Research Progress in the Application of Artificial Intelligence in Reactor Neutron Analysis

Wu Hongchun; Lei Kaihui; Shen Jingwen

doi:10.13832/j.jnpe.2025.01.0001

Volume 46 Issue 1

Feb. 2025

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Wu Hongchun, Lei Kaihui, Shen Jingwen. Research Progress in the Application of Artificial Intelligence in Reactor Neutron Analysis[J]. Nuclear Power Engineering, 2025, 46(1): 1-12. doi: 10.13832/j.jnpe.2025.01.0001

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Wu Hongchun, Lei Kaihui, Shen Jingwen. Research Progress in the Application of Artificial Intelligence in Reactor Neutron Analysis[J]. Nuclear Power Engineering, 2025, 46(1): 1-12. doi: 10.13832/j.jnpe.2025.01.0001

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Research Progress in the Application of Artificial Intelligence in Reactor Neutron Analysis

doi: 10.13832/j.jnpe.2025.01.0001

School of Nuclear Science and Technology, Xi’an Jiaotong University, Xi’an, 710049, China

Received Date: 2024-10-25
Accepted Date: 2024-11-07
Rev Recd Date: 2024-11-15
Publish Date: 2025-02-15

Abstract

Abstract

Amid the global surge in scientific intelligence research and applications, artificial intelligence (AI) technology has been applied in various aspects of reactor neutron analysis to enhance its intelligence, precision and efficiency. This paper provides a comprehensive review of the research progress in the application of AI technologies within reactor neutron analysis, aiming to provide insights for advancing the digitalization of reactors and supporting future developments in this field. We first introduce AI methods' basic classifications and characteristics, and then investigate the four key aspects of neutron analysis: nuclear data evaluation, problem modeling, numerical solution of equations, and transport result application. Also critical technologies are reviewed and summarized for each aspect. Finally, the paper discusses challenges in AI-based neutron analysis, particularly in model, data, and application security. In response to these challenges, the paper proposes research recommendations.
- Reactor physics,
- Neutron analysis,
- Artificial intelligence,
- Physics-informed machine learning,
- Neutron transport

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References(94)

References

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