"""Example: Empirical Gaussian deflection parameters
This example illustrates the main parameters of the Empirical Gaussian
deflection model and their effects on the wind turbine wake.
"""
import copy
import matplotlib.pyplot as plt
import numpy as np
from floris import FlorisModel
from floris.flow_visualization import plot_rotor_values, visualize_cut_plane
# Initialize FLORIS with the given input file.
# For basic usage, FlorisModel provides a simplified and expressive
# entry point to the simulation routines.
# Options
show_flow_cuts = True
num_in_row = 5 # Should be at least 3
first_three_yaw_angles = [20., 20., 10.]
yaw_angles = np.array(first_three_yaw_angles + [0.0]*(num_in_row-3))[None, :]
print("Turbine yaw angles (degrees): ", yaw_angles[0])
# Define function for visualizing wakes
def generate_wake_visualization(fmodel, title=None):
# Using the FlorisModel functions, get 2D slices.
x_bounds = [-500, 3000]
y_bounds = [-250, 250]
z_bounds = [0.001, 500]
cross_plane_locations = [10, 1200, 2500]
horizontal_plane_location = 90.0
streamwise_plane_location = 0.0
# Contour plot colors
min_ws = 4
max_ws = 10
horizontal_plane = fmodel.calculate_horizontal_plane(
x_resolution=200,
y_resolution=100,
height=horizontal_plane_location,
x_bounds=x_bounds,
y_bounds=y_bounds,
)
y_plane = fmodel.calculate_y_plane(
x_resolution=200,
z_resolution=100,
crossstream_dist=streamwise_plane_location,
x_bounds=x_bounds,
z_bounds=z_bounds,
)
cross_planes = []
for cpl in cross_plane_locations:
cross_planes.append(
fmodel.calculate_cross_plane(
y_resolution=100,
z_resolution=100,
downstream_dist=cpl
)
)
# Create the plots
# Cutplane settings
cp_ls = "solid" # line style
cp_lw = 0.5 # line width
cp_clr = "black" # line color
fig = plt.figure()
fig.set_size_inches(12, 12)
# Horizontal profile
ax = fig.add_subplot(311)
visualize_cut_plane(horizontal_plane, ax=ax, title="Top-down profile",
min_speed=min_ws, max_speed=max_ws)
ax.plot(x_bounds, [streamwise_plane_location]*2, color=cp_clr,
linewidth=cp_lw, linestyle=cp_ls)
for cpl in cross_plane_locations:
ax.plot([cpl]*2, y_bounds, color=cp_clr, linewidth=cp_lw,
linestyle=cp_ls)
ax = fig.add_subplot(312)
visualize_cut_plane(y_plane, ax=ax, title="Streamwise profile",
min_speed=min_ws, max_speed=max_ws)
ax.plot(x_bounds, [horizontal_plane_location]*2, color=cp_clr,
linewidth=cp_lw, linestyle=cp_ls)
for cpl in cross_plane_locations:
ax.plot([cpl, cpl], z_bounds, color=cp_clr, linewidth=cp_lw,
linestyle=cp_ls)
# Spanwise profiles
for i, (cp, cpl) in enumerate(zip(cross_planes, cross_plane_locations)):
visualize_cut_plane(cp, ax=fig.add_subplot(3, len(cross_planes), i+7),
title="Loc: {:.0f}m".format(cpl), min_speed=min_ws,
max_speed=max_ws)
# Add overall figure title
if title is not None:
fig.suptitle(title, fontsize=16)
## Main script
# Load input yaml and define farm layout
fmodel = FlorisModel("../inputs/emgauss.yaml")
D = fmodel.core.farm.rotor_diameters[0]
fmodel.set(
layout_x=[x*5.0*D for x in range(num_in_row)],
layout_y=[0.0]*num_in_row,
wind_speeds=[8.0],
wind_directions=[270.0],
yaw_angles=yaw_angles,
)
# Save dictionary to modify later
fmodel_dict = fmodel.core.as_dict()
# Run wake calculation
fmodel.run()
# Look at the powers of each turbine
turbine_powers = fmodel.get_turbine_powers().flatten()/1e6
fig0, ax0 = plt.subplots(1,1)
width = 0.1
nw = -2
x = np.array(range(num_in_row))+width*nw
nw += 1
title = "Original"
ax0.bar(x, turbine_powers, width=width, label=title)
ax0.legend()
# Visualize wakes
if show_flow_cuts:
generate_wake_visualization(fmodel, title)
# Increase the maximum deflection attained
fmodel_dict_mod = copy.deepcopy(fmodel_dict)
fmodel_dict_mod['wake']['wake_deflection_parameters']['empirical_gauss']\
['horizontal_deflection_gain_D'] = 5.0
fmodel = FlorisModel(fmodel_dict_mod)
fmodel.set(
wind_speeds=[8.0],
wind_directions=[270.0],
yaw_angles=yaw_angles,
)
fmodel.run()
turbine_powers = fmodel.get_turbine_powers().flatten()/1e6
x = np.array(range(num_in_row))+width*nw
nw += 1
title = "Increase max deflection"
ax0.bar(x, turbine_powers, width=width, label=title)
if show_flow_cuts:
generate_wake_visualization(fmodel, title)
# Add (increase) influence of wake added mixing
fmodel_dict_mod = copy.deepcopy(fmodel_dict)
fmodel_dict_mod['wake']['wake_deflection_parameters']['empirical_gauss']\
['mixing_gain_deflection'] = 100.0
fmodel = FlorisModel(fmodel_dict_mod)
fmodel.set(
wind_speeds=[8.0],
wind_directions=[270.0],
yaw_angles=yaw_angles,
)
fmodel.run()
turbine_powers = fmodel.get_turbine_powers().flatten()/1e6
x = np.array(range(num_in_row))+width*nw
nw += 1
title = "Increase mixing gain"
ax0.bar(x, turbine_powers, width=width, label=title)
if show_flow_cuts:
generate_wake_visualization(fmodel, title)
# Add (increase) the yaw-added mixing contribution
fmodel_dict_mod = copy.deepcopy(fmodel_dict)
# Include a WIM gain so that YAM is reflected in deflection as well
# as deficit
fmodel_dict_mod['wake']['wake_deflection_parameters']['empirical_gauss']\
['mixing_gain_deflection'] = 100.0
fmodel_dict_mod['wake']['wake_deflection_parameters']['empirical_gauss']\
['yaw_added_mixing_gain'] = 1.0
fmodel = FlorisModel(fmodel_dict_mod)
fmodel.set(
wind_speeds=[8.0],
wind_directions=[270.0],
yaw_angles=yaw_angles,
)
fmodel.run()
turbine_powers = fmodel.get_turbine_powers().flatten()/1e6
x = np.array(range(num_in_row))+width*nw
nw += 1
title = "Increase yaw-added mixing"
ax0.bar(x, turbine_powers, width=width, label=title)
if show_flow_cuts:
generate_wake_visualization(fmodel, title)
# Power plot aesthetics
ax0.set_xticks(range(num_in_row))
ax0.set_xticklabels(["T{0}".format(t) for t in range(num_in_row)])
ax0.legend()
ax0.set_xlabel("Turbine")
ax0.set_ylabel("Power [MW]")
plt.show()
import warnings
warnings.filterwarnings('ignore')
