diff --git a/.github/workflows/pytest.yml b/.github/workflows/pytest.yml index 1969924..66aa75c 100644 --- a/.github/workflows/pytest.yml +++ b/.github/workflows/pytest.yml @@ -2,6 +2,8 @@ name: Pytest on: push: + branches: + - master pull_request: branches: - master @@ -13,7 +15,7 @@ jobs: strategy: fail-fast: false matrix: - python-version: ["3.9", "3.10", "3.11", "3.12", "3.13"] + python-version: ["3.10", "3.11", "3.12", "3.13", "3.14"] steps: - name: Checkout code diff --git a/.gitignore b/.gitignore index e626d6f..e56a54c 100644 --- a/.gitignore +++ b/.gitignore @@ -1,6 +1,7 @@ # Created by https://www.toptal.com/developers/gitignore/api/python # Edit at https://www.toptal.com/developers/gitignore?templates=python - +.DS_Store +Validation/ fig fig* diff --git a/MITRotor/CachedLUT.py b/MITRotor/CachedLUT.py new file mode 100644 index 0000000..0569c3b --- /dev/null +++ b/MITRotor/CachedLUT.py @@ -0,0 +1,160 @@ +from abc import ABC, abstractmethod +from pathlib import Path +import numpy as np +import polars as pl +from scipy.interpolate import RectBivariateSpline + +class CachedLUT(ABC): + """ + Abstract base class for a Cached Look-Up Table (LUT) with interpolation capabilities. + + Attributes: + ---------- + key1 : str + First key used for table indexing. + key2 : str + Second key used for table indexing. + to_interp : list[str] + List of column names in the table to perform interpolation on. + cache_fn : Path + Path to the CSV file used for caching the table data. + regenerate : bool, optional + Flag indicating whether to regenerate the table even if cache file exists (default is False). + s : float, optional + Smoothing factor used in interpolation (default is 0.001). + + Methods: + -------- + load() -> pl.DataFrame: + Load the cached table data from the CSV file. + + save() -> pl.DataFrame: + Save the current table data to the CSV file. + + _make_interpolator_on(on: str) -> RectBivariateSpline: + Create a 2D interpolation function for a specific column ('on') in the table. + + make_interpolators() -> dict[str, RectBivariateSpline]: + Create interpolation functions for all columns specified in 'to_interp'. + + generate_table() -> pl.DataFrame: + Abstract method to generate the look-up table. To be implemented in subclasses. + """ + + def __init__( + self, + key1: str, + key2: str, + to_interp: list[str], + cache_fn: Path, + regenerate: bool = False, + s=0.001, + ): + """ + Initialize CachedLUT with specified parameters. + + Parameters: + ----------- + key1 : str + First key used for table indexing. + key2 : str + Second key used for table indexing. + to_interp : list[str] + List of column names in the table to perform interpolation on. + cache_fn : Path + Path to the CSV file used for caching the table data. + regenerate : bool, optional + Flag indicating whether to regenerate the table even if cache file exists (default is False). + s : float, optional + Smoothing factor used in interpolation (default is 0.001). + """ + self.key1 = key1 + self.key2 = key2 + self.to_interp = to_interp + self.cache_fn = cache_fn + self.s = s + + if cache_fn.exists() and not regenerate: + self.df = self.load() + else: + self.df = self.generate_table() + self.save() + + self.interpolators = self.make_interpolators() + + @abstractmethod + def generate_table(self) -> pl.DataFrame: + """ + Abstract method to generate the look-up table. + + This method should be implemented in subclasses to define how the table + is generated based on specific requirements. + + Returns: + -------- + pl.DataFrame: + The generated look-up table. + """ + pass + + def load(self) -> pl.DataFrame: + """ + Load the cached table data from the CSV file. + + Returns: + -------- + pl.DataFrame: + The loaded DataFrame from the CSV. + """ + return pl.read_csv(self.cache_fn) + + def save(self) -> None: + """ + Save the current table data to the CSV file. + + Returns: + -------- + None + """ + self.df.write_csv(self.cache_fn) + + def _make_interpolator_on(self, on: str) -> RectBivariateSpline: + """ + Create a 2D interpolation function for a specific column ('on') in the table. + + Parameters: + ----------- + on : str + Column name for which to create the interpolation function. + + Returns: + -------- + RectBivariateSpline: + Interpolation function for the specified column. + """ + df_piv = ( + self.df.sort(self.key1, self.key2) + .pivot(index=self.key2, columns=self.key1, values=on, maintain_order=True) + .sort(self.key2) + ) + + x = np.array(df_piv.columns[1:], dtype=float).copy() + y = df_piv[self.key2].to_numpy().copy() + z = df_piv.to_numpy()[:, 1:].copy() + + interp = RectBivariateSpline(y, x, z, s=self.s) + return interp + + def make_interpolators(self) -> dict[str, RectBivariateSpline]: + """ + Create interpolation functions for all columns specified in 'to_interp'. + + Returns: + -------- + dict[str, RectBivariateSpline]: + Dictionary where keys are column names and values are interpolation functions. + """ + interpolators = {} + for key in self.to_interp: + interpolators[key] = self._make_interpolator_on(key) + return interpolators diff --git a/MITRotor/Geometry.py b/MITRotor/Geometry.py index 09cac9d..8a00144 100644 --- a/MITRotor/Geometry.py +++ b/MITRotor/Geometry.py @@ -10,7 +10,7 @@ def __init__(self, Nr, Ntheta): self.Nr = Nr self.Ntheta = Ntheta - self.mu = np.linspace(0.0, 0.99999, Nr) + self.mu = np.linspace(0.0, 0.9999, Nr) self.theta = np.linspace(0.0, 2 * np.pi, Ntheta) self.theta_mesh, self.mu_mesh = np.meshgrid(self.theta, self.mu) @@ -42,12 +42,12 @@ def cartesian(self, yaw: float, tilt: float) -> Tuple[ArrayLike, ...]: return X, Y, Z def annulus_average(self, X: ArrayLike): - X_azim = 1 / (2 * np.pi) * np.trapz(X, self.theta_mesh, axis=-1) + X_azim = 1 / (2 * np.pi) * np.trapezoid(X, self.theta_mesh, axis=-1) return X_azim def rotor_average(self, X: ArrayLike): # Takes annulus average quantities and performs rotor average - X_rotor = 2 * np.trapz(X * self.mu, self.mu) + X_rotor = 2 * np.trapezoid(X * self.mu, self.mu) return X_rotor diff --git a/MITRotor/Momentum.py b/MITRotor/Momentum.py index 7f1dd1a..6fc4646 100644 --- a/MITRotor/Momentum.py +++ b/MITRotor/Momentum.py @@ -1,9 +1,13 @@ from abc import ABC, abstractmethod from typing import TYPE_CHECKING, Literal import numpy as np +import polars as pl +import itertools from numpy.typing import ArrayLike - +from MITRotor.CachedLUT import CachedLUT +from pathlib import Path from UnifiedMomentumModel import Momentum as UMM +from UnifiedMomentumModel.Utilities.Geometry import calc_eff_yaw, eff_yaw_inv_rotation if TYPE_CHECKING: from .Geometry import BEMGeometry @@ -42,7 +46,6 @@ def compute_induction(self, Cx: ArrayLike, yaw: float = 0, tilt:float = 0) -> Ar def compute_initial_wake_velocities(self, Ct: float, yaw: float = 0, tilt: float = 0.0) -> ArrayLike: ... - def _func_rotor( self, @@ -81,7 +84,7 @@ def _func_annulus( annulus_avg_axial_force = ( geom.annulus_average( - np.clip(aero_props.C_x_corr, 0, 1.69) + np.clip(aero_props.C_x_corr, -10, 10) ) )[:, None] * np.ones(geom.shape) @@ -98,7 +101,7 @@ def _func_sector( geom: "BEMGeometry", tilt: float = 0.0, ) -> ArrayLike: - axial_force = np.clip(aero_props.C_x_corr, 0, 1.69) + axial_force = np.clip(aero_props.C_x_corr, -10, 10) return self.compute_induction(axial_force, yaw = yaw, tilt = tilt) @@ -119,6 +122,7 @@ def __call__( class ConstantInduction(MomentumModel): def __init__(self, a = 1/3): self.a = a + self._func = self._func_rotor def compute_induction(self, Cx, yaw, tilt = 0) -> ArrayLike: if tilt != 0: @@ -149,7 +153,9 @@ def __init__(self, averaging: Literal["sector", "annulus", "rotor"] = "rotor"): def compute_induction(self, Cx, yaw, tilt = 0): if tilt != 0: raise ValueError("Tilt not supported by the ClassicalMomentum momentum model. Use UMM.") - return 0.5 * (1 - np.sqrt(1 - Cx)) + Cx = np.asarray(Cx) + sqrt_term = np.sqrt(np.where(Cx < 1, 1 - Cx, np.nan)) + return 0.5 * (1 - sqrt_term) def