Research on Data-Driven Methods for Solving High-Dimensional Neutron Transport Equations

Zhiqiang Peng, Jichong Lei, Zining Ni, Tao Yu (), Jinsen Xie (), Jun Hong and Hong Hu
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Zhiqiang Peng: School of Nuclear Science and Technology, University of South China, Hengyang 421001, China
Jichong Lei: School of Nuclear Science and Technology, University of South China, Hengyang 421001, China
Zining Ni: School of Nuclear Science and Technology, University of South China, Hengyang 421001, China
Tao Yu: School of Nuclear Science and Technology, University of South China, Hengyang 421001, China
Jinsen Xie: School of Nuclear Science and Technology, University of South China, Hengyang 421001, China
Jun Hong: School of Safe and Management Engineering, Hunan Institute of Technology, Hengyang 421002, China
Hong Hu: School of Safe and Management Engineering, Hunan Institute of Technology, Hengyang 421002, China

Energies, 2024, vol. 17, issue 16, 1-11

Abstract: With the continuous development of computer technology, artificial intelligence has been widely applied across various industries. To address the issues of high computational cost and inefficiency in traditional numerical methods, this paper proposes a data-driven artificial intelligence approach for solving high-dimensional neutron transport equations. Based on the AFA-3G assembly model, a neutron transport equation solving model is established using deep neural networks, considering factors that influence the neutron transport process in real engineering scenarios, such as varying temperature, power, and boron concentration. Comparing the model’s predicted values with reference values, the average error in the infinite multiplication factor k inf of the assembly is found to be 145.71 pcm (10 −5 ), with a maximum error of 267.10 pcm. The maximum relative error is less than 3.5%, all within the engineering error standards of 500 pcm and 5%. This preliminary validation demonstrates the feasibility of using data-driven artificial intelligence methods to solve high-dimensional neutron transport equations, offering a new option for engineering design and practical engineering computations.

Keywords: data-driven; deep neural networks; infinite multiplication factor k inf; neutron transport equation (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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