Title: Upsampling Monte Carlo Reactor Simulation Tallies in Depleted Sodium-Cooled Fast Reactor Assemblies Using a Convolutional Neural Network

Journal Article · · Energies
DOI: https://doi.org/10.3390/en17092177 · OSTI ID:2345276
 [1]; ORCiD logo [2];  [1]
  1. Colorado School of Mines, Golden, CO (United States)
  2. Argonne National Laboratory (ANL), Argonne, IL (United States)
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The computational demand of neutron Monte Carlo transport simulations can increase rapidly with the spatial and energy resolution of tallied physical quantities. Convolutional neural networks have been used to increase the resolution of Monte Carlo simulations of light water reactor assemblies while preserving accuracy with negligible additional computational cost. Here, we show that a convolutional neural network can also be used to upsample tally results from Monte Carlo simulations of sodium-cooled fast reactor assemblies, thereby extending the applicability beyond thermal systems. The convolutional neural network model is trained using neutron flux tallies from 300 procedurally generated nuclear reactor assemblies simulated using OpenMC. Validation and test datasets included 16 simulations of procedurally generated assemblies, and a realistic simulation of a European sodium-cooled fast reactor assembly was included in the test dataset. We show the residuals between the high-resolution flux tallies predicted by the neural network and high-resolution Monte Carlo tallies on relative and absolute bases. The network can upsample tallies from simulations of fast reactor assemblies with diverse and heterogeneous materials and geometries by a factor of two in each spatial and energy dimension. The network’s predictions are within the statistical uncertainty of the Monte Carlo tallies in almost all cases. This includes test assemblies for which burnup values and geometric parameters were well outside the ranges of those in assemblies used to train the network.

Research Organization:
Argonne National Laboratory (ANL), Argonne, IL (United States)
Sponsoring Organization:
USDOE
Grant/Contract Number:
AC02-06CH11357
OSTI ID:
2345276
Journal Information:
Energies, Journal Name: Energies Journal Issue: 9 Vol. 17; ISSN 1996-1073
Publisher:
MDPICopyright Statement
Country of Publication:
United States
Language:
English

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Deep Residual Learning for Image Recognition conference June 2016
Enhanced Deep Residual Networks for Single Image Super-Resolution conference July 2017

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21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS
OpenMC
convolutional neural network
neutron Monte Carlo
residual network
sodium-cooled fast reactors