compute_initial_wake_velocities(self, Ct: float, yaw: float, tilt: float = 0.0) -> ArrayLike: if tilt != 0: @@ -161,6 +167,47 @@ def compute_initial_wake_velocities(self, Ct: float, yaw: float, tilt: float = 0 +class MadsenMomentum(MomentumModel): + """ + Madsen Momentum model based on 2020 paper: + https://wes.copernicus.org/articles/5/1/2020/ + """ + def __init__(self, + averaging: Literal["sector", "annulus", "rotor"] = "rotor", + cosine_exponent: bool = False): + if averaging == "rotor": + self._func = self._func_rotor + elif averaging == "annulus": + self._func = self._func_annulus + elif averaging == "sector": + self._func = self._func_sector + else: + raise ValueError(f"Averaging method {averaging} not found for MadsenMomentum model.") + self.averaging = averaging + self.cosine_exponent = cosine_exponent + + + def compute_induction(self, Cx: ArrayLike, yaw: float, tilt: float = 0.0) -> ArrayLike: + if tilt != 0: + raise ValueError("Tilt not supported by the Madsen momentum model. Use UMM.") + if self.cosine_exponent: + Ct = Cx / (np.cos(yaw)**2) + else: + Ct = Cx + + an = Ct**3 * 0.0883 + Ct**2 * 0.0586 + Ct * 0.2460 + return an + + def compute_initial_wake_velocities(self, Ct: float, yaw: float, tilt: float = 0.0) -> ArrayLike: + if tilt != 0: + raise ValueError("Tilt not supported by the Madsen momentum model. Use UMM.") + u4 = np.sqrt(np.maximum(1 - Ct, 0)) + v4 = - (1/4) * Ct * np.sin(yaw) + w4 = 0.0 + return u4, v4, w4 + + + class HeckMomentum(MomentumModel): """ Heck Momentum model based on 2023 paper: @@ -191,9 +238,8 @@ def compute_induction(self, Cx: ArrayLike, yaw: float, tilt: float = 0.0) -> Arr (1 - self.ac) ** 2 * np.sin(yaw) ** 2 + 4 ) ** 2 - a = (2 * Cx - 4 + np.sqrt(-(Cx**2) * np.sin(yaw) ** 2 - 16 * Cx + 16)) / ( - -4 + np.sqrt(-(Cx**2) * np.sin(yaw) ** 2 - 16 * Cx + 16) - ) + sqrt_term = np.sqrt(np.maximum(-(Cx**2) * np.sin(yaw) ** 2 - 16 * Cx + 16, 0)) + a = (2 * Cx - 4 + sqrt_term) / (-4 + sqrt_term + 1e-16) mask = Cx > Ctc if np.iterable(Cx): @@ -224,7 +270,7 @@ class UnifiedMomentum(MomentumModel): Note that this version takes in CT and thus uses the thrust based unified momentum model. """ - def __init__(self, averaging: Literal["sector", "annulus", "rotor"] = "rotor", beta=0.1403): + def __init__(self, averaging: Literal["sector", "annulus", "rotor"] = "rotor", beta=0.1403, model_Ct=None): self.beta = beta if averaging == "rotor": @@ -237,7 +283,10 @@ def __init__(self, averaging: Literal["sector", "annulus", "rotor"] = "rotor", b raise ValueError(f"Averaging method {averaging} not found for UnifiedMomentum model.") self.averaging = averaging - self.model_Ct = UMM.ThrustBasedUnified(beta=beta) + self.model_Ct = ( + model_Ct if model_Ct is not None + else UMM.ThrustBasedUnified(beta=beta) + ) def compute_induction(self, Cx: ArrayLike, yaw: float = 0.0, tilt: float = 0.0) -> ArrayLike: sol = self.model_Ct(Cx, yaw = yaw, tilt = tilt) @@ -248,41 +297,112 @@ def compute_initial_wake_velocities(self, Cx: ArrayLike, yaw: float = 0.0, tilt: return sol.u4, sol.v4, sol.w4 -class MadsenMomentum(MomentumModel): +# Look-up table for unified model +def func_Ct(x, beta, cached) -> dict: + Ct, eff_yaw = x + model_Ct = UMM.ThrustBasedUnified(beta = beta, cached=cached) + sol = model_Ct(Ct, np.deg2rad(np.round(eff_yaw, 2))) + return dict( + Ct=Ct, + eff_yaw=np.round(eff_yaw, 2), + Ctprime=sol.Ctprime, + Cp=sol.Cp, + an=sol.an, + u4=sol.u4, + v4=sol.v4, + w4=sol.w4, # will be 0 as no tilt in yaw-only frame + x0=sol.x0, + dp=sol.dp, + dp_NL=sol.dp_NL, + ) + +CACHE_FN_CT = Path(__file__).parent / "unified_momentum_model_Ct_table.csv" +class ThrustBasedUnifiedLUT(CachedLUT, UMM.MomentumBase): """ - Madsen Momentum model based on 2020 paper: - https://wes.copernicus.org/articles/5/1/2020/ + Note: This is NOT a BEM model!! This is the look-up-table version of + ThrustBasedUnified as implemented in the UnifiedMomentumModel library!! + + This is used as the momentum theory (actuator