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Artificial intelligence in reactor physics:current status and future prospects 认领 引用
1
作者 Rui-Zhi Zhang Sheng-Feng Zhu +5 位作者 Kan Wang Ding She Jean-Philippe Argaud Bertrand Bouriquet Qing Li He-Lin Gong 《Nuclear Science and Techniques》 SCIE EI CAS CSCD 2026年第6期196-231,共36页
Reactor physics is the study of neutron properties,focusing on the use of models to examine the interactions between neutrons and materials in nuclear reactors.Artificial intelligence(AI)has made significant contribut... Reactor physics is the study of neutron properties,focusing on the use of models to examine the interactions between neutrons and materials in nuclear reactors.Artificial intelligence(AI)has made significant contributions to reactor physics,such as in operational simulations,safety design,real-time monitoring,core management,and maintenance.This paper presents a comprehensive review of AI approaches in reactor physics,especially considering the category of Machine Learning(ML,which we also refer to as AI/ML to recall the AI name we found in articles),with the aim of describing the application scenarios,frontier topics,unsolved challenges,and future research directions.From equation solving and state parameter prediction to nuclear industry applications,this study provides a step-by-step overview of ML methods applied to steadystate,transient,and burnup problems.Most studies have achieved industry-demanded models by enhancing the efficiency of deterministic methods or correcting uncertainty methods,which leads to successful applications.However,research on ML methods in reactor physics is somewhat fragmented,and the ability to generalize models must be strengthened.Progress is still possible,especially in addressing theoretical challenges and enhancing industrial applications,such as building surrogate models and digital twins. 展开更多
关键词 Reactor physics Artificial intelligence Machine learning Neutron governing equations Fuel burnup Simulation Core design Monitoring
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Physics-constrained neural network for solving discontinuous interface K-eigenvalue problem with application to reactor physics 认领 引用 被引量：12
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作者 Qi-Hong Yang Yu Yang +3 位作者 Yang-Tao Deng Qiao-Lin He He-Lin Gong Shi-Quan Zhang 《Nuclear Science and Techniques》 SCIE EI CAS CSCD 2023年第10期178-200,共23页
Machine learning-based modeling of reactor physics problems has attracted increasing interest in recent years.Despite some progress in one-dimensional problems,there is still a paucity of benchmark studies that are ea... Machine learning-based modeling of reactor physics problems has attracted increasing interest in recent years.Despite some progress in one-dimensional problems,there is still a paucity of benchmark studies that are easy to solve using traditional numerical methods albeit still challenging using neural networks for a wide range of practical problems.We present two networks,namely the Generalized Inverse Power Method Neural Network(GIPMNN)and Physics-Constrained GIPMNN(PC-GIPIMNN)to solve K-eigenvalue problems in neutron diffusion theory.GIPMNN follows the main idea of the inverse power method and determines the lowest eigenvalue using an iterative method.The PC-GIPMNN additionally enforces conservative interface conditions for the neutron flux.Meanwhile,Deep Ritz Method(DRM)directly solves the smallest eigenvalue by minimizing the eigenvalue in Rayleigh quotient form.A comprehensive study was conducted using GIPMNN,PC-GIPMNN,and DRM to solve problems of complex spatial geometry with variant material domains from the fleld of nuclear reactor physics.The methods were compared with the standard flnite element method.The applicability and accuracy of the methods are reported and indicate that PC-GIPMNN outperforms GIPMNN and DRM. 展开更多
关键词 Neural network Reactor physics Neutron diffusion equation Eigenvalue problem Inverse power method
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Reactor field reconstruction from sparse and movable sensors using Voronoi tessellation-assisted convolutional neural networks 认领 引用 被引量：3
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作者 He-Lin Gong Han Li +1 位作者 Dunhui Xiao Sibo Cheng 《Nuclear Science and Techniques》 SCIE EI CAS CSCD 2024年第5期173-185,共13页
The aging of operational reactors leads to increased mechanical vibrations in the reactor interior.The vibration of the incore sensors near their nominal locations is a new problem for neutronic field reconstruction.C... The aging of operational reactors leads to increased mechanical vibrations in the reactor interior.The vibration of the incore sensors near their nominal locations is a new problem for neutronic field reconstruction.Current field-reconstruction methods fail to handle spatially moving sensors.In this study,we propose a Voronoi tessellation technique in combination with convolutional neural networks to handle this challenge.Observations from movable in-core sensors were projected onto the same global field structure using Voronoi tessellation,holding the magnitude and location information of the sensors.General convolutional neural networks were used to learn maps from observations to the global field.The proposed method reconstructed multi-physics fields(including fast flux,thermal flux,and power rate)using observations from a single field(such as thermal flux).Numerical tests based on the IAEA benchmark demonstrated the potential of the proposed method in practical engineering applications,particularly within an amplitude of 5 cm around the nominal locations,which led to average relative errors below 5% and 10% in the L2 and L∞norms,respectively. 展开更多
关键词 Voronoi tessellation Field reconstruction Nuclear reactors Reactor physics On-line monitoring
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