_p`` are AeroCoefficients evaluated over the full tuple. + # A coefficient function only takes the variables it uses, named after + # them. The slope ``clalpha`` is a Function of Mach only; the fin roll + # coefficients ``cl_0``/``cl_p`` are AeroCoefficients evaluated over the + # full tuple. def make_normal(slope): - def cN(alpha, beta, mach, reynolds, pitch_rate, yaw_rate, roll_rate): + def cN(alpha, mach): return slope.get_value_opt(mach) * alpha return cN def make_side(slope): - def cY(alpha, beta, mach, reynolds, pitch_rate, yaw_rate, roll_rate): + def cY(beta, mach): return -slope.get_value_opt(mach) * beta return cY @@ -205,7 +206,7 @@ def _full_body_derivatives(reference_rocket): def coefficients_at(alpha, beta, red_pitch, red_yaw, red_roll, mach): rate_factor = 2.0 / reference_length # reduced rate -> omega at unit speed omega = (red_pitch * rate_factor, red_yaw * rate_factor, red_roll * rate_factor) - r1, r2, r3, m1, m2, m3 = _full_body_force_and_moment( + r1, r2, _, m1, m2, m3 = _full_body_force_and_moment( reference_rocket, alpha, beta, mach, omega ) return { diff --git a/tests/integration/simulation/test_event.py b/tests/integration/simulation/test_event.py index 05622da76..c9d1b585b 100644 --- a/tests/integration/simulation/test_event.py +++ b/tests/integration/simulation/test_event.py @@ -33,6 +33,7 @@ solve_linear, ) from rocketpy.simulation.helpers.dynamics import SIX_DOF_DYNAMICS, _PhaseDynamics +from rocketpy.simulation.solution import Solution def _callback_return_time(context): @@ -96,9 +97,6 @@ def _sample_state(time, vz): def _canonical_solution(*rows): """Build a Solution holding the given canonical rows in one phase.""" - from rocketpy.simulation.helpers.dynamics import SIX_DOF_DYNAMICS - from rocketpy.simulation.solution import Solution - solution = Solution() solution._start_phase( SIX_DOF_DYNAMICS, start_canonical=tuple(rows[0][1:]) if rows else None @@ -340,15 +338,15 @@ def test_core_event_builders_update_flight_state_and_commands(): out_of_rail_state = _sample_state(0.5, 1.0) out_of_rail_state[1] = 1.0 assert out_of_rail_trigger( - dict(flight=flight, canonical_state=out_of_rail_state[1:]) + {"flight": flight, "canonical_state": out_of_rail_state[1:]} ) out_of_rail_callback( - dict( - flight=flight, - event=out_of_rail_event, - time=0.5, - canonical_state=out_of_rail_state[1:], - ) + { + "flight": flight, + "event": out_of_rail_event, + "time": 0.5, + "canonical_state": out_of_rail_state[1:], + } ) assert flight.out_of_rail_time == pytest.approx(0.5) assert flight.out_of_rail_time_index == 1 @@ -359,15 +357,15 @@ def test_core_event_builders_update_flight_state_and_commands(): assert out_of_rail_event.commands.new_flight_phase_name == "free_flight" assert apogee_trigger( - dict(flight=flight, canonical_state=_sample_state(1.0, -1.0)[1:]) + {"flight": flight, "canonical_state": _sample_state(1.0, -1.0)[1:]} ) apogee_result = apogee_callback( - dict( - flight=flight, - event=apogee_event, - time=1.0, - canonical_state=_sample_state(1.0, -1.0)[1:], - ) + { + "flight": flight, + "event": apogee_event, + "time": 1.0, + "canonical_state": _sample_state(1.0, -1.0)[1:], + } ) assert apogee_result is False assert flight.apogee_time == pytest.approx(1.0) @@ -381,14 +379,14 @@ def test_core_event_builders_update_flight_state_and_commands(): impact_state[2] = -3.0 impact_state[3] = -4.0 impact_state[6] = -4.0 - assert impact_trigger(dict(flight=flight, canonical_state=impact_state[1:])) + assert impact_trigger({"flight": flight, "canonical_state": impact_state[1:]}) impact_callback( - dict( - flight=flight, - event=impact_event, - time=2.0, - canonical_state=impact_state[1:], - ) + { + "flight": flight, + "event": impact_event, + "time": 2.0, + "canonical_state": impact_state[1:], + } ) assert flight.impact_time == pytest.approx(2.0) assert flight.x_impact == pytest.approx(2.0) @@ -398,19 +396,17 @@ def