Risk {criticality}

Risk Function

Description

This function estimates process criticality accident risk (imports Sample function).

Usage

Risk(
  bn,
  code = "mcnp",
  cores = parallel::detectCores()/2,
  dist = "gamma",
  facility.data,
  keff.cutoff = 0.9,
  metamodel,
  risk.pool = 100,
  sample.size = 1e+09,
  usl = 0.95,
  ext.dir,
  training.dir = NULL
)

Arguments

bn

Bayesian network

code

Monte Carlo radiation transport code (e.g., "cog", "mcnp")

cores

Number of CPU cores to use for generating Bayesian network samples

dist

Truncated probability distribution (e.g., "gamma", "normal")

facility.data

.csv file name

keff.cutoff

keff cutoff value (e.g., keff >= 0.9)

metamodel

List of deep neural network metamodels and weights

risk.pool

Number of times risk is calculated

sample.size

Number of samples used to calculate risk

usl

Upper subcritical limit (e.g., keff >= 0.95)

ext.dir

External directory (full path)

training.dir

Training directory (full path)

Value

A list of lists containing process criticality accident risk estimates and Bayesian network samples

Examples

ext.dir <- paste0(tempdir(), "/criticality/extdata")
dir.create(ext.dir, recursive = TRUE, showWarnings = FALSE)

extdata <- paste0(.libPaths()[1], "/criticality/extdata")
file.copy(paste0(extdata, "/facility.csv"), ext.dir, recursive = TRUE)
file.copy(paste0(extdata, "/mcnp-dataset.RData"), ext.dir, recursive = TRUE)

config <- FALSE
try(config <- reticulate::py_config()$available)
try(if (config == TRUE) {
  Risk(
    bn = BN(
      facility.data = "facility.csv",
      ext.dir = ext.dir),
    code = "mcnp",
    cores = 1,
    facility.data = "facility.csv",
    keff.cutoff = 0.5,
    metamodel = NN(
      batch.size = 128,
      ensemble.size = 1,
      epochs = 10,
      layers = "256-256-16",
      replot = FALSE,
      ext.dir = ext.dir),
    risk.pool = 10,
    sample.size = 1e+04,
    ext.dir = ext.dir,
    training.dir = NULL
  )
})