Self-Shielded Multigroup Microscopic Cross Section Generation Using Targeted Artificial Neural Networks
Abstract
Multigroup neutron transport models are used widely in simulation codes due to their memory and compute efficiency compared to continuous energy methods. However, multigroup codes rely on nuclear data prepared using approximate methods that require significant manual effort and expertise to account for self-shielding effects. In addition, multigroup nuclear data libraries prepared in this way can be limited in their applicability for modeling diverse reactors and operating conditions. Here we show that trained artificial neural networks can predict self-shielded microscopic cross sections accurately in pincell simulations containing uranium dioxide fuel with a fixed geometry and temperature. The model's predictive accuracy remains high across fuel enrichment and burnup ranges encountered in light water reactors. Critically, the neural networks' predictions require only the atomic concentration of the fuel's constituent nuclides as inputs. The networks are trained using 8,704 samples of training and validation data generated using continuous energy OpenMC pincell simulations. The trained neural networks predict self-shielded total, fission, absorption, elastic, and total scattering cross sections for combinations of 90 nuclides in the CASMO-8 energy group structure. The neural networks' predictions of self-shielded cross sections are validated by comparison with 1,500 samples of reference cross sections unseen during training and tallied using OpenMC simulations. The predicted and ground truth cross sections in the test data set agree to within 0.119% on average. When the predicted cross sections are used in a multigroup eigenvalue run, they achieve a mean absolute error in keff of 200 pcm.
Related
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BibTeX
@misc{nichols2026selfshielded,
title = {{Self-Shielded Multigroup Microscopic Cross Section Generation Using Targeted Artificial Neural Networks}},
author = {Joshua Nichols and Andrew Osborne and Ben Whewell and Nathan Gibson},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.10464},
eprint = {2610.10464},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.10464},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Self-Shielded Multigroup Microscopic Cross Section Generation Using Targeted Artificial Neural Networks
AU - Joshua Nichols
AU - Andrew Osborne
AU - Ben Whewell
AU - Nathan Gibson
PY - 2026
DA - 2026/10/07
PB - arXiv
DO - 10.48550/arxiv.2610.10464
UR - https://arxiv.org/abs/2610.10464
AB - Multigroup neutron transport models are used widely in simulation codes due to their memory and compute efficiency compared to continuous energy methods. However, multigroup codes rely on nuclear data prepared using approximate methods that require significant manual effort and expertise to account for self-shielding effects. In addition, multigroup nuclear data libraries prepared in this way can be limited in their applicability for modeling diverse reactors and operating conditions. Here we show that trained artificial neural networks can predict self-shielded microscopic cross sections accurately in pincell simulations containing uranium dioxide fuel with a fixed geometry and temperature. The model's predictive accuracy remains high across fuel enrichment and burnup ranges encountered in light water reactors. Critically, the neural networks' predictions require only the atomic concentration of the fuel's constituent nuclides as inputs. The networks are trained using 8,704 samples of training and validation data generated using continuous energy OpenMC pincell simulations. The trained neural networks predict self-shielded total, fission, absorption, elastic, and total scattering cross sections for combinations of 90 nuclides in the CASMO-8 energy group structure. The neural networks' predictions of self-shielded cross sections are validated by comparison with 1,500 samples of reference cross sections unseen during training and tallied using OpenMC simulations. The predicted and ground truth cross sections in the test data set agree to within 0.119% on average. When the predicted cross sections are used in a multigroup eigenvalue run, they achieve a mean absolute error in keff of 200 pcm.
ER -