Wrapper function to run CRM calculations under option 1:
Basic model, i.e. flights across collision risk height are uniformly distributed.
Proportion at collision risk height derived from site survey.
Usage
crm_opt1(
flux_factor,
prop_crh_surv,
avg_prob_coll,
mth_prop_oper,
avoidance_rate,
lac_factor
)
Arguments
- flux_factor
a vector containing the flux factor for each month
- prop_crh_surv
The proportion of flights at collision risk height derived from site survey (\(Q_2R\)). Only required for model Option 1.
- 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.
See also
get_flux_factor()
for calculating the flux factor
Examples
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_opt1(
flux_factor = flux_fct,
prop_crh_surv = 0.13,
avg_prob_coll = 0.1494609,
mth_prop_oper = turb_oper_month,
avoidance_rate = 0.989,
lac_factor = 0.9998287)
#> [1] 78.86228 40.14027 74.74202 81.46317 97.97719 85.37349 78.57620
#> [8] 111.10758 61.99363 56.93208 66.58078 85.62335