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


if __name__ == "__main__":
    # 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')