disk theory) part of BEM in the + UnifiedMomentumLUT MomentumModel below!!! + + - Args: + - cache_fn (Path): path to cache file + - regenerate (Bool): True if cache should regenerate each call, False otherwise + - s (float): smoothing parameter used in interpolation in CachedLUT.py + - LUT_Cts (ArrayLike): list of CT values to cache results for + - LUT_yaws (ArrayLike): list of effective yaw values (in degrees) to cache results for --> + as described in UMM documentation, yaw and tilt can be combined into an "effective yaw" + the cache uses effective yaw, so these values should cover the range of effective yaws you + want. You can use UnifiedMomentumModel.Utilities.Geometry.calc_eff_yaw to find + - beta (float, optional) - used in UMM + - cached (boolean, optional) - cache nonlinear pressure in UMM """ - def __init__(self, - averaging: Literal["sector", "annulus", "rotor"] = "rotor", - cosine_exponent: bool = False): - if averaging == "rotor": - self._func = self._func_rotor - elif averaging == "annulus": - self._func = self._func_annulus - elif averaging == "sector": - self._func = self._func_sector - else: - raise ValueError(f"Averaging method {averaging} not found for MadsenMomentum model.") - self.averaging = averaging - self.cosine_exponent = cosine_exponent + def __init__(self, cache_fn: Path = CACHE_FN_CT, regenerate=False, s=0.025, + LUT_Cts=None, LUT_yaws=None, beta=0.1403, cached=True, + ): + self._beta = beta + self._nlp_cached = cached + # set reasonable CT and eff_yaw range + self._Cts = LUT_Cts if LUT_Cts is not None else np.linspace(-1, 1.5, 100) + self._eff_yaws = LUT_yaws if LUT_yaws is not None else np.arange(-50.0, 50.1, 2.0) + # create look-up-table + super().__init__( + "eff_yaw", + "Ct", + ["Ctprime", "Cp", "an", "u4", "v4", "w4", "x0", "dp", "dp_NL"], + cache_fn, + regenerate=regenerate, + s=s, + ) + def generate_table(self) -> pl.DataFrame: + params = list(itertools.product(self._Cts, self._eff_yaws)) + # Run unified model and variations + results = [] + for param in params: # this can use the vectorization once branches are merged! + results.append(func_Ct(param, beta = self._beta, cached = self._nlp_cached)) + return pl.from_dicts(results).unique(["Ct", "eff_yaw"]) + + def __call__(self, Ct: ArrayLike, yaw: ArrayLike, tilt: ArrayLike = 0) -> UMM.MomentumSolution: + """ + - Args: + - Ct (float): thrust coefficent + - yaw (float): degrees yaw (in radians) + - tilt (float): degrees tilt (in radians) + """ + # calculate effective yaw + eff_yaw = calc_eff_yaw(yaw, tilt) + eff_yaw_deg = np.rad2deg(eff_yaw) + # rotate back into yaw-tilt frame -> has to be done here to keep LUT 2D + # see UnifiedMomentum library's Geometry file for more information + u4 = self.interpolators["u4"](Ct, eff_yaw_deg, grid=False) + v4 = self.interpolators["v4"](Ct, eff_yaw_deg, grid=False) + w4 = self.interpolators["w4"](Ct, eff_yaw_deg, grid=False) + u4, v4, w4 = eff_yaw_inv_rotation(u4, v4, w4, eff_yaw, yaw, tilt) + # create Momentum solution + return UMM.MomentumSolution( + self.interpolators["Ctprime"](Ct, eff_yaw_deg, grid=False), + yaw, + self.interpolators["an"](Ct, eff_yaw_deg, grid=False), + u4, + v4, + self.interpolators["x0"](Ct, eff_yaw_deg, grid=False), + self.interpolators["dp"](Ct, eff_yaw_deg, grid=False), + tilt = tilt, + w4 = w4, + ) - def compute_induction(self, Cx: ArrayLike, yaw: float, tilt: float = 0.0) -> ArrayLike: - if tilt != 0: - raise ValueError("Tilt not supported by the Madsen momentum model. Use UMM.") - if self.cosine_exponent: - Ct = Cx / (np.cos(yaw)**2) - else: - Ct = Cx - an = Ct**3 * 0.0883 + Ct**2 * 0.0586 + Ct * 0.2460 - return an +class UnifiedMomentumLUT(UnifiedMomentum): + """ + Note: This is a BEM model!! This is the look-up-table version of + UnifiedMomentum. It uses ThrustBasedUnifiedLUT (see above!) rather + than ThrustBasedUnified from the UnifiedMomentumModel library - def compute_initial_wake_velocities(self, Ct: float, yaw: float, tilt: float = 0.0) -> ArrayLike: - if tilt != 0: - raise ValueError("Tilt not supported by the Madsen momentum model. Use UMM.") - u4 = np.sqrt(1 - Ct) - v4 = - (1/4) * Ct * np.sin(yaw) - w4 = 0.0 - return u4, v4, w4 + Note: **kwargs can be used to pass arguments to ThrustBasedUnifiedLUT constructor. + """ + def __init__(self, averaging = "rotor", **kwargs): + super().