generate_rotor_grids: Geometric attributes at equidistant points within the rotor's...

In HiDef-Aerial-Surveying/stochLAB: Stochastic Collision Risk Model

Geometric attributes at equidistant points within the rotor's unit circle

Description

Generates a list of grids containing geometric attributes of the rotor disk at equidistant locations, taking the center of the rotor as the reference point and overlaying the left-half of the rotor disk. The size of grid cells are determined by xinc and yinc, and their values map properties of the rotor at the cell's location.

Usage

generate_rotor_grids(yinc = 0.05, xinc = 0.05, chord_prof)

Arguments

yinc, xinc	numeric values, the grid increments along the y-axis and x-axis (i.e. grid cell dimensions)
chord_prof	A data frame with the chord taper profile of the rotor blade. Function expects two named columns: pp_radius, equidistant intervals of radius at bird passage point, as a proportion of rotor_radius, within the range [0, 1]. chord, the chord width at pp_radius, as a proportion of blade_width. Defaults to a generic profile for a typical modern 5MW turbine. See chord_prof_5MW() for details.

Details

These grids are required for an alternative approach to the calculation of probability of collision under the extended model (i.e. non-uniform flight distribution at risk height).

Functions xrisksum2, pcollxy and pcoll used in Masden & Cook implementation (PCollFunctions.r script) were based on Visual Basic computations available in the original Band worksheet. This Visual Basic code was devised under a deterministic context, i.e. for one single set of collision calculations for one given species and turbine scenario. However, in a stochastic context, where potentially thousands of collision calculations are performed per species and turbine scenario, it became clear that xrisksum2 and associated functions were highly inefficient for the task at hand.

The alternative approach streamlines computations by calculating (relative) rotor geometric attributes outside the stochastic sampling loop, which remain constant over iterations. These elements, calculated via generate_rotor_grids, are then applied to sampled parameters via vectorized operations. The number of calculations per iteration are thence substantially reduced, leading to significant gains in computational speed over Masden's implementation for a 1000 iterations run.

Value

A list with the following elements, taking the center of the rotor as the origin in the rotor's plane:

• x_grid, a 2D array of horizontal distances from the rotor's horizontal axis, as proportion of rotor radius, at each grid point

• y_grid, a 2D array of vertical distances from the rotor's vertical axis, as proportion of rotor radius, at each grid point

• r_grid, a 2D array of radial distances from rotor center, as proportion of rotor radius, at each grid point

• phi_grid, a 2D array of angles, relative to vertical, at each grid point

• chord_grid, a 2D array of blade chord width at each grid point

All elements are representative of the left-half of the rotor circle

See Also

get_x_grid(), get_y_grid(), get_phi_grid()

Examples

rotor_grids <- generate_rotor_grids(yinc = 0.05, xinc = 0.05, chord_prof_5MW)

HiDef-Aerial-Surveying/stochLAB documentation built on March 16, 2023, 8:13 a.m.