test_core_event_builders_update_flight_state_and_commands(): assert impact_event.commands._terminate is True assert out_of_rail_exact_time_function( - dict(canonical_state=out_of_rail_state[1:], flight=flight) + {"canonical_state": out_of_rail_state[1:], "flight": flight} ) == pytest.approx(0.0) assert out_of_rail_exact_time_derivative( - dict(canonical_state=out_of_rail_state[1:], flight=flight) + {"canonical_state": out_of_rail_state[1:], "flight": flight} ) == pytest.approx(0.0) assert apogee_event_exact_time_function( - dict(canonical_state=_sample_state(1.0, -1.0)[1:]) + {"canonical_state": _sample_state(1.0, -1.0)[1:]} ) == pytest.approx(-1.0) - assert impact_event_exact_time_function(dict(height_agl=-4.0)) == pytest.approx( - -4.0 - ) + assert impact_event_exact_time_function({"height_agl": -4.0}) == pytest.approx(-4.0) assert impact_event_exact_time_derivative( - dict(canonical_state=impact_state[1:], flight=flight) + {"canonical_state": impact_state[1:], "flight": flight} ) == pytest.approx(-4.0) @@ -431,7 +427,7 @@ def test_apogee_trigger_returns_false_without_complete_history( assert ( apogee_trigger( - dict(flight=flight, canonical_state=_sample_state(1.0, -1.0)[1:]) + {"flight": flight, "canonical_state": _sample_state(1.0, -1.0)[1:]} ) is False ) diff --git a/tests/unit/rocket/aero_surface/test_aero_coefficient.py b/tests/unit/rocket/aero_surface/test_aero_coefficient.py index 5d20a644e..28c49cc5a 100644 --- a/tests/unit/rocket/aero_surface/test_aero_coefficient.py +++ b/tests/unit/rocket/aero_surface/test_aero_coefficient.py @@ -219,7 +219,7 @@ def test_infer_single_var_unmatched_label_gives_none(): def test_infer_single_var_missing_inputs_gives_none(): class NoInputs: - pass + """An object with no inputs to read a variable name from.""" assert AeroCoefficient._infer_single_var(NoInputs(), IV) is None diff --git a/tests/unit/rocket/aero_surface/test_barrowman_generic_equivalence.py b/tests/unit/rocket/aero_surface/test_barrowman_generic_equivalence.py index 57a0777c1..04ae01a86 100644 --- a/tests/unit/rocket/aero_surface/test_barrowman_generic_equivalence.py +++ b/tests/unit/rocket/aero_surface/test_barrowman_generic_equivalence.py @@ -22,6 +22,8 @@ TrapezoidalFins, ) from rocketpy.mathutils import Vector +from rocketpy.rocket.aero_surface._barrowman_surface import _BarrowmanSurface +from rocketpy.rocket.aero_surface.generic_surface import GenericSurface def test_barrowman_derived_cp_matches_geometric_cp(): @@ -158,8 +160,6 @@ def test_barrowman_surface_uses_geometric_compute_path(): Barrowman method (their own ``compute_forces_and_moments``): the resultant force is reported at the geometric center of pressure and its moment is transported geometrically from there.""" - from rocketpy.rocket.aero_surface._barrowman_surface import _BarrowmanSurface - from rocketpy.rocket.aero_surface.generic_surface import GenericSurface nose = NoseCone( length=0.55829, kind="vonkarman", base_radius=0.0635, rocket_radius=0.0635 diff --git a/tests/unit/rocket/aero_surface/test_controllable_generic_surface.py b/tests/unit/rocket/aero_surface/test_controllable_generic_surface.py index 5aebe3917..49b9b5167 100644 --- a/tests/unit/rocket/aero_surface/test_controllable_generic_surface.py +++ b/tests/unit/rocket/aero_surface/test_controllable_generic_surface.py @@ -12,7 +12,7 @@ def _moment_at_deflection(surface, deflection, comp="pitch"): surface.set_control("deflection", deflection) - r1, r2, r3, m1, m2, m3 = surface.compute_forces_and_moments( + _, _, _, m1, m2, m3 = surface.compute_forces_and_moments( Vector([0, 0, -100]), 100, 0.29, diff --git a/tests/unit/rocket/aero_surface/test_generic_surfaces.py b/tests/unit/rocket/aero_surface/test_generic_surfaces.py index 31c0a5a16..1dbcca1e6 100644 --- a/tests/unit/rocket/aero_surface/test_generic_surfaces.py +++ b/tests/unit/rocket/aero_surface/test_generic_surfaces.py @@ -5,9 +5,16 @@ import