__init__( + averaging = averaging, + model_Ct = ThrustBasedUnifiedLUT(**kwargs), + ) \ No newline at end of file diff --git a/MITRotor/__init__.py b/MITRotor/__init__.py index 9366b1e..8a3bc1e 100644 --- a/MITRotor/__init__.py +++ b/MITRotor/__init__.py @@ -2,6 +2,6 @@ from .Aerodynamics import AerodynamicProperties, AerodynamicModel, DefaultAerodynamics, KraghAerodynamics from .TangentialInduction import TangentialInductionModel, DefaultTangentialInduction, NoTangentialInduction from .TipLoss import TipLossModel, NoTipLoss, PrandtlTipLoss -from .Momentum import MomentumModel, ConstantInduction, ClassicalMomentum, HeckMomentum, UnifiedMomentum, MadsenMomentum +from .Momentum import MomentumModel, ConstantInduction, ClassicalMomentum, HeckMomentum, UnifiedMomentum, MadsenMomentum, UnifiedMomentumLUT from .BEMSolver import BEM, BEMSolution from .ReferenceTurbines import IEA15MW, IEA10MW, IEA3_4MW, NREL5MW, IEA22MW diff --git a/examples/MITRotor_quickstart.ipynb b/examples/MITRotor_quickstart.ipynb index 71288d7..fc669fd 100644 --- a/examples/MITRotor_quickstart.ipynb +++ b/examples/MITRotor_quickstart.ipynb @@ -152,6 +152,8 @@ "\n", "- `MadsenMomentum`: argument `cosine_exponent`, which is an optional exponent on the cosine term in the Madsen model [[Madsen et al., 2020](https://wes.copernicus.org/articles/5/1/2020/)].\n", "\n", + "- `UnifiedMomentumLUT`: a child class of `UnifiedMomentum` that uses a lookup table for UMM calculations. Keyword arguments can be passed through the constructor to the momentum theory model `ThrustBasedUnifiedLUT`\n", + "\n", "The `HeckMomentum` model with default arguments is provided to the `BEM` as the momentum model if the user doesn't provide one.\n", "\n", "**Note: If you wish to solve a problem with non-zero tilt, you must use the `UnifiedMomentum` model.**\n", diff --git a/examples/example_05_LUT_BEM.py b/examples/example_05_LUT_BEM.py new file mode 100644 index 0000000..4381f56 --- /dev/null +++ b/examples/example_05_LUT_BEM.py @@ -0,0 +1,189 @@ +import numpy as np +import polars as pl +import pytest +from pathlib import Path +import matplotlib.pyplot as plt +from MITRotor.Momentum import UnifiedMomentumLUT, UnifiedMomentum +from MITRotor import BEM, IEA15MW, BEMGeometry +import time + +cache_dir = Path("cache") +cache_dir.mkdir(exist_ok=True, parents=True) +cache_file = cache_dir / "lut.csv" + +figdir = Path("fig") +figdir.mkdir(exist_ok=True, parents=True) + +#------------------------------------------------------------ +# Example for UMM vs LUT UMM Momentum Model +#------------------------------------------------------------ +print("Making Cache!") +start_cache = time.perf_counter() +ref_model = UnifiedMomentum() +lut_model = UnifiedMomentumLUT( + cache_fn=cache_file, + regenerate=True, + LUT_Cts=np.linspace(-0.5,1.5,40), + LUT_yaws=np.linspace(0.0,40.1,40), +) +end_cache = time.perf_counter() +print(f"Cache created in {end_cache - start_cache} seconds!") + +Cts = np.linspace(-0.5, 1.5, 30) +yaws = np.linspace(0.0, 40.0, 30) +rad_yaws = np.deg2rad(yaws) +an_ref = np.zeros((Cts.size, yaws.size)) +an_lut = np.zeros_like(an_ref) +tilt = np.deg2rad(5) +start_ref = time.perf_counter() +for (i, Ct) in enumerate(Cts): + for (j, rad_yaw) in enumerate(rad_yaws): + an_ref[i, j] = ref_model.compute_induction(Ct, rad_yaw, tilt=tilt) +end_ref = time.perf_counter() +print(f"Ran reference UMM in {end_ref - start_ref} seconds!") + +start_lut = time.perf_counter() +for (i, Ct) in enumerate(Cts): + for (j, rad_yaw) in enumerate(rad_yaws): + an_lut[i, j] = lut_model.compute_induction(Ct, rad_yaw, tilt=tilt) +end_lut = time.perf_counter() +print(f"Ran LUT UMM in {end_lut - start_lut} seconds!") + +an_lut_masked = np.ma.masked_invalid(an_lut) +an_ref_masked = np.ma.masked_invalid(an_ref) +diff = an_lut - an_ref +diff_masked = np.ma.masked_invalid(diff) + +# Presentation-friendly typography +plt.rcParams.update({ + "font.size": 18, + "axes.titlesize": 22, + "axes.labelsize": 20, + "xtick.labelsize": 18, + "ytick.labelsize": 18, +}) + +fig, axes = plt.subplots(1, 3, figsize=(18, 6), constrained_layout=True) +for ax in axes: + ax.set_box_aspect(1) + +YAW, CT = np.meshgrid(yaws, Cts) + +# --- Shared levels for reference and LUT --- +nlevels = 20 +vmin = min(np.nanmin(an_ref), np.nanmin(an_lut)) +vmax = max(np.nanmax(an_ref), np.nanmax(an_lut)) +levels = np.linspace(vmin, vmax, nlevels) + +cmap = plt.cm.viridis.copy() +cmap.set_bad(color="lightgray") + +# Panel 1 — Reference +cf0 = axes[0].contourf(YAW, CT, an_ref_masked, levels=levels, cmap=cmap) +axes[0].set_title("Reference Model\nInduction Factor ($a_n$)") +axes[0].set_xlabel("Yaw Angle (deg)") +axes[0].set_ylabel("Thrust Coefficient ($C_t$)") + +# Panel 2 — LUT +cf1 = axes[1].contourf(YAW, CT, an_lut_masked, levels=levels, cmap=cmap) +axes[1].set_title("LUT Model\nInduction Factor ($a_n$)") +axes[1].set_xlabel("Yaw Angle (deg)") +axes[1].set_ylabel("Thrust Coefficient ($C_t$)") + +# Shared colorbar for first two +cbar = fig.colorbar(cf0, ax=axes[:2], shrink=0.9) +cbar.set_label("Induction Factor ($a_n$)") + +# --- Difference plot --- +absmax = np.nanmax(np.abs(diff)) +diff_levels = np.linspace(-absmax, absmax, nlevels) + +cf2 = axes[2].contourf(YAW, CT, diff_masked, levels=diff_levels, cmap=cmap) +axes[2].set_title("Difference\n(LUT − Reference)") +axes[2].set_xlabel("Yaw Angle (deg)") +axes[2].set_ylabel("Thrust Coefficient ($C_t$)") + +cbar_diff = fig.colorbar(cf2, ax=axes[2], shrink=0.9) +cbar_diff.set_label("Δ $a_n$") + +plt.savefig(figdir / "example_5_an_LUT_compare.png", dpi=300) + +#------------------------------------------------------------ +# Example for UMM vs LUT UMM BEM Model +#------------------------------------------------------------ + +rotor = IEA15MW() +Nr, Ntheta = 10, 20 +ref_bem = BEM(rotor=rotor, momentum_model=ref_model, geometry = BEMGeometry(Nr=Nr, Ntheta=Ntheta)) +lut_bem = BEM(rotor=rotor, momentum_model=lut_model, geometry = BEMGeometry(Nr=Nr, Ntheta=Ntheta)) + +tsrs = np.linspace(2.5, 12.5, 12) +pitches = np.linspace(0.0, 15.0, 12) +rad_pitches = np.deg2rad(pitches) +cp_ref = np.zeros((tsrs.size, pitches.size)) +cp_lut = np.zeros_like(cp_ref) + +start_ref = time.perf_counter() +for (i, tsr) in enumerate(tsrs): + for (j, rad_pitch) in enumerate(rad_pitches): + cp_ref[i, j] = ref_bem(rad_pitch, tsr, yaw = 0.0, tilt = 0.0).Cp() +end_ref = time.perf_counter() +print(f"Ran reference BEM in {end_ref - start_ref} seconds!") + +start_lut = time.perf_counter() +for (i, tsr) in enumerate(tsrs): + for (j, rad_pitch) in enumerate(rad_pitches): + cp_lut[i, j] = lut_bem(rad_pitch, tsr, yaw = 0.0, tilt = 0.0).Cp() +end_lut = time.perf_counter() +print(f"Ran LUT BEM in {end_lut - start_lut} seconds!") + +cp_lut_masked = np.ma.masked_invalid(cp_lut) +cp_ref_masked = np.ma.masked_invalid(cp_ref) +diff = cp_lut - cp_ref +diff_masked = np.ma.masked_invalid(diff) + +fig, axes = plt.subplots(1, 3, figsize=(18, 6), constrained_layout=True) +for ax in axes: + ax.set_box_aspect(1) + +PITCH, TSR = np.meshgrid(pitches, tsrs) + +# --- Shared levels for reference and LUT --- +nlevels = 20 +vmin = min(np.nanmin(cp_ref), np.nanmin(cp_lut)) +vmax = max(np.nanmax(cp_ref), np.nanmax(cp_lut)) +levels = np.linspace(vmin, vmax, nlevels) + +cmap = plt.cm.viridis.copy() +cmap.set_bad(color="lightgray") + +# Panel 1 — Reference +cf0 = axes[0].contourf(PITCH, TSR, cp_ref_masked, levels=levels, cmap=cmap) +axes[0].set_title("Reference Model\nCoefficent of Power ($C_P$)") +axes[0].set_xlabel("Pitch Angle (deg)") +axes[0].set_ylabel("TSR") + +# Panel 2 — LUT +cf1 = axes[1].contourf(PITCH, TSR, cp_lut_masked, levels=levels, cmap=cmap) +axes[1].set_title("LUT Model\nCoefficent of Power ($C_P$)") +axes[1].set_xlabel("Pitch Angle (deg)") +axes[1].set_ylabel("TSR") + +# Shared colorbar for first two +cbar = fig.colorbar(cf0, ax=axes[:2], shrink=0.9) +cbar.set_label("Power Coefficent ($C_P$)") + +# --- Difference plot --- +absmax = np.nanmax(np.abs(diff)) +diff_levels = np.linspace(-absmax, absmax, nlevels) + +cf2 = axes[2].contourf(PITCH, TSR, diff_masked, levels=diff_levels, cmap=cmap) +axes[2].set_title("Difference\n(LUT − Reference)") +axes[2].set_xlabel("Pitch Angle (deg)") +axes[2].set_ylabel("TSR") + +cbar_diff = fig.colorbar(cf2, ax=axes[2], shrink=0.9) +cbar_diff.set_label("Δ $C_P$") + +plt.savefig(figdir / "example_5_cp_LUT_compare.png", dpi=300) + diff --git a/poetry.lock 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a/pyproject.toml b/pyproject.toml index efbf767..239968e 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -7,8 +7,8 @@ readme = "README.md" packages = [{ include = "MITRotor" }] [tool.poetry.dependencies] -python = "^3.8" -numpy = ">=1.16.5" +python = ">=3.10,<4.0" +numpy = "^2.2.1" scipy = ">=1.6" unified-momentum-model = {git = "https://github.com/Howland-Lab/Unified-Momentum-Model.git"} pyyaml = "^6.0.1" diff --git a/tests/test_cachedLUT.py b/tests/test_cachedLUT.py new file mode 100644 index 0000000..961058d --- /dev/null +++ b/tests/test_cachedLUT.py @@ -0,0 +1,228 @@ +import numpy as np +import polars as pl +import pytest +from pathlib import Path + +from MITRotor.CachedLUT import CachedLUT +from MITRotor.Momentum import ThrustBasedUnifiedLUT, UnifiedMomentumLUT, UnifiedMomentum +from UnifiedMomentumModel import Momentum as UMM + + +# ------------------------------------------------------------------ +# Helpers +# ------------------------------------------------------------------ + +class DummyLUT(CachedLUT): + """Minimal concrete LUT for testing cache + interpolation.""" + calls = 0 + + def generate_table(self) -> pl.DataFrame: + DummyLUT.calls += 1 + + x = np.linspace(0, 1, 5) + y = np.linspace(0, 1, 6) + + rows = [] + for xi in x: + for yi in y: + rows.append( + { + "x": xi, + "y": yi, + "z": xi + yi, + "w": xi * yi, # second surface + } + ) + return pl.DataFrame(rows) + +# ------------------------------------------------------------------ +# CachedLUT caching behavior +# ------------------------------------------------------------------ + +def test_lut_generates_and_saves(tmp_path): + cache_file = tmp_path / "lut.csv" + + lut = DummyLUT( + key1="x", + key2="y", + to_interp=["z", "w"], + cache_fn=cache_file, + regenerate=True, + ) + + assert cache_file.exists() + assert isinstance(lut.df, pl.DataFrame) + + +def test_lut_loads_from_cache(tmp_path): + cache_file = tmp_path / "lut.csv" + + DummyLUT( + key1="x", + key2="y", + to_interp=["z", "w"], + cache_fn=cache_file, + regenerate=True, + ) + + lut = DummyLUT( + key1="x", + key2="y", + to_interp=["z", "w"], + cache_fn=cache_file, + regenerate=False, + ) + + assert isinstance(lut.df, pl.DataFrame) + +# ------------------------------------------------------------------ +# Interpolator creation +# ------------------------------------------------------------------ + +def test_interpolators_created_for_all_fields(tmp_path): + cache_file = tmp_path / "lut.csv" + + lut = DummyLUT( + key1="x", + key2="y", + to_interp=["z", "w"], + cache_fn=cache_file, + regenerate=True, + ) + + assert set(lut.interpolators.keys()) == {"z", "w"} + +# ------------------------------------------------------------------ +# Interpolation correctness +# ------------------------------------------------------------------ + +def test_interpolator_reproduces_surfaces(tmp_path): + cache_file = tmp_path / "lut.csv" + + lut = DummyLUT( + key1="x", + key2="y", + to_interp=["z", "w"], + cache_fn=cache_file, + regenerate=True, + s=0.0, + ) + + z_val = lut.interpolators["z"](0.3, 0.4, grid=False) + w_val = lut.interpolators["w"](0.3, 0.4, grid=False) + + assert np.isclose(z_val, 0.3 + 0.4, atol=1e-3) + assert np.isclose(w_val, 0.3 * 0.4, atol=1e-3) + +# ------------------------------------------------------------------ +# Regenerate flag behavior +# ------------------------------------------------------------------ + +def test_regenerate_forces_new_generation(tmp_path): + cache_file = tmp_path / "lut.csv" + + DummyLUT.calls = 0 + + DummyLUT( + key1="x", + key2="y", + to_interp=["z"], + cache_fn=cache_file, + regenerate=True, + ) + + DummyLUT( + key1="x", + key2="y", + to_interp=["z"], + cache_fn=cache_file, + regenerate=True, + ) + + assert DummyLUT.calls == 2 + + +def test_no_regenerate_uses_cache(tmp_path): + cache_file = tmp_path / "lut.csv" + + DummyLUT.calls = 0 + + DummyLUT( + key1="x", + key2="y", + to_interp=["z"], + cache_fn=cache_file, + regenerate=True, + ) + + DummyLUT( + key1="x", + key2="y", + to_interp=["z"], + cache_fn=cache_file, + regenerate=False, + ) + + assert DummyLUT.calls == 1 + +# ------------------------------------------------------------------ +# Test ThrustBasedUnifiedLUT behavior and correctness +# ------------------------------------------------------------------ +@pytest.fixture(scope="module") +def lut_model(tmp_path_factory): + cache_dir = tmp_path_factory.mktemp("lut_cache") + cache_file = cache_dir / "lut.csv" + lut_model = ThrustBasedUnifiedLUT( + cache_fn=cache_file, + regenerate=True, + LUT_Cts=np.linspace(-0.5,1,12), + LUT_yaws=np.linspace(-15.0,15.1,12), + ) + return lut_model + +def test_generate_table_shape(lut_model): + assert lut_model.df.shape[0] == 144 + assert set(lut_model.df.columns) == {"eff_yaw", "Ctprime", "Cp", "Ct", "an", "u4", "v4", "w4", "x0", "dp", "dp_NL"} + +@pytest.mark.parametrize("Ct,yaw,tilt", [ + (0.2, np.deg2rad(0.0), np.deg2rad(0.0)), + (0.2, np.deg2rad(0.0), np.deg2rad(10.0)), + (0.8, np.deg2rad(8.0), np.deg2rad(0.0)), + (1.0, np.deg2rad(-8.0), np.deg2rad(6.0)), + (1.0, np.deg2rad(-6.0), np.deg2rad(-8.0)), +]) +def test_lut_matches_unified_model(lut_model, Ct, yaw, tilt): + ref_model = UMM.ThrustBasedUnified() + sol_ref = ref_model(Ct, yaw, tilt = tilt) + sol_lut = lut_model(Ct, yaw, tilt = tilt) + atol = 1e-2 + rtol = 0.02 + np.testing.assert_allclose(sol_lut.an, sol_ref.an, atol=atol, rtol = rtol) + np.testing.assert_allclose(sol_lut.Cp, sol_ref.Cp, atol=atol, rtol = rtol) + np.testing.assert_allclose(sol_lut.u4, sol_ref.u4, atol=atol, rtol = rtol) + np.testing.assert_allclose(sol_lut.v4, sol_ref.v4, atol=atol, rtol = rtol) + +def test_compute_induction_rotor(lut_model): + ref_model = UnifiedMomentum() + lut_model = UnifiedMomentumLUT( + s = 0, + cache_fn=lut_model.cache_fn, + regenerate=False, # uses the same LUT as previous test + ) + + Ct = np.array([0.8]) + yaw = np.deg2rad(12.0) + tilt = np.deg2rad(4.0) + + an_ref = ref_model.compute_induction(Ct, yaw, tilt=tilt) + an_lut = lut_model.compute_induction(Ct, yaw, tilt=tilt) + + u4_ref, v4_ref, w4_ref = ref_model.compute_initial_wake_velocities(Ct, yaw, tilt = tilt) + u4_lut, v4_lut, w4_lut = lut_model.compute_initial_wake_velocities(Ct, yaw, tilt = tilt) + + atol = 1e-2 + rtol = 0.02 + np.testing.assert_allclose(an_lut, an_ref, atol=atol, rtol = rtol) + np.testing.assert_allclose(u4_lut, u4_ref, atol=atol, rtol = rtol) + np.testing.assert_allclose(v4_lut, v4_ref, atol=atol, rtol = rtol) + np.testing.assert_allclose(w4_lut, w4_ref, atol=atol, rtol = rtol) \ No newline at end of file diff --git a/tests/test_package.py b/tests/test_package.py index d89f74f..95a7cc7 100644 --- a/tests/test_package.py +++ b/tests/test_package.py @@ -12,7 +12,6 @@ def test_tangential_induction_model_imports(): def test_tip_loss_model_imports(): from MITRotor import TipLossModel, NoTipLoss, PrandtlTipLoss - def test_momentum_model_imports(): from MITRotor import ( MomentumModel, @@ -21,6 +20,7 @@ def test_momentum_model_imports(): HeckMomentum, UnifiedMomentum, MadsenMomentum, + UnifiedMomentumLUT, )