## Contents

More information regarding the numerical and computational formulation in FLORIS are detailed here. See Introductory Concepts for a guide on the basics.

# Create a basic FLORIS model for use later
import numpy as np
import matplotlib.pyplot as plt
from floris import FlorisModel
fmodel = FlorisModel("gch.yaml")


## Data structures#

FLORIS adopts a structures of arrays data modeling paradigm (SoA, relative to array of structures {AoS}) for nearly all of the data in the floris.core package. This data model enables vectorization (SIMD operations) through Numpy array broadcasting for many operations. In general, there are two types of array shapes:

• Field quantities have points throughout the computational domain but in context-specific locations and have the shape (n findex, n turbines, n grid, n grid).

• Scalar quantities have a single value for each turbine and typically have the shape (n findex, n turbines, 1, 1). For scalar quanities, the arrays may be created with the shape (n findex, n turbines) and then expanded to the 4-dimensional shape prior to running the wake calculation.

## Grids#

FLORIS includes a number of grid-types that create sampling points within the computational domain for different contexts. In the typical use case, AEP or some other metric of wind farm energy yield is the end result. Since the mathematical models in FLORIS are all analytical, we only need to create points on the turbines themselves in order to calculate the incoming wind speeds given all of the upstream conditions. In this case, we use the floris.core.grid.TurbineGrid() or floris.core.grid.TurbineCubatureGrid(). Each of these grid-types put points only on the turbine swept area, so all other field-quantities in FLORIS have the same shape.

# Plot the grid point locations for TurbineGrid and TurbineCubatureGrid

fmodel.set(layout_x=[0.0], layout_y=[0.0])