crm_opt3: Number of collisions under model Option 3
In HiDef-Aerial-Surveying/stochLAB: Stochastic Collision Risk Model

crm_opt3

R Documentation

Number of collisions under model Option 3

Description

Wrapper function to run CRM calculations under option 3, i.e.:

Using the extended model, which takes into account the distribution of bird flight heights at risk height (above the minimum and below the maximum height of the rotor blade)
Using generic flight height distribution data

Usage

crm_opt3(
  rotor_grids,
  d_y_gen,
  rotor_radius,
  blade_width,
  rotor_speed,
  blade_pitch,
  flight_type,
  wing_span,
  flight_speed,
  body_lt,
  n_blades,
  prop_upwind,
  avoidance_rate,
  flux_factor,
  mth_prop_oper,
  lac_factor
)

Arguments

`rotor_grids`	A list object containing geometric attributes of the rotor at equidistant points within its unit circle. This object should be built via the function `generate_rotor_grids()`.
`d_y_gen`	a vector with the proportion of birds at height bands within the rotor disc, from a generic flight height distribution
`rotor_radius`	Numeric value. The radius of the rotor (R), in metres.
`blade_width`	Numeric value, giving the maximum blade width, in metres.
`rotor_speed`	Numeric value. The operational rotation speed, in revolutions/min.
`blade_pitch`	Numeric value. The average blade pitch angle, the angle between the blade surface and the rotor plane (γ), in radians.
`flight_type`	A character string, either 'flapping' or 'gliding', indicating the species' characteristic flight type.
`wing_span`	Numeric value. The wingspan of the bird (W), in metres.
`flight_speed`	Numeric value. The bird flying speed (v), in metres/sec.
`body_lt`	Numeric value. The length of the bird (L), in metres.
`n_blades`	An integer, the number of blades in rotor (b).
`prop_upwind`	Numeric value between 0-1 giving the proportion of flights upwind - defaults to 0.5.
`avoidance_rate`	a numeric value within the interval [0, 1]. The avoidance rate, expressing the probability that a bird flying on a collision course with a turbine will take evading action to avoid collision.
`flux_factor`	a vector containing the flux factor for each month
`mth_prop_oper`	A numeric vector, the proportion of time during which turbines are operational per month.
`lac_factor`	A numerical value, the large array correction factor. Defaults to 1, meaning large array correction is not applicable.

Value

A numeric vector, the expected number of collisions per month based on model option 3

Examples


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

 gen_fhd_dat <- Johnston_Flight_heights_SOSS %>%
      dplyr::filter(variable=="Gannet.est") %>%
      dplyr::select(height,prop)

 gen_fhd <- gen_fhd_dat$prop

 gen_fhd_at_rotor <-
    get_fhd_rotor(
      hub_height = 150,
      fhd = gen_fhd,
      rotor_radius = 120,
      tidal_offset = 2.5,
      yinc = 0.05)


 flux_fct <- get_flux_factor(
      n_turbines = 100,
      rotor_radius = 120,
      flight_speed = 13.1,
      bird_dens = c(1.19,0.85,1.05,1.45,1.41,1.45,1.12,1.45,0.93,0.902,1.06,1.23),
      daynight_hrs = Day_Length(52),
      noct_activity = 0.5
      )

turb_oper <- data.frame(
   month = month.abb,
   prop_oper = runif(12,0.5,0.8)
   )
turb_oper_month <- turb_oper$prop_oper

crm_opt3(
  rotor_grids = rotor_grids,
  d_y_gen = gen_fhd_at_rotor,
  rotor_radius = 120,
  blade_width = 5,
  rotor_speed = 15,
  blade_pitch = 15,
  flight_type = "flapping",
  wing_span = 1.01,
  flight_speed = 13.1,
  body_lt = 0.85,
  n_blades = 3,
  prop_upwind = 0.5,
  avoidance_rate = 0.981,
  flux_factor = flux_fct,
  mth_prop_oper = turb_oper_month,
  lac_factor = 0.9998299
  )

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