Get expected collisions based on the basic model

Provides the number of collisions expected to occur by month. The basic model assumes a uniform distribution of bird flights at risk height (i.e. between min and max rotor height).

Usage

get_collisions_basic(
  n_transits,
  avg_prob_coll,
  mth_prop_oper,
  avoidance_rate,
  lac_factor = 1
)

Arguments

n_transits

A numeric vector, the estimated number of bird flights crossing the rotors of the wind farm at each time period.

avg_prob_coll

A numeric value, the average probability of collision for a single bird transit through a rotor, assuming no avoidance action (\(p_{average}\)).

mth_prop_oper

A numeric vector, the proportion of time during which turbines are operational per month.

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.

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 at each time periods

Examples

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

 flux_factor <- 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
 )

 prop_crh_surv <- 0.13

 n_transits_opt1 <- flux_factor * prop_crh_surv

 get_collisions_basic(
   n_transits = n_transits_opt1,
   avg_prob_coll = 0.1494609,
   mth_prop_oper = mth_oper_month,
   avoidance_rate = 0.989,
   lac_factor = 0.9998287
 )
#>  [1]  89.65238  50.48045  59.66852  91.27034  96.52906 104.53342  73.66691
#>  [8] 100.09275  57.91201  69.35534  51.39960  87.43155