Tum misalignment model

examples/41_compare_yaw_loss.py

@@ -0,0 +1,87 @@
+# Copyright 2024 NREL
+
+# Licensed under the Apache License, Version 2.0 (the "License"); you may not
+# use this file except in compliance with the License. You may obtain a copy of
+# the License at http://www.apache.org/licenses/LICENSE-2.0
+
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+# License for the specific language governing permissions and limitations under
+# the License.
+
+# See https://floris.readthedocs.io for documentation
+
+import matplotlib.pyplot as plt
+import numpy as np
+import yaml
+
+from floris.tools import FlorisInterface
+
+
+"""
+Test alternative models of loss to yawing
+"""
+
+# Parameters
+N  = 101 # How many steps to cover yaw range in
+yaw_max = 30 # Maximum yaw 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)
+
+
+
+# Now loop over the operational models to compare
+op_models = ["cosine-loss", "tum-loss"]
+results = {}
+
+for op_model in op_models:
+
+    print(f"Evaluating model: {op_model}")
+
+    # Grab model of FLORIS
+    fi = FlorisInterface("inputs/gch.yaml")
+
+    # Initialize to a simple 1 turbine case with n_findex = N
+    fi.set(
+        layout_x=[0],
+        layout_y=[0],
+        wind_directions=270 * np.ones(N),
+        wind_speeds=8 * np.ones(N),
+    )
+
+    with open(str(
+        fi.floris.as_dict()["farm"]["turbine_library_path"] /
+        (fi.floris.as_dict()["farm"]["turbine_type"][0] + ".yaml")
+    )) as t:
+        turbine_type = yaml.safe_load(t)
+    turbine_type["power_thrust_model"] = op_model
+
+    # Change the turbine type
+    fi.set(turbine_type=[turbine_type], yaw_angles=yaw_angles)
+
+    # Calculate the power
+    fi.run()
+    turbine_power = fi.get_turbine_powers().squeeze()
+
+    # Save the results
+    results[op_model] = turbine_power
+
+# Plot the results
+fig, ax = plt.subplots()
+
+colors = ["C0", "k", "r"]
+linestyles = ["solid", "dashed", "dotted"]
+for key, c, ls in zip(results, colors, linestyles):
+    central_power = results[key][yaw_angles.squeeze() == 0]
+    ax.plot(yaw_angles.squeeze(), results[key]/central_power, label=key, color=c, linestyle=ls)
+
+ax.grid(True)
+ax.legend()
+ax.set_xlabel("Yaw angle [deg]")
+ax.set_ylabel("Normalized turbine power [deg]")
+
+plt.show()

floris/simulation/rotor_velocity.py

@@ -29,6 +29,18 @@ def rotor_velocity_yaw_correction(
 
     return rotor_effective_velocities
 
+def tum_rotor_velocity_yaw_correction(
+    pP: float,
+    yaw_angles: NDArrayFloat,
+    rotor_effective_velocities: NDArrayFloat,
+) -> NDArrayFloat:
+    # Compute the rotor effective velocity adjusting for yaw settings
+    pW = pP / 3.0  # Convert from pP to w
+    # TODO: cosine loss hard coded
+    rotor_effective_velocities = rotor_effective_velocities * cosd(yaw_angles) ** pW
+
+    return rotor_effective_velocities
+
 def rotor_velocity_tilt_correction(
     tilt_angles: NDArrayFloat,
     ref_tilt: NDArrayFloat,

floris/simulation/turbine/__init__.py

@@ -4,4 +4,5 @@
     MixedOperationTurbine,
     SimpleDeratingTurbine,
     SimpleTurbine,
+    TUMLossTurbine,
 )