R/run_MC_TL_TUN.R

In RLumCarlo: Monte-Carlo Methods for Simulating Luminescence Phenomena

#' @title Run Monte-Carlo Simulation for TL (tunnelling transitions)
#'
#' @description Runs a Monte-Carlo (MC) simulation of thermoluminescence (TL) caused by
#' tunnelling (TUN) transitions. Tunnelling refers to quantum mechanical
#' tunnelling processes from the excited state of the trap into a recombination centre.
#' The heating rate in this function is assumed to be 1 K/s.
#'
#' @details
#'
#' **The model**
#'
#' \deqn{
#' I_{TUN}(r',t) = -dn/dt = (s * exp(-E/(k_{B} * T))) * exp(-(\rho')^{-1/3} * r') * n(r',t)
#' }
#'
#' Where in the function: \cr
#' s := frequency for the tunnelling process (s^-1) \cr
#' E := thermal activation energy (eV) \cr
#' \eqn{k_{B}} := Boltzmann constant (8.617 x 10^-5 eV K^-1)\cr
#' T := temperature (°C)\cr
#' r' := the dimensionless tunnelling radius \cr
#' \eqn{\rho}' := `rho'`, the dimensionless density of recombination centres (see Huntley (2006)) \cr
#' t := time (s) \cr
#' n := the instantaneous number of electrons at distance r'
#'
#' @param E [numeric] (**required**): Thermal activation energy of the trap (eV)
#'
#' @param s [list] (**required**): The effective frequency factor for the tunnelling process (s^-1)
#'
#' @param rho [numeric] (**required**): The dimensionless density of recombination centres
#' (defined as \eqn{\rho}' in Huntley 2006)
#'
#' @param r_c [numeric] (*with default*): Critical distance (>0) that is to be used if
#' the sample has been thermally and/or optically pretreated. This parameter expresses the fact
#' that electron-hole pairs within a critical radius `r_c` have already recombined.
#'
#' @param times [numeric] (**required**): The sequence of temperature steps within the simulation (s).
#' The default heating rate is set to 1 K/s. The final temperature is `max(times) * b`
#'
#' @param b [numeric] (*with default*): the heating rate in K/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 [numeric] (*with default*): The total number of electron traps available (dimensionless). Can be a vector of `length(clusters)`, shorter values are recycled.
#'
#' @param delta.r [numeric] (*with default*): The increments of the dimensionless distance r'
#'
#' @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 length(r) x clusters
#' and a [numeric] time vector.
#'
#' @section Function version: 0.1.0
#'
#' @author Johannes Friedrich, University of Bayreuth (Germany), Sebastian Kreutzer,
#' Geography & Earth Sciences, Aberystwyth University (United Kingdom)
#'
#' @references
#' Huntley, D.J., 2006. An explanation of the power-law decay of luminescence.
#' Journal of Physics: Condensed Matter, 18(4), 1359.
#'
#' Pagonis, V. and Kulp, C., 2017. Monte Carlo simulations of tunneling
#' phenomena and nearest neighbor hopping mechanism in feldspars.
#' Journal of Luminescence 181, 114–120. \doi{10.1016/j.jlumin.2016.09.014}
#'
#' 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**
#'
#' Aitken, M.J., 1985. Thermoluminescence dating. Academic Press.
#'
#' Jain, M., Guralnik, B., Andersen, M.T., 2012. Stimulated luminescence emission from
#' localized recombination in randomly distributed defects.
#' Journal of Physics: Condensed Matter 24, 385402.
#'
#' @examples
#' ## the short example
#' run_MC_TL_TUN(
#'  s = 1e12,
#'  E = 0.9,
#'  rho = 1,
#'  r_c = 0.1,
#'  times = 80:120,
#'  b = 1,
#'  clusters = 50,
#'  method = 'seq',
#'  delta.r = 1e-1) %>%
#' plot_RLumCarlo()
#'
#' \dontrun{
#' ## the long (meaningful example)
#' results <- run_MC_TL_TUN(
#'  s = 1e12,
#'  E = 0.9,
#'  rho = 0.01,
#'  r_c = 0.1,
#'  times = 80:220,
#'  clusters = 100,
#'  method = 'par',
#'  delta.r = 1e-1)
#'
#' ## plot
#' plot_RLumCarlo(results)
#' }
#'
#' @keywords models data
#' @encoding UTF-8
#' @md
#' @export
run_MC_TL_TUN <- function(
  s,
  E,
  rho,
  r_c = 0,
  times,
  b = 1,
  clusters = 10,
  N_e = 200,
  delta.r = 0.1,
  method = "par",
  output = "signal",
  ...){

# Integrity checks ----------------------------------------------------------------------------
  if(!output %in% c("signal", "remaining_e"))
    stop("[run_MC_TL_TUN()] 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))

# Setting parameters --------------------------------------------------------------------------
r <- seq(abs(r_c), 2, abs(delta.r))

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

}

# Expand parameters -------------------------------------------------------
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_TL_TUN(
      times = times,
      b = b[1],
      N_e = N_e[c],
      r = r,
      rho = rho[1],
      E = E[1],
      s = s[1]
    )
   return(results[[output]])

  }

# Return --------------------------------------------------------------------------------------
if (output == "signal") temp <- temp / b
.return_ModelOutput(time = times * b, signal = temp)
}