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

Author

Listed:
  • Zhiqiang Peng

    (School of Nuclear Science and Technology, University of South China, Hengyang 421001, China
    School of Safe and Management Engineering, Hunan Institute of Technology, Hengyang 421002, China
    Key Lab of Advanced Nuclear Energy Design and Safety, Ministry of Education, Hengyang 421001, China)

  • Jichong Lei

    (School of Nuclear Science and Technology, University of South China, Hengyang 421001, China
    School of Safe and Management Engineering, Hunan Institute of Technology, Hengyang 421002, China
    Key Lab of Advanced Nuclear Energy Design and Safety, Ministry of Education, Hengyang 421001, China)

  • Zining Ni

    (School of Nuclear Science and Technology, University of South China, Hengyang 421001, China
    School of Safe and Management Engineering, Hunan Institute of Technology, Hengyang 421002, China
    Key Lab of Advanced Nuclear Energy Design and Safety, Ministry of Education, Hengyang 421001, China)

  • Tao Yu

    (School of Nuclear Science and Technology, University of South China, Hengyang 421001, China
    Key Lab of Advanced Nuclear Energy Design and Safety, Ministry of Education, Hengyang 421001, China)

  • Jinsen Xie

    (School of Nuclear Science and Technology, University of South China, Hengyang 421001, China
    Key Lab of Advanced Nuclear Energy Design and Safety, Ministry of Education, 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)

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.

Suggested Citation

  • Zhiqiang Peng & Jichong Lei & Zining Ni & Tao Yu & Jinsen Xie & Jun Hong & Hong Hu, 2024. "Research on Data-Driven Methods for Solving High-Dimensional Neutron Transport Equations," Energies, MDPI, vol. 17(16), pages 1-11, August.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:16:p:4153-:d:1460515
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    References listed on IDEAS

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    1. Michal Pikus & Jarosław Wąs, 2023. "Using Deep Neural Network Methods for Forecasting Energy Productivity Based on Comparison of Simulation and DNN Results for Central Poland—Swietokrzyskie Voivodeship," Energies, MDPI, vol. 16(18), pages 1-15, September.
    2. Harleen Kaur Sandhu & Saran Srikanth Bodda & Abhinav Gupta, 2023. "A Future with Machine Learning: Review of Condition Assessment of Structures and Mechanical Systems in Nuclear Facilities," Energies, MDPI, vol. 16(6), pages 1-23, March.
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