Add MIT yaw correction model (3rd pass)

docs/references.bib

@@ -299,3 +299,15 @@ @article{SinnerFleming2024grs
 title = {Robust wind farm layout optimization},
 journal = {Journal of Physics: Conference Series},
 }
+
+@article{HeckJohlasHowland2023_yawed_adm,
+doi = {10.1017/jfm.2023.129},
+url = {https://doi.org/10.1017/jfm.2023.129},
+year = {2023},
+month = {mar},
+publisher={Cambridge University Press},
+volume = {959},
+author = {K.S. Heck, H.M. Johlas and M.F. Howland},
+title = {Modelling the induction, thrust and power of a yaw-misaligned actuator disk},
+journal = {Journal of Fluid Mechanics},
+}

examples/examples_turbine/004_compare_yaw_loss.py

@@ -0,0 +1,87 @@
+"""
+Example: Change operation model and compare power loss in yaw.
+
+This example illustrates how to define different operational models and compares
+the power loss resulting from yaw misalignment across these various models.
+"""
+
+import itertools
+
+import matplotlib.pyplot as plt
+import numpy as np
+
+from floris import FlorisModel, TimeSeries
+
+
+# Parameters
+N = 101  # How many steps to cover yaw range in
+yaw_max = 30  # Maximum yaw angle to test
+
+# Set up the yaw angle sweep
+yaw_angles = np.zeros((N, 1))
+yaw_angles[:, 0] = np.linspace(-yaw_max, yaw_max, N)
+# print(yaw_angles.shape)
+
+
+def evaluate_yawed_power(wsp: float, op_model: str) -> float:
+    print(f"Evaluating model: {op_model}   wind speed: {wsp} m/s")
+
+    # Grab model of FLORIS
+    fmodel = FlorisModel("../inputs/gch.yaml")
+
+    # Run N cases by setting up a TimeSeries (which is just several independent simulations)
+    wind_directions = np.ones(N) * 270.0
+    fmodel.set(
+        wind_data=TimeSeries(
+            wind_speeds=wsp,
+            wind_directions=wind_directions,
+            turbulence_intensities=0.06,
+        )
+    )
+
+    yaw_angles = np.array(
+        [(yaw, 0.0, 0.0) for yaw in np.linspace(-yaw_max, yaw_max, N)]
+    )
+    fmodel.set_operation_model(op_model)
+    fmodel.set(yaw_angles=yaw_angles)
+    fmodel.run()
+
+    # Save the power output results in kW
+    return fmodel.get_turbine_powers()[:, 0] / 1000
+
+
+# Loop over the operational models and wind speeds to compare
+op_models = ["simple", "cosine-loss", "mit-loss"]
+wind_speeds = [11.0, 11.5, 15.0]
+results = {}
+for op_model, wsp in itertools.product(op_models, wind_speeds):
+
+    # Save the power output results in kW
+    results[(op_model, wsp)] = evaluate_yawed_power(wsp, op_model)
+# Plot the results
+fig, axes = plt.subplots(1, len(wind_speeds), sharey=True)
+
+colors = ["C0", "k", "r"]
+linestyles = ["solid", "dashed", "dotted"]
+for wsp, ax in zip(wind_speeds, axes):
+    ax.set_title(f"wsp: {wsp} m/s")
+    ax.set_xlabel("Yaw angle [deg]")
+    ax.grid(True)
+    for op_model, c, ls in zip(op_models, colors, linestyles):
+
+        upstream_yaw_angle = yaw_angles[:, 0]
+        central_power = results[(op_model, wsp)][upstream_yaw_angle == 0]
+        ax.plot(
+            upstream_yaw_angle,
+            results[(op_model, wsp)] / central_power,
+            label=op_model,
+            color=c,
+            linestyle=ls,
+        )
+
+ax.grid(True)
+ax.legend()
+axes[0].set_xlabel("Yaw angle [deg]")
+axes[0].set_ylabel("Normalized turbine power [-]")
+
+plt.show()

floris/core/turbine/__init__.py

@@ -1,4 +1,5 @@
 
+from floris.core.turbine.mit_turbine import MITTurbine
 from floris.core.turbine.operation_models import (
     AWCTurbine,
     CosineLossTurbine,