Example: Uncertain Model With Parallelization

"""Example: Uncertain Model With Parallelization

This example demonstrates how to combined the parallelized model with the uncertain model
"""

import matplotlib.pyplot as plt
import numpy as np

from floris import (
    FlorisModel,
    TimeSeries,
    UncertainFlorisModel,
)
from floris.par_floris_model import ParFlorisModel


# Following the refactoring of ParFlorisModel, the UncertainFlorisModel can be
# parallelized by passing the ParFlorisModel as the model to be run.  This example
# demonstrates the usage and shows that the result obtained from the UncertainFlorisModel
# with and without parallelization is the same.  The results are compared to the nominal
# results.

# Instantiate a FlorisModel and ParallelFlorisModel using the GCH model
fmodel = FlorisModel("../inputs/gch.yaml")
pfmodel = ParFlorisModel("../inputs/gch.yaml")

# Use the above model to declare a serial and parallel UncertainFlorisModel
ufmodel = UncertainFlorisModel(fmodel)
pufmodel = UncertainFlorisModel(pfmodel)


# Define an inflow where wind direction is swept while
# wind speed and turbulence intensity are held constant
wind_directions = np.arange(240.0, 300.0, 1.0)
time_series = TimeSeries(
    wind_directions=wind_directions,
    wind_speeds=8.0,
    turbulence_intensities=0.06,
)

# Define a two turbine farm and apply the inflow
D = 126.0
layout_x = np.array([0, D * 6])
layout_y = [0, 0]

# Apply to fmodel, ufmodel, and pufmodel
fmodel.set(
    layout_x=layout_x,
    layout_y=layout_y,
    wind_data=time_series,
)

ufmodel.set(
    layout_x=layout_x,
    layout_y=layout_y,
    wind_data=time_series,
)

pufmodel.set(
    layout_x=layout_x,
    layout_y=layout_y,
    wind_data=time_series,
)

# Run the models
fmodel.run()
ufmodel.run()
pufmodel.run()

# Collect the farm power results from each model
farm_powers_nom = fmodel.get_farm_power() / 1e3
farm_powers_unc = ufmodel.get_farm_power() / 1e3
farm_powers_punc = pufmodel.get_farm_power() / 1e3

# Compare the results
fig, ax = plt.subplots()
ax.plot(wind_directions, farm_powers_nom.flatten(), 'k-', label="Nominal power")
ax.plot(wind_directions, farm_powers_unc.flatten(), 'bs-', label="Uncertain power")
ax.plot(wind_directions, farm_powers_punc.flatten(), 'r.--', label="Parallel uncertain power")
ax.grid(True)
ax.legend()
ax.set_xlabel("Wind Direction (deg)")
ax.set_ylabel("Power (kW)")

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