R/run_MC_CW_OSL_DELOC.R

In RLumCarlo: Monte-Carlo Methods for Simulating Luminescence Phenomena

#' @title Run Monte-Carlo Simulation for CW-OSL (delocalized transitions)
#'
#' @description Runs a Monte-Carlo (MC) simulation of continuous wave optically stimulated luminescence
#' (CW-OSL) using the one trap one recombination centre (OTOR) model.
#' The term delocalized here refers to the involvement of the conduction band.
#'
#' @details
#'
#' **The model**
#'
#' \deqn{
#' I_{DELOC}(t) = -dn/dt = A * (n^2 / (N*R + n(1-R)))
#' }
#'
#' Where in the function: \cr
#'  t := time (s) \cr
#'  A := the optical excitation rate from trap to conduction band (1/s)\cr
#'  n := `n_filled`, the instantaneous number of electrons \cr
#'  N := `N_e` the available number of electron traps available \cr
#'  R := retrapping ratio for delocalized transitions
#'
#' @param A [numeric] (**required**): The optical excitation rate from trap to conduction band (s^-1)
#'
#' @param times [numeric] (**required**): The sequence of temperature steps within the simulation (s)
#'
#' @param clusters [numeric] (*with default*): The number of created clusters for the MC runs. The input can be the output of [create_ClusterSystem]. In that case `n_filled` indicate absolute numbers of a system.
#'
#' @param N_e [integer] (*with default*): The total number of electron traps available (dimensionless). Can be a vector of `length(clusters)`, shorter values are recycled.
#'
#' @param n_filled [integer] (*with default*): The number of filled electron traps at the beginning
#' of the simulation (dimensionless). Can be a vector of `length(clusters)`, shorter values are recycled.
#'
#' @param R [numeric] (**required**): The retrapping ratio for delocalized transitions (dimensionless)
#'
#' @param method [character] (*with default*): Sequential `'seq'` or parallel `'par'`processing. In
#' the parallel mode the function tries to run the simulation on multiple CPU cores (if available) with
#' a positive effect on the computation time.
#'
#' @param output [character] (*with default*): Output is either the `'signal'` (the default) or
#' `'remaining_e'` (the remaining charges, electrons, in the trap)
#'
#' @param \dots further arguments, such as `cores` to control the number of used CPU cores or `verbose` to silence the terminal
#'
#' @return This function returns an object of class `RLumCarlo_Model_Output` which
#' is a [list] consisting of an [array] with dimension length(times) x clusters
#' and a [numeric] time vector.
#'
#' @section Function version: 0.1.0
#'
#' @author Sebastian Kreutzer, Institute of Geography, Heidelberg University (Germany)
#'
#' @references
#' Pagonis, V., Friedrich, J., Discher, M., Müller-Kirschbaum, A., Schlosser, V., Kreutzer, S.,
#' Chen, R. and Schmidt, C., 2019. Excited state luminescence signals from a random
#' distribution of defects: A new Monte Carlo simulation approach for feldspar.
#' Journal of Luminescence 207, 266–272. \doi{10.1016/j.jlumin.2018.11.024}
#'
#' **Further reading**
#'
#' Chen, R., McKeever, S.W.S., 1997. Theory of Thermoluminescence and Related Phenomena.
#' WORLD SCIENTIFIC. \doi{10.1142/2781}
#'
#' @examples
#' ## brief example
#' run_MC_CW_OSL_DELOC(
#'  A = 0.12,
#'  R = 0.1,
#'  times = 0:10,
#'  clusters = 10,
#'  method = "seq") %>%
#' plot_RLumCarlo(legend = TRUE)
#'
#' ## A long example
#' \dontrun{
#' A <- c(0.1,0.3,0.5,1)
#' times <- seq(0, 60, 1)
#' s <- 1e12
#' E <- 1
#' R <- c(1e-7, 1e-6, 0.01, 0.1) # sequence of different R values
#' clusters <- 1000 # number of Monte Carlo simulations
#' N_e <- c(200, 500, 700, 400) # number of free electrons
#' n_filled <- c(200, 500, 100, 70) # number of filled traps
#' method <-"par"
#' output <- "signal"
#' col <- c(1,2,3,4) # ifferent colours for the individual curves
#' plot_uncertainty <- c(TRUE,FALSE,TRUE,FALSE)  # do you want to see the uncertainty?
#' add_TF <- c(FALSE,rep(TRUE, (length(R)-1)))
#'
#' ## loop to plot different curves into one plot
#' for (u in 1:length(R)){
#'  results <- run_MC_CW_OSL_DELOC(
#'   A = A[u],
#'   times,
#'   clusters = clusters,
#'   N_e = N_e[u],
#'   n_filled = n_filled[u],
#'   R = R[u],
#'   method = method,
#'   output = output)
#'
#' plot_RLumCarlo(
#' results,
#'  add = add_TF[u],
#'  legend = FALSE,
#'  col = col[u],
#'  main = "Delocalised Transition")
#' }
#' # add your legend with your parameters
#' legend("topright",
#'   ncol = 4,
#'   cex = 0.55,
#'   title = "parameters",
#'   legend=c(
#'    paste0("A = ", A),
#'    paste0("n_filled = ", n_filled),
#'    paste0("N_e = ", N_e),
#'    paste0("R = ", R)),
#'    bty = "n",
#'    text.col = col)
#'}
#'
#' @keywords models data
#' @encoding UTF-8
#' @md
#' @export
run_MC_CW_OSL_DELOC <- function(
  A,
  times,
  clusters = 10,
  N_e = 200,
  n_filled = N_e,
  R,
  method = "par",
  output = "signal",
  ...){

# Integrity checks ----------------------------------------------------------------------------
  if(!output %in% c("signal", "remaining_e"))
    stop("[run_MC_CW_OSL_DELOC()] Allowed keywords for 'output' are either 'signal' or 'remaining_e'!", call. = FALSE)

# Register multi-core back end --------------------------------------------
  cl <- .registerClusters(method, ...)
  on.exit(parallel::stopCluster(cl))

# Enable dosimetric cluster system -----------------------------------------
  if(class(clusters)[1] == "RLumCarlo_ClusterSystem"){
    n_filled <- .distribute_electrons(
      clusters = clusters,
      N_system = n_filled[1])[["e_in_cluster"]]
    clusters <- clusters$cl_groups

  }

# Expand parameters -------------------------------------------------------
n_filled <- rep(n_filled, length.out = max(clusters))
N_e <- rep(N_e, length.out = max(clusters))

# Run model ---------------------------------------------------------------
  temp <- foreach(c = 1:max(clusters),
                  .packages = 'RLumCarlo',
                  .combine = 'comb_array',
                  .multicombine = TRUE) %dopar% {

    results <- MC_C_CW_OSL_DELOC(
      times = times,
      N_e = N_e[c],
      n_filled = n_filled[c],
      R = R[1],
      A = A[1])

    return(results[[output]])

  }  # end c++-loop

# Return --------------------------------------------------------------------------------------
.return_ModelOutput(signal = temp, time = times)
}