Upsampling Monte Carlo Reactor Simulation Tallies in Depleted Sodium-Cooled Fast Reactor Assemblies Using a Convolutional Neural Network Article Swipe
J. A. Berry
,
Paul Romano
,
Andrew Osborne
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/en17092177
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/en17092177
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.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/en17092177
- OA Status
- gold
- Cited By
- 1
- References
- 15
- Related Works
- 10
- OpenAlex ID
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https://openalex.org/W4396599345Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/en17092177Digital Object Identifier
- Title
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Upsampling Monte Carlo Reactor Simulation Tallies in Depleted Sodium-Cooled Fast Reactor Assemblies Using a Convolutional Neural NetworkWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
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2024-05-02Full publication date if available
- Authors
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J. A. Berry, Paul Romano, Andrew OsborneList of authors in order
- Landing page
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https://doi.org/10.3390/en17092177Publisher landing page
- Open access
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://doi.org/10.3390/en17092177Direct OA link when available
- Concepts
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Monte Carlo method, Convolutional neural network, Sodium-cooled fast reactor, Computer science, Artificial neural network, Neutron, Neutron transport, Nuclear engineering, Physics, Artificial intelligence, Nuclear physics, Mathematics, Engineering, StatisticsTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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