A Data-Driven Method for Calculating Neutron Flux Distribution Based on Deep Learning and the Discrete Ordinates Method

Yanchao Li, Bin Zhang (), Shouhai Yang and Yixue Chen
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Yanchao Li: School of Nuclear Science and Engineering, North China Electric Power University, Beijing 102206, China
Bin Zhang: School of Nuclear Science and Engineering, North China Electric Power University, Beijing 102206, China
Shouhai Yang: State Key Laboratory of Nuclear Power Safety Technology and Equipment, China Nuclear Power Engineering Co., Ltd., Shenzhen 518172, China
Yixue Chen: School of Nuclear Science and Engineering, North China Electric Power University, Beijing 102206, China

Energies, 2024, vol. 17, issue 14, 1-24

Abstract: The efficient and accurate calculation of neutron flux distribution is essential for evaluating the safety of nuclear facilities and the surrounding environment. While traditional numerical simulation methods such as the discrete ordinates (S N ) method and Monte Carlo method have demonstrated excellent performance in terms of accuracy, their complex solving process incurs significant computational costs. This paper explores a data-driven and efficient method for obtaining neutron flux distribution based on deep learning, specifically targeting shielding problems with constant geometry and varying material cross-sections in practical engineering. The proposed method bypasses the intricate numerical transport calculation process of the discrete ordinates method by constructing a surrogate model that captures the correlation between transport characteristics and neutron flux from data characteristics. Simulations were carried out using Kobayashi-1 and Kobayashi-2 geometric models for shielding problems with constant geometry and varying material cross-sections. A series of validations have proved that the data-driven surrogate model demonstrates high generalization ability and reliability, while reducing the time required to obtain neutron flux distribution to 0.1 s without compromising on calculation accuracy compared to the discrete ordinates method.

Keywords: data-driven; deep learning; discrete ordinates method; surrogate model (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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