基于BP神经网络的高通量铅铋反应堆中子通量最大化设计方法研究  

Design method of high-flux lead-bismuth cooled reactor neutron flux maximization based on BP neural network

作　　者：王彤 刘紫静[1,2] 赵鹏程[1,2] 肖英杰 WANG Tong;LIU Zijing;ZHAO Pengcheng;XIAO Yingjie(College of Nuclear Science and Technology,University of South China,Hengyang 421001,China;Hunan Digital Reactor Engineering and Technology Research Center,University of South China,Hengyang 421001,China)

机构地区：[1]南华大学核科学技术学院,衡阳421001 [2]南华大学湖南省数字化反应堆工程技术研究中心,衡阳421001

出　　处：《核技术》2024年第10期156-168,共13页Nuclear Techniques

基　　金：装备预研教育部联合基金青年人才项目(No.8091B032243)资助。

摘　　要：优化设计高通量铅铋反应堆对于缓解快中子研究堆辐照资源稀缺难题、支撑先进核能系统的发展具有重要意义。本文以提升堆芯中子通量水平为目标,针对高通量铅铋反应堆复杂的多维非线性约束优化问题,依托反应堆蒙特卡罗程序RMC和子通道程序Subchanflow,构建了基于BP神经网络(Back Propagation Neural Network)算法的预测模型,并结合Sobol指数法开展堆芯设计参数的敏感性分析,提出了基于BP神经网络动态代理模型(Dynamic Surrogate Model,DSM)更新迭代的设计优化方法,开发了高通量铅铋反应堆设计优化平台。以多功能超高通量堆为例开展堆芯栅径比、燃料芯块直径、活性区高度、径向反射层厚度的多堆芯参数协同优化验证,结果表明:该优化方法对堆芯中子通量密度与有效增殖因数keff的预测精度误差在0.1%之内,在4组堆芯设计变量单独作用和共同作用下对于最大中子通量的影响程度都按照反射层厚度<栅径比<活性区高度<燃料芯块直径的递增顺序排列,优化后的中子通量密度相比原始设计方案提高了15.41%,表明开发的高通量铅铋反应堆设计优化平台有效可靠。[Background]The development of high-throughput reactors is of great significance for supporting the development of nuclear science and technology,improving the efficiency of nuclear energy utilization,meeting the needs of radioactive isotope production,and carrying out irradiation tests and performance tests of new nuclear fuels and structural materials in reactors.Due to the high power density of the core fuel and the large demand for thermal cooling,the nuclear-thermal coupling phenomenon in the high-throughput lead-bismuth reactor(HT-LBR)is more significant than that in conventional lead-bismuth reactor(LBR).When the design optimization of high flux LBR is carried out,it is necessary to carry out collaborative optimization of multiple core parameters,improve the neutron flux density,and meet the physical/thermal constraints such as core refueling period,fuel cladding temperature and coolant flow rate.Therefore,the design optimization of high flux lead-bismuth cooled reactor is a complex problem of multi-physics,multi-variable and multi-constraint coupling.[Purpose]This study aims to improve the neutron flux level of LBR and solve the optimization design problem of HT-LBR.[Methods]Firstly,a HT-LBR training database was constructed to contain different core design parameter combinations and corresponding objective function response values and constraint condition response values.Based on the reactor Monte Carlo code RMC and sub-channel Code Subchanflow,a Back-Propagation(BP)neural network prediction model was established as a proxy model for reactor physical calculation and analysis to achieve rapid prediction of core neutron flux density and effective multiplication factor using aforementioned training database.Secondly,an updated iterative optimization method based on BP neural network Dynamic Surrogate Model(DSM)was proposed to improve the optimization efficiency and global optimization ability,and search for the optimal HT-LBR core design parameter combination within the design range.Thirdly,based on the ope

关 键 词：铅铋反应堆 高通量堆 BP神经网络 动态代理模型 优化方法 中子通量密度 

分 类 号：TL411.3[核科学技术—核技术及应用]