Example: Multi-dimensional Cp/Ct with 2 Hs values

This example follows the previous example but shows the effect of changing the Hs setting.NOTE: The multi-dimensional Cp/Ct data used in this example is fictional for the purposes offacilitating this example. The Cp/Ct values for the different wave conditions are scaledvalues of the original Cp/Ct data for the IEA 15MW turbine.

import matplotlib.pyplot as plt
import numpy as np

from floris import FlorisModel, TimeSeries


# Initialize FLORIS with the given input file.
fmodel = FlorisModel("../inputs/gch_multi_dim_cp_ct.yaml")

# Make a second Floris instance with a different setting for Hs.
# Note the multi-cp-ct file (iea_15MW_multi_dim_Tp_Hs.csv)
# for the turbine model iea_15MW_floating_multi_dim_cp_ct.yaml
# Defines Hs at 1 and 5.
# The value in gch_multi_dim_cp_ct.yaml is 3.01 which will map
# to 5 as the nearer value, so we set the other case to 1
# for contrast.
fmodel_dict_mod = fmodel.core.as_dict()
fmodel_dict_mod["flow_field"]["multidim_conditions"]["Hs"] = 1.0
fmodel_hs_1 = FlorisModel(fmodel_dict_mod)

# Set both cases to 3 turbine layout
fmodel.set(layout_x=[0.0, 500.0, 1000.0], layout_y=[0.0, 0.0, 0.0])
fmodel_hs_1.set(layout_x=[0.0, 500.0, 1000.0], layout_y=[0.0, 0.0, 0.0])

# Use a sweep of wind speeds
wind_speeds = np.arange(5, 20, 1.0)
time_series = TimeSeries(
    wind_directions=270.0, wind_speeds=wind_speeds, turbulence_intensities=0.06
)
fmodel.set(wind_data=time_series)
fmodel_hs_1.set(wind_data=time_series)

# Calculate wakes with baseline yaw
fmodel.run()
fmodel_hs_1.run()

# Collect the turbine powers in kW
turbine_powers = fmodel.get_turbine_powers() / 1000.0
turbine_powers_hs_1 = fmodel_hs_1.get_turbine_powers() / 1000.0

# Plot the power in each case and the difference in power
fig, axarr = plt.subplots(1, 3, sharex=True, figsize=(12, 4))

for t_idx in range(3):
    ax = axarr[t_idx]
    ax.plot(wind_speeds, turbine_powers[:, t_idx], color="k", label="Hs=3.1 (5)")
    ax.plot(wind_speeds, turbine_powers_hs_1[:, t_idx], color="r", label="Hs=1.0")
    ax.grid(True)
    ax.set_xlabel("Wind Speed (m/s)")
    ax.set_title(f"Turbine {t_idx}")

axarr[0].set_ylabel("Power (kW)")
axarr[0].legend()
fig.suptitle("Power of each turbine")

plt.show()
import warnings
warnings.filterwarnings('ignore')

floris.logging_manager.LoggingManager WARNING The current model does not account for vertical wake deflection due to tilt. Corrections to power and thrust coefficient can be included, but no vertical wake deflection will occur.

floris.logging_manager.LoggingManager WARNING The current model does not account for vertical wake deflection due to tilt. Corrections to power and thrust coefficient can be included, but no vertical wake deflection will occur.