基于压缩感知理论的中子能谱解谱方法  

Neutron Spectrum Unfolding Method Based on Compressed Sensing Theory

作　　者：吴广皓 邵刚 时光 刘斌[4,5] 潘良明[4,5] 王锋[4,5] 周小为 WU Guanghao;SHAO Gang;SHI Guang;LIU Bin;PAN Liangming;WANG Feng;ZHOU Xiaowei(Chengdu Hezong Nuclear Power Engineering Co.,Ltd.,Nuclear Power Institute of China,Chengdu 610213,China;Fujian Fuqing Nuclear Power Co.,Ltd.,Fuqing 350300,China;Civil Engineering Design Sub-institute,Nuclear Power Institute of China,Chengdu 610213,China;Key Laboratory of Low-grade Energy Utilization Technologies and Systems,Ministry of Education,Chongqing University,Chongqing 400044,China;Department of Nuclear Engineering and Technology,Chongqing University,Chongqing 400044,China)

机构地区：[1]中国核动力研究设计院成都核总核动力研究设计工程有限公司,四川成都610213 [2]福建福清核电有限公司,福建福清350300 [3]中国核动力研究设计院建筑设计所,四川成都610213 [4]重庆大学低品位能源利用技术及系统教育部重点实验室,重庆400044 [5]重庆大学核工程与核技术系,重庆400044

出　　处：《原子能科学技术》2024年第6期1311-1318,共8页Atomic Energy Science and Technology

摘　　要：准确的中子能谱信息对于核装置的设计和运行具有十分重要的意义,现有解谱方法通常将先验信息作为迭代初值使用,限制了解谱过程多重先验信息的使用。本文针对中子能谱测量中的解谱问题,建立了基于压缩感知理论的中子能谱解谱方法,并应用于几种典型中子能谱的多球谱仪测量和辐照监督管处中子能谱的活化片测量中的中子能谱解谱问题,结果表明:所建立的解谱方法可实现一次解谱中多重先验信息的使用,可有效实现典型中子能谱和反应堆辐照监督管处中子能谱的解谱,解谱结果与标准解吻合良好。另外,由于多重先验信息的使用,使得解谱过程对方程数量的依赖降低,去除^(238)U和^(237)Np等裂变探测器时辐照监督管处中子能谱依然可得到较高精度的求解,为辐照监督项目去除裂变探测器奠定了理论基础。Accurate information of neutron spectrum is very important for design and operation of nuclear facilities.The current unfolding methods commonly use the prior information as the initial values of the iteration process,which limits implementation of multiple prior information during the unfolding process and accuracies of the unfolding results are difficult to be enhanced.In this paper,a neutron spectrum unfolding method based on compressed sensing theory was proposed.Multiple prior information can be implemented into the unfolding process due to the basic principle of the compressed sensing theory.The proposed method includes two processes,such as the sparse representation process and the sparse reconstruction process.Two kinds of algorithms,the K-SVD algorithm and the online dictionary learning algorithm,were applied for sparse representation.The K-SVD algorithm is efficient and easy to be implemented.However,computation always fail while the singularity of the training matrix increases.The online dictionary learning algorithm uses the stochastic approximation.It assumes the training set as a distribution and processes one sample from the distribution during each iteration.Hence,the online dictionary learning algorithm can effectively avoid the computation failure caused byℓ0ℓ1matrix singularity.Algorithms based on-norm and-norm were applied for sparse reconstruction.ℓ0The-norm based algorithm has the closet meaning to sparsity but lacks the ability of suppressingℓ1noises containing in the measured data.The-norm based algorithm equivalents the sparse reconstruction to the LASSO equation,which has better performance on suppressing noises.The proposed unfolding method was applied to unfolding problems of multi-sphere spectrometer of measuring several typical neutron spectra.The K-SVD algorithm was applied for sparse representationℓ0and the-norm based algorithm was applied for sparse reconstruction.The unfolded spectra agree well with the standard solutions.High accuracies can be obtained with implementat

关 键 词：中子能谱解谱 压缩感知理论 稀疏表示算法 稀疏重构算法 

分 类 号：TL413.3[核科学技术—核技术及应用]