run_MC_LM_OSL_TUN: Run Monte-Carlo Simulation for LM-OSL (tunnelling...

In RLumCarlo: Monte-Carlo Methods for Simulating Luminescence Phenomena

Run Monte-Carlo Simulation for LM-OSL (tunnelling transitions)

Description

Runs a Monte-Carlo (MC) simulation of linearly modulated optically stimulated luminescence (LM-OSL) using the tunnelling (TUN) model. Tunnelling refers to quantum mechanical tunnelling processes from the excited state of the trapped charge, into a recombination centre.

Usage

run_MC_LM_OSL_TUN(
  A,
  rho,
  times,
  clusters = 10,
  r_c = 0,
  delta.r = 0.1,
  N_e = 200,
  method = "par",
  output = "signal",
  ...
)

Arguments

A	numeric (required): The effective optical excitation rate for the tunnelling process
rho	numeric (required): The dimensionless density of recombination centres (defined as ρ' in Huntley 2006) (dimensionless)
times	numeric (required): The sequence of time steps within the simulation (s)
clusters	numeric (with default): The number of MC runs
r_c	numeric (with default): Critical distance (>0) that is to be used if the sample has 1 been thermally and/or optically pretreated. This parameter expresses the fact that electron-hole pairs within a critical radius r_c have already been recombined.
delta.r	numeric (with default): Increments of dimensionless distance r'
N_e	numeric (width default): The total number of electron traps available (dimensionless). Can be a vector of length(clusters), shorter values are recycled.
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.
output	character (with default): output is either the 'signal' (the default) or 'remaining_e' (the remaining charges, electrons, in the trap)
...	further arguments, such as cores to control the number of used CPU cores or verbose to silence the terminal

Details

The model

I_{TUN}(r',t) = -dn/dt = (A * t/P) * exp(-(ρ')^{-1/3} * r') * n(r',t)

Where in the function:
A := the optical excitation rate for the tunnelling process (s^-1)
t := time (s)
P := maximum stimulation time (s)
r' := the dimensionless tunnelling radius
ρ := rho the dimensionless density of recombination centres see Huntley (2006)
n := the instantaneous number of electrons corresponding to the radius r'

Value

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.

Function version

0.1.0

How to cite

Friedrich, J., Kreutzer, S., 2022. run_MC_LM_OSL_TUN(): Run Monte-Carlo Simulation for LM-OSL (tunnelling transitions). Function version 0.1.0. In: Friedrich, J., Kreutzer, S., Pagonis, V., Schmidt, C., 2022. RLumCarlo: Monte-Carlo Methods for Simulating Luminescence Phenomena. R package version 0.1.9. https://CRAN.R-project.org/package=RLumCarlo

Author(s)

Johannes Friedrich, University of Bayreuth (Germany), Sebastian Kreutzer, Institute of Geography, Heidelberg University (Germany)

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_LM_OSL_TUN(
 A = 1,
 rho = 1e-3,
 times = 0:100,
 clusters = 10,
 N_e = 100,
 r_c = 0.1,
 delta.r = 1e-1,
 method = "seq",
 output = "signal") %>%
plot_RLumCarlo(norm = TRUE)

## Not run: 
## the long (meaningful) example
results <- run_MC_LM_OSL_TUN(
 A = 1,
 rho = 1e-3,
 times = 0:1000,
 clusters = 30,
 N_e = 100,
 r_c = 0.1,
 delta.r = 1e-1,
 method = "par",
 output = "signal")

plot_RLumCarlo(results, norm = TRUE)

## End(Not run)

RLumCarlo documentation built on Aug. 9, 2022, 1:06 a.m.