numpy as np import pytest -from rocketpy import Function, GenericSurface, LinearGenericSurface +from rocketpy import ( + AeroCoefficient, + Function, + GenericSurface, + LinearGenericSurface, + Rocket, +) from rocketpy._encoders import RocketPyDecoder, RocketPyEncoder from rocketpy.mathutils import Vector +from rocketpy.rocket.aero_surface._helpers import total_angle_and_roll def _rpy_round_trip(obj): @@ -1135,7 +1142,6 @@ def _area_length(): def test_a_one_argument_function_is_read_by_its_name(): """The Mach hint for unnamed one-input sources must not override a name that is a variable; a name that is not one still means Mach.""" - from rocketpy import AeroCoefficient assert AeroCoefficient(lambda alpha: alpha, single_var="mach").depends_on == ( "alpha", @@ -1252,7 +1258,6 @@ def test_wind_and_total_angle_tables_round_trip_without_pickling(): def test_setting_the_center_of_pressure_updates_the_rocket(): - from rocketpy import Rocket surface = GenericSurface(*_area_length(), {"cN": lambda alpha: 2 * alpha}) rocket = Rocket( @@ -1378,7 +1383,6 @@ def test_converted_coefficients_accept_arrays(kind): def test_roll_angle_of_the_wind_is_zero_flying_exactly_tail_first(): - from rocketpy.rocket.aero_surface._helpers import total_angle_and_roll assert total_angle_and_roll(np.pi, np.pi) == pytest.approx((np.pi, 0.0)) diff --git a/tests/unit/rocket/aero_surface/test_individual_fins.py b/tests/unit/rocket/aero_surface/test_individual_fins.py index fa1d0ef0c..ab0a52917 100644 --- a/tests/unit/rocket/aero_surface/test_individual_fins.py +++ b/tests/unit/rocket/aero_surface/test_individual_fins.py @@ -397,12 +397,20 @@ def test_calisto_finset_vs_four_individual_fins_close(): [ ( TrapezoidalFin, - dict(root_chord=0.120, tip_chord=0.040, span=0.100, rocket_radius=0.0635), + { + "root_chord": 0.120, + "tip_chord": 0.040, + "span": 0.100, + "rocket_radius": 0.0635, + }, ), - (EllipticalFin, dict(root_chord=0.120, span=0.100, rocket_radius=0.0635)), + (EllipticalFin, {"root_chord": 0.120, "span": 0.100, "rocket_radius": 0.0635}), ( FreeFormFin, - dict(shape_points=[(0, 0), (0.06, 0.1), (0.12, 0.0)], rocket_radius=0.0635), + { + "shape_points": [(0, 0), (0.06, 0.1), (0.12, 0.0)], + "rocket_radius": 0.0635, + }, ), ], ) @@ -423,12 +431,20 @@ def test_canted_individual_fin_builds_and_places(fin_cls, geometry): [ ( TrapezoidalFin, - dict(root_chord=0.120, tip_chord=0.040, span=0.100, rocket_radius=0.0635), + { + "root_chord": 0.120, + "tip_chord": 0.040, + "span": 0.100, + "rocket_radius": 0.0635, + }, ), - (EllipticalFin, dict(root_chord=0.120, span=0.100, rocket_radius=0.0635)), + (EllipticalFin, {"root_chord": 0.120, "span": 0.100, "rocket_radius": 0.0635}), ( FreeFormFin, - dict(shape_points=[(0, 0), (0.06, 0.1), (0.12, 0.0)], rocket_radius=0.0635), + { + "shape_points": [(0, 0), (0.06, 0.1), (0.12, 0.0)], + "rocket_radius": 0.0635, + }, ), ], ) @@ -470,17 +486,25 @@ def test_individual_fin_roll_moment_independent_of_angular_position(fin_cls, geo ( TrapezoidalFins, TrapezoidalFin, - dict(root_chord=0.120, tip_chord=0.040, span=0.100, rocket_radius=0.0635), + { + "root_chord": 0.120, + "tip_chord": 0.040, + "span": 0.100, + "rocket_radius": 0.0635, + }, ), ( EllipticalFins, EllipticalFin, - dict(root_chord=0.120, span=0.100, rocket_radius=0.0635), + {"root_chord": 0.120, "span": 0.100, "rocket_radius": 0.0635}, ), ( FreeFormFins, FreeFormFin, - dict(shape_points=[(0, 0), (0.06, 0.1), (0.12, 0.0)], rocket_radius=0.0635), + { + "shape_points": [(0, 0), (0.06, 0.1), (0.12, 0.0)], + "rocket_radius": 0.0635, + }, ), ], ) diff --git a/tests/unit/rocket/aero_surface/test_surface_coefficient_completeness.py b/tests/unit/rocket/aero_surface/test_surface_coefficient_completeness.py index 04760fbc9..f0df58bb0 100644 --- a/tests/unit/rocket/aero_surface/test_surface_coefficient_completeness.py +++ b/tests/unit/rocket/aero_surface/test_surface_coefficient_completeness.py @@ -154,8 +154,6 @@ def test_center_of_pressure_accessors(name): def test_body_input_is_recovered_by_body_accessors(): """Coefficients supplied in the body frame are recovered by the body-frame accessors (they round-trip through the canonical wind-frame storage).""" - from rocketpy import GenericSurface - surface = GenericSurface( reference_area=0.01, reference_length=0.1, @@ -174,8 +172,6 @@ def test_body_input_is_recovered_by_body_accessors(): def test_wind_and_body_input_agree_at_zero_angle(): """cL == cN, cD == cA and cQ == cY at zero angle of attack and sideslip, regardless of the frame the coefficients were supplied in.""" - from rocketpy import GenericSurface - wind = GenericSurface( reference_area=0.01, reference_length=0.1, @@ -196,8 +192,6 @@ def test_wind_and_body_input_agree_at_zero_angle(): def test_mixed_frame_input_raises(): """Supplying both wind and body force coefficients without declaring the frame is rejected.""" - from rocketpy import GenericSurface - with pytest.raises(ValueError, match="[Mm]ixed"): GenericSurface( reference_area=0.01, diff --git a/tests/unit/rocket/test_generic_calisto_equivalence.py b/tests/unit/rocket/test_generic_calisto_equivalence.py index 0882a5111..a86fe2e56 100644 --- a/tests/unit/rocket/test_generic_calisto_equivalence.py +++ b/tests/unit/rocket/test_generic_calisto_equivalence.py @@ -9,7 +9,6 @@ comparison lives in ``tests/unit/simulation/test_flight.py``. """ -import numpy as np import pytest GENERIC_FIXTURES = [ diff --git a/tests/unit/rocket/test_rocket.py b/tests/unit/rocket/test_rocket.py index 9b088fd54..79251020e 100644 --- a/tests/unit/rocket/test_rocket.py +++ b/tests/unit/rocket/test_rocket.py @@ -1,3 +1,4 @@ +import copy import json import warnings from itertools import product @@ -17,7 +18,6 @@ ) from rocketpy._encoders import RocketPyDecoder, RocketPyEncoder from rocketpy.mathutils.vector_matrix import Vector -from rocketpy.rocket._helpers import summed_force_and_moment from rocketpy.motors.empty_motor import EmptyMotor from rocketpy.motors.motor import Motor @@ -235,7 +235,6 @@ def test_asymmetry_warning_is_shown_once_per_configuration(calisto): def test_a_canted_fin_moves_its_leading_edge(calisto): """Canting a fin already on the rocket places it as if it were added canted: the position the user gave is kept, the lever arm follows the cant.""" - import copy geometry = {"root_chord": 0.12, "tip_chord": 0.04, "span": 0.1} fin = TrapezoidalFin(0, rocket_radius=0.0635, **geometry) diff --git a/tests/unit/rocket/test_stability_rework.py b/tests/unit/rocket/test_stability_rework.py index 05b7becb2..9844bf270 100644 --- a/tests/unit/rocket/test_stability_rework.py +++ b/tests/unit/rocket/test_stability_rework.py @@ -19,18 +19,18 @@ Rocket, ) from rocketpy._encoders import RocketPyDecoder, RocketPyEncoder -from rocketpy.rocket.aero_surface.aero_coefficient import AeroCoefficient from rocketpy.rocket._helpers import ( aerodynamic_damping, corrective_and_damping_moments, - lateral_inertia_and_rate, - stability_margin_and_slope, - stability_surfaces, damping_derivative, is_incidence_linear, + lateral_inertia_and_rate, neutral_point_and_slope, + stability_margin_and_slope, + stability_surfaces, summed_force_and_moment, ) +from rocketpy.rocket.aero_surface.aero_coefficient import AeroCoefficient from rocketpy.rocket.aero_surface.fins.trapezoidal_fin import TrapezoidalFin @@ -862,9 +862,9 @@ def _lumped_twin(rocket, force_convention="body"): power_on_drag=0, center_of_mass_without_motor=rocket.center_of_mass_without_motor, ) - power_off, _ = rocket.to_surface( + power_off = rocket.to_surface( machs=[0.2, 0.3, 0.4], force_convention=force_convention - ) + )[0] twin.add_full_body_aerodynamics( power_off, position=rocket.center_of_dry_mass_position ) @@ -998,9 +998,9 @@ def test_lumping_follows_the_reynolds_number(model): at_zero + 7.0, rel=1e-6 ) - power_off, _ = rocket.to_surface( + power_off = rocket.to_surface( machs=machs, model=model, angles=angles, reynolds=[1e5, 1e6, 1e7] - ) + )[0] for reynolds in (1e5, 1e6, 1e7): expected = _source_coefficients(rocket, alpha, 0.5, reynolds)[0] lumped = power_off.cN(alpha, 0, 0.5, reynolds, 0, 0, 0) @@ -1036,9 +1036,9 @@ def test_lumping_keeps_a_control(): ) assert canard.get_control("deflection") == 0.02 - power_off, _ = rocket.to_surface( + power_off = rocket.to_surface( machs=machs, model="table", angles=angles, controls=controls - ) + )[0] assert isinstance(power_off, ControllableGenericSurface) assert power_off.control_variables == ["deflection"] alpha, deflection = math.radians(3), math.radians(5) @@ -1114,7 +1114,7 @@ def _table_twin(rocket, **kwargs): power_on_drag=0, center_of_mass_without_motor=rocket.center_of_mass_without_motor, ) - power_off, _ = rocket.to_surface(model="table", machs=[0.2, 0.3, 0.4], **kwargs) + power_off = rocket.to_surface(model="table", machs=[0.2, 0.3, 0.4], **kwargs)[0] twin.add_full_body_aerodynamics( power_off, position=rocket.center_of_dry_mass_position ) @@ -1238,9 +1238,9 @@ def test_lumping_rejects_bad_options(): def test_table_surface_saves_and_loads(): """The table surfaces round trip through the RocketPy encoder.""" rocket = _canted_calisto() - power_off, _ = rocket.to_surface( + power_off = rocket.to_surface( model="table", machs=[0.2, 0.3], angles=np.radians([-10, 0, 10]) - ) + )[0] loaded = json.loads(json.dumps(power_off, cls=RocketPyEncoder), cls=RocketPyDecoder) args = (math.radians(6), math.radians(-4), 0.25, 0.0, 0.1, 0.0, 0.2) for name in ("cN", "cY", "cA", "cm", "cn", "cl"): @@ -1388,7 +1388,7 @@ def test_axisymmetric_nonlinear_yaw_is_read_across_the_wind(): theta, mach = math.radians(10), 0.3 diameter = 2 * rocket.radius - r1, r2, _, m1, _, _ = summed_force_and_moment(rocket, theta, 0.0, mach, (0, 0, 0)) + _, r2, _, m1, _, _ = summed_force_and_moment(rocket, theta, 0.0, mach, (0, 0, 0)) dynamic_pressure_area = 0.5 * rocket.area cN = -r2 / dynamic_pressure_area cm = m1 / (dynamic_pressure_area * diameter) diff --git a/tests/unit/simulation/test_dynamics.py b/tests/unit/simulation/test_dynamics.py index 13e53fea7..6da403cdf 100644 --- a/tests/unit/simulation/test_dynamics.py +++ b/tests/unit/simulation/test_dynamics.py @@ -119,7 +119,7 @@ def heading_to_canonical(values): return [values[name] for name in CANONICAL_STATE_NAMES] -def heading_to_canonical_dot(values, values_dot): +def heading_to_canonical_dot(_values, values_dot): result = [0.0] * 13 for name, value in values_dot.items(): if name in CANONICAL_STATE_NAMES: @@ -206,7 +206,7 @@ def test_bound_dynamics_post_process_at_returns_reported_variables(): def test_bound_dynamics_forwards_phase_arguments(): """A phase argument fixed at bind time reaches both call paths.""" - def with_extra(flight, t, u, post_processing=False, *, parachute): + def with_extra(flight, t, _u, post_processing=False, *, parachute): flight.calls.append((t, post_processing, parachute)) return [parachute] @@ -239,7 +239,7 @@ def test_bound_dynamics_default_initial_state(): def test_bound_dynamics_custom_initial_state(): - def seed(flight, t, canonical_state): + def seed(_flight, _t, canonical_state): return [canonical_state[2]] # only altitude dynamics = _PhaseDynamics( @@ -432,7 +432,7 @@ def test_unknown_post_process_variable_raises(): ) flight.solution._append([0.0, *[0.0] * 6]) with pytest.raises(KeyError, match="No flight phase computed"): - flight.solution.post["nope"] + _ = flight.solution.post["nope"] def test_a_phase_with_no_live_dynamics_is_skipped(): @@ -443,7 +443,7 @@ def test_a_phase_with_no_live_dynamics_is_skipped(): # The variable is one the phase declares, so the error says why it cannot be # produced rather than claiming the flight never computes it. with pytest.raises(KeyError, match="read back from a saved file"): - flight.solution.post["ax"] + _ = flight.solution.post["ax"] @pytest.mark.parametrize("name", FULL_POST_PROCESS_VARS) diff --git a/tests/unit/simulation/test_event.py b/tests/unit/simulation/test_event.py index 8803cf0f7..dd8456769 100644 --- a/tests/unit/simulation/test_event.py +++ b/tests/unit/simulation/test_event.py @@ -843,9 +843,8 @@ def test_sample_on_a_step_boundary_is_checked_exactly_once(): genuinely coincides with a check. """ # imported here only to keep Flight out of this module's import graph - from rocketpy.simulation.flight import ( - Flight, # pylint: disable=import-outside-toplevel - ) + # pylint: disable-next=import-outside-toplevel + from rocketpy.simulation.flight import Flight checked = [] @@ -1240,7 +1239,8 @@ def test_an_exact_time_event_does_not_rewrite_the_shared_context(): seen = {} event = Event( - callback=lambda context: seen.update(context), + # a lambda: Event reads the signature, which dict.update does not have + callback=lambda context: seen.update(context), # pylint: disable=unnecessary-lambda trigger=_always_true, name="refined", exact_time_function=lambda context: context["state"][5], @@ -1273,7 +1273,8 @@ def altitude_interpolator(time): seen = {} event = Event( - callback=lambda context: seen.update(context), + # a lambda: Event reads the signature, which dict.update does not have + callback=lambda context: seen.update(context), # pylint: disable=unnecessary-lambda trigger=_always_true, exact_time_function=lambda context: context["height_agl"], exact_time_config={"target": 4.0}, diff --git a/tests/unit/simulation/test_flight.py b/tests/unit/simulation/test_flight.py index 3ea7472d9..12bbf88df 100644 --- a/tests/unit/simulation/test_flight.py +++ b/tests/unit/simulation/test_flight.py @@ -9,6 +9,7 @@ from scipy import optimize from rocketpy import Components, Flight, Function, LinearGenericSurface, Rocket +from rocketpy.mathutils.vector_matrix import Vector from rocketpy.rocket._helpers import aerodynamic_damping from rocketpy.simulation.helpers.flight_derivatives import u_dot, u_dot_generalized @@ -724,13 +725,13 @@ def test_generic_surface_calisto_flight_matches_barrowman( """ generic_rocket = request.getfixturevalue(generic_rocket_name) - launch = dict( - environment=example_plain_env, - rail_length=5.2, - inclination=85, - heading=0, - terminate_on_apogee=True, - ) + launch = { + "environment": example_plain_env, + "rail_length": 5.2, + "inclination": 85, + "heading": 0, + "terminate_on_apogee": True, + } reference_flight = Flight(rocket=calisto_robust, **launch) generic_flight = Flight(rocket=generic_rocket, **launch) @@ -910,7 +911,6 @@ def _rigid_burning_calisto( def _center_of_mass_inertia(rocket, t): """Position of the center of mass relative to the center of dry mass in the body frame, and the inertia tensor about it.""" - from rocketpy.mathutils.vector_matrix import Vector # pylint: disable=import-outside-toplevel r_cm = Vector([0, 0, -rocket.com_to_cdm_function.get_value_opt(t)]) mass = rocket.total_mass.get_value_opt(t) @@ -939,7 +939,6 @@ def test_dynamics_take_moments_about_the_center_of_mass( ``M + R x r_cm``, not the untransferred moment (legacy) nor the transfer the other way (generalized, before the fix). """ - from rocketpy.mathutils.vector_matrix import Vector # pylint: disable=import-outside-toplevel rocket = _rigid_burning_calisto( calisto_motorless, calisto_nose_cone, calisto_tail, calisto_trapezoidal_fins @@ -980,7 +979,6 @@ def test_generalized_dynamics_match_a_rigid_body_when_rotating( center of mass, ``I_cm w_dot = M_cm - w x (I_cm w)``, and the acceleration of the center of dry mass follows from the center of mass' one, ``a_O = F / m - w_dot x r_cm - w x (w x r_cm)``.""" - from rocketpy.mathutils.vector_matrix import Vector # pylint: disable=import-outside-toplevel rocket = _rigid_burning_calisto( calisto_motorless, calisto_nose_cone, calisto_tail, calisto_trapezoidal_fins @@ -1027,7 +1025,6 @@ def test_integrator_damping_matches_the_oscillator(flight_calisto_robust): Thomson's form), once the rate is taken about the same point: the state rotates about the center of dry mass, ``a`` ahead of the center of mass, which adds ``C1 a / V`` of angle-of-attack coupling.""" - from rocketpy.mathutils.vector_matrix import Vector # pylint: disable=import-outside-toplevel flight = flight_calisto_robust rocket = flight.rocket diff --git a/tests/unit/simulation/test_solution.py b/tests/unit/simulation/test_solution.py index a84e41e44..df6b886a0 100644 --- a/tests/unit/simulation/test_solution.py +++ b/tests/unit/simulation/test_solution.py @@ -13,7 +13,6 @@ CANONICAL_INDEX, CANONICAL_STATE_NAMES, FULL_POST_PROCESS_VARS, - PARACHUTE_DYNAMICS, SIX_DOF_DYNAMICS, _PhaseDynamics, ) @@ -32,7 +31,7 @@ def descent_row(t, fill=None): return [float(t), *state] -def stub_derivative(flight, t, u, post_processing=False): +def stub_derivative(_flight, t, u, post_processing=False): """Stand-in equations of motion. These tests store rows, never integrate.""" return [t] if post_processing else list(u) @@ -719,7 +718,7 @@ def test_post_values_stay_the_same_length_as_the_rows(): lambda: solution._insert(0, canonical_row(-1)), lambda: solution._pop(0), lambda: solution._insert_before_last(descent_row(5.5)), - lambda: solution._drop_last(), + solution._drop_last, lambda: solution._set_row(-1, descent_row(9)), ): mutate() @@ -785,7 +784,7 @@ def test_value_at_unknown_state_raises(): # --------------------------------------------------------------------------- -def replay_derivative(flight, t, u, post_processing=False): +def replay_derivative(_flight, t, u, post_processing=False): """Report values that say which row they came from, so replay is visible.""" return [t, 2 * t, 3 * t] if post_processing else list(u) @@ -798,7 +797,7 @@ def replay_derivative(flight, t, u, post_processing=False): ) -def replay_thrust_derivative(flight, t, u, post_processing=False): +def replay_thrust_derivative(_flight, t, u, post_processing=False): """Like :func:`replay_derivative`, for a phase that also reports thrust.""" return [t, 2 * t, 3 * t, 100.0] if post_processing else list(u) @@ -871,7 +870,7 @@ def test_a_gap_in_a_recording_flight_is_an_error(): solution.records_post_values = True solution._set_post_values(0, [1.0, 2.0, 3.0]) with pytest.raises(ValueError, match="row 1 has none"): - solution.post["ax"] + _ = solution.post["ax"] with pytest.raises(ValueError, match="row 1 has none"): solution.post.at_index(1) @@ -950,7 +949,7 @@ def test_phase_values_gives_one_phase_at_a_time(): def test_an_unknown_variable_says_what_the_flight_computes(): solution = build_replay_solution() with pytest.raises(KeyError, match="No flight phase computed"): - solution.post["not_a_variable"] + _ = solution.post["not_a_variable"] def test_a_solution_read_back_from_a_file_cannot_report_them(): @@ -961,7 +960,7 @@ def test_a_solution_read_back_from_a_file_cannot_report_them(): ) solution._append(descent_row(0)) with pytest.raises(KeyError, match="read back from a saved file"): - solution.post["ax"] + _ = solution.post["ax"] with pytest.raises(KeyError, match="read back from a saved file"): solution.post.at_index(0) diff --git a/tests/unit/stochastic/test_stochastic_rocket.py b/tests/unit/stochastic/test_stochastic_rocket.py index f900459a1..bef344cca 100644 --- a/tests/unit/stochastic/test_stochastic_rocket.py +++ b/tests/unit/stochastic/test_stochastic_rocket.py @@ -1,5 +1,12 @@ import pytest +from rocketpy import ( + FreeFormFins, + GenericSurface, + StochasticNoseCone, + StochasticRocket, + TrapezoidalFin, +) from rocketpy.rocket.rocket import Rocket @@ -33,8 +40,6 @@ def test_zero_dispersion_keeps_the_full_drag_and_the_stability_phase( """A drag that depends on more than Mach, the stability phase and a generic surface on the rocket survive the stochastic mirror unchanged, and