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Upsampling Monte Carlo neutron transport simulation tallies using a convolutional neural network

Energy and AI 2023年第3期13卷 117-125页

作者： Andrew Osborne joffrey dorville Paul Romano Nuclear Science&Engineering The Colorado School of Mines101214th St.GoldenCO80401United States of America Department of Mechanical Engineering The Colorado School of Mines1610 Illinois St.GoldenCO80401United States of America Argonne National Laboratory LemontILUnited States of America

The physical quantities calculated by nuclear reactor Monte Carlo simulations are typically recorded on a grid of two or three spatial dimensions and one dimension of neutron *** of this,increasing the resolution of the calculated quantities can have a significant impact on the memory and CPU time required to run a *** neural networks have been shown to accurately upsample coarse-resolution photo-graphic images to resolutions multiple times finer than the *** we show that a convolutional neural network can accurately upsample flux tallies in a Monte Carlo neutron transport simulation by a factor of two along the spatial and energy *** flux tallies in pressurized water reactor assemblies were calculated using OpenMC at a 64×64 pixel spatial resolution and 8 neutron energy groups for input to the neural *** network upsamples the low-resolution neutron flux to 128×128 pixel spatial resolution and 16 neutron energy ***-resolution neutron flux tallies and their uncertainties were also calculated with OpenMC and compared with the network’s *** upsampled data and the high-resolution tally results agree to within the statistical uncertainty calculated by OpenMC.

关键词： OpenMC Convolutional neural network Residual network Neutron Monte Carlo

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