Title:	Modeling Fissile Material Operations in Nuclear Facilities
Version:	0.9.3
Depends:	R (≥ 3.6.0), caret
Description:	A collection of functions for modeling fissile material operations in nuclear facilities, based on Zywiec et al (2021) <doi:10.1016/j.ress.2020.107322>.
License:	MIT + file LICENSE
Encoding:	UTF-8
RoxygenNote:	7.2.3
Imports:	bnlearn, dplyr, evd, fitdistrplus, ggplot2, keras, magrittr, parallel, reticulate, scales
NeedsCompilation:	no
Packaged:	2023-05-29 18:13:13 UTC; Will
Author:	William Zywiec [aut, cre]
Maintainer:	William Zywiec <willzywiec@gmail.com>
Repository:	CRAN
Date/Publication:	2023-05-29 22:20:14 UTC

BN Function

Description

This function creates a Bayesian network from pre-formatted nuclear facility data.

Usage

BN(dist = "gamma", facility.data, ext.dir)

Arguments

Value

A Bayesian network that models fissile material operations (op), controls (ctrl), and parameters that affect nuclear criticality safety

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)

BN(
  facility.data = "facility.csv",
  ext.dir = ext.dir
)

Model Function

Description

This function builds the deep neural network metamodel architecture.

Usage

Model(
  dataset,
  layers = "8192-256-256-256-256-16",
  loss = "sse",
  opt.alg = "adamax",
  learning.rate = 0.00075,
  ext.dir
)

Arguments

Value

A deep neural network metamodel of Monte Carlo radiation transport code simulation data

NN Function

Description

This function trains an ensemble of deep neural networks to predict keff values (imports Tabulate, Scale, Model, Fit, Plot, and Test functions).

Usage

NN(
  batch.size = 8192,
  code = "mcnp",
  dataset,
  ensemble.size = 5,
  epochs = 1500,
  layers = "8192-256-256-256-256-16",
  loss = "sse",
  opt.alg = "adamax",
  learning.rate = 0.00075,
  val.split = 0.2,
  overwrite = FALSE,
  remodel = FALSE,
  replot = TRUE,
  verbose = FALSE,
  ext.dir,
  training.dir = NULL
)

Arguments

Value

A list of lists containing an ensemble of deep neural networks and weights

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) {
  NN(
    batch.size = 128,
    ensemble.size = 1,
    epochs = 10,
    layers = "256-256-16",
    loss = "sse",
    replot = FALSE,
    ext.dir = ext.dir
  )
})

Plot Function

Description

This function generates and saves plots and data.

Usage

Plot(i, history = NULL, plot.dir)

Arguments

Value

No output (generates and saves ggplot2 files and training histories)

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

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
  )
})

Sample Function

Description

This function samples the Bayesian network and generates keff predictions using a deep neural network metamodel.

Usage

Sample(
  bn,
  code = "mcnp",
  cores = parallel::detectCores()/2,
  keff.cutoff = 0.9,
  metamodel,
  sample.size = 1e+09,
  ext.dir,
  risk.dir = NULL
)

Arguments

Value

A list of Bayesian network samples with predicted keff values

Scale Function

Description

This function centers, scales, and one-hot encodes variables.

Usage

Scale(code = "mcnp", dataset = NULL, output, ext.dir)

Arguments

Value

A list of centered, scaled, and one-hot-encoded training and test data

Tabulate Function

Description

This function loads/saves training and test data (imports Scale function).

Usage

Tabulate(code = "mcnp", ext.dir)

Arguments

Value

A list of centered, scaled, and one-hot-encoded training and test data

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)

Tabulate(
  ext.dir = ext.dir
)

Test Function

Description

This function calculates deep neural network metamodel weights and generates keff predictions for all training and test data.

Usage

Test(dataset, ensemble.size = 5, loss = "sse", ext.dir, training.dir)

Arguments

Value

A list of deep neural network weights