BP neural network based reconstruction method for radiation field applications

doi:10.1016/j.nucengdes.2021.111228

	BP neural network based reconstruction method for radiation field applications
	Zhou, Wen 1,2; Sun, Guomin 1; Yang, Zihui 1; Wang, Hui 1; Fang, Li 1,2; Wang, Jianye 1
刊名	NUCLEAR ENGINEERING AND DESIGN
	2021-08-15
卷号	380
关键词	Radiation field Shielding calculation Regression analysis BP neural network SuperMC
ISSN号	0029-5493
DOI	10.1016/j.nucengdes.2021.111228
通讯作者	Sun, Guomin()
英文摘要	In order to achieve optimal radiation protection, rapid and accurate reconstruction of radiation field has vital significance in the selection of working paths during the overhaul of nuclear power plants and the decommissioning of nuclear facilities. The radiation field is usually reconstructed by various interpolation methods, but the reconstruction accuracy of such methods is insufficient, With the improvement of AI technology, neural networks have great potential in radiation field reconstruction, but conventional neural networks is prone to local minima and vanishing grandient problem. This paper aims to develop a radiation field reconstruction method based on an adaptive Back-propagation (BP) neural network neural network method with learning rate decay and a corresponding sampling method for multisampling in places where flux gradient changes drastically, and verify its accuracy and feasibility. The proposed method achieves global optimality and avoids vanishing grandient problem by virtue of adaptive algorithm and learning rate decay, ensuring that the radiation field is reconstructed with the smallest relative average error when the sampling point is determined, moreover, the proposed sampling method can greatly improve the accuracy of radiation field reconstruction. The accuracy of the proposed method was tested with three MC simulated radiation fields with simpler cases, and the feasibility of the proposed method was further validated with two MC simulated, more complex and realistic scenes. The results of the proposed method show that the errors of the three test cases are 1.7%, 6.8%, and 7.8%, and the errors of the two validated cases are 8.8% and 7.7%, respectively. The merit of this method was preliminarily verified, further validation is underway to validate its application in real world scenarios.
资助项目	National Key Research and Development Program of China[2019YFE0191700] ; Natural Science Foundation of Anhui Province[2008085MA23]
WOS关键词	SIMULATION
WOS研究方向	Nuclear Science & Technology
语种	英语
出版者	ELSEVIER SCIENCE SA
WOS记录号	WOS:000674617200004
资助机构	National Key Research and Development Program of China ; Natural Science Foundation of Anhui Province
内容类型	期刊论文
源URL	[http://ir.hfcas.ac.cn:8080/handle/334002/123305]
专题	中国科学院合肥物质科学研究院
通讯作者	Sun, Guomin
作者单位	1.Chinese Acad Sci, Hefei Inst Phys Sci, Key Lab Neutron & Radiat Safety, Hefei 230031, Anhui, Peoples R China 2.Univ Sci & Technol China, Hefei 230027, Anhui, Peoples R China
推荐引用方式 GB/T 7714	Zhou, Wen,Sun, Guomin,Yang, Zihui,et al. BP neural network based reconstruction method for radiation field applications[J]. NUCLEAR ENGINEERING AND DESIGN,2021,380.
APA	Zhou, Wen,Sun, Guomin,Yang, Zihui,Wang, Hui,Fang, Li,&Wang, Jianye.(2021).BP neural network based reconstruction method for radiation field applications.NUCLEAR ENGINEERING AND DESIGN,380.
MLA	Zhou, Wen,et al."BP neural network based reconstruction method for radiation field applications".NUCLEAR ENGINEERING AND DESIGN 380(2021).