the drag factor scales the whole coefficient.""" - from rocketpy import GenericSurface - from rocketpy.stochastic import StochasticRocket rocket = calisto_robust table = [[a, m, 0.4 + a**2 + 0.1 * m] for a in (-0.3, 0, 0.3) for m in (0, 1, 2)] @@ -50,7 +55,9 @@ def test_zero_dispersion_keeps_the_full_drag_and_the_stability_phase( created = stochastic.create_object() assert created.power_off_drag_7d(*state) == rocket.power_off_drag_7d(*state) assert created.stability_phase == "power_on" - assert any(type(s) is GenericSurface for s, _ in created.aerodynamic_surfaces) + # exactly a GenericSurface: every other surface on the rocket is a subclass + plain = [s for s, _ in created.aerodynamic_surfaces if type(s) is GenericSurface] # pylint: disable=unidiomatic-typecheck + assert plain scaled = StochasticRocket(rocket=rocket, power_off_drag_factor=(1.2, 0)) scaled.add_motor(cesaroni_m1670, position=(-1.255, 0)) @@ -61,13 +68,6 @@ def test_zero_dispersion_keeps_the_full_drag_and_the_stability_phase( def test_surfaces_with_no_stochastic_version_are_carried_over(): """Free-form fins, individual fins and generic surfaces have no stochastic version; each generated rocket keeps them, at their position.""" - from rocketpy import ( - FreeFormFins, - GenericSurface, - StochasticNoseCone, - StochasticRocket, - TrapezoidalFin, - ) rocket = Rocket( radius=0.0635, From 8ec2c8bf8b576d3d262c0ecb331b366e4c4b11d6 Mon Sep 17 00:00:00 2001 From: MateusStano Date: Mon, 5 Oct 2026 20:10:24 -0300 Subject: [PATCH 21/22] STY: ruff update --- README.md | 12 +++++------- 1 file changed, 5 insertions(+), 7 deletions(-) diff --git a/README.md b/README.md index 8247ab1a3..f0d71acb1 100644 --- a/README.md +++ b/README.md @@ -170,10 +170,10 @@ env = Environment( tomorrow = datetime.date.today() + datetime.timedelta(days=1) env.set_date( - (tomorrow.year, tomorrow.month, tomorrow.day, 12), timezone="America/Denver" -) # Tomorrow's date in year, month, day, hour UTC format + (tomorrow.year, tomorrow.month, tomorrow.day, 12), timezone="America/Denver" +) # Tomorrow's date in year, month, day, hour UTC format -env.set_atmospheric_model(type='Forecast', file='GFS') +env.set_atmospheric_model(type="Forecast", file="GFS") ``` This can be followed up by starting a Solid Motor object. To get help on it, just use: @@ -233,9 +233,7 @@ buttons = calisto.set_rail_buttons( calisto.add_motor(Pro75M1670, position=-1.255) -nose = calisto.add_nose( - length=0.55829, kind="vonKarman", position=1.278 -) +nose = calisto.add_nose(length=0.55829, kind="vonKarman", position=1.278) fins = calisto.add_trapezoidal_fins( n=4, @@ -290,7 +288,7 @@ To actually create a Flight object, use: ```python test_flight = Flight( - rocket=calisto, environment=env, rail_length=5.2, inclination=85, heading=0 + rocket=calisto, environment=env, rail_length=5.2, inclination=85, heading=0 ) ``` From 17d32fef0bef08f691c995987e2c9a66af51abe8 Mon Sep 17 00:00:00 2001 From: MateusStano Date: Mon, 5 Oct 2026 20:32:12 -0300 Subject: [PATCH 22/22] STY: put the unused-argument disable where pylint 4.1 reads it Co-Authored-By: Claude Opus 5.5 --- rocketpy/rocket/aero_surface/_barrowman_surface.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/rocketpy/rocket/aero_surface/_barrowman_surface.py b/rocketpy/rocket/aero_surface/_barrowman_surface.py index a461f5ee1..c614fc384 100644 --- a/rocketpy/rocket/aero_surface/_barrowman_surface.py +++ b/rocketpy/rocket/aero_surface/_barrowman_surface.py @@ -147,7 +147,7 @@ def evaluate_coefficients(self): "cl", ) - def compute_forces_and_moments( + def compute_forces_and_moments( # pylint: disable=unused-argument self, stream_velocity, stream_speed, @@ -155,7 +155,7 @@ def compute_forces_and_moments( rho, cp, omega, - *args, # pylint: disable=unused-argument + *args, ): """Compute the surface's forces and moments with the classic Barrowman method. Called at each simulation step.