摘要
为解决铅铋反应堆多因素耦合影响下的复杂非线性多维优化问题,构建了基于径向基(RBF)代理模型预测、正交拉丁超立方抽样(OLHS)和小生境遗传算法(NGA)寻优的堆芯智能优化方法,开发了包含抽样、蒙卡程序耦合处理、堆芯参数预测寻优等功能的铅铋反应堆设计优化平台,并以堆芯最小燃料装载量为优化目标进行方案寻优验证。研究结果表明:RBF代理模型可准确快速地预测铅铋反应堆堆芯特性参数,与蒙卡程序计算值比较,其预测的堆芯有效增殖因子(k_(eff))相对误差在±0.1%以内;该智能优化方法应用于铅铋反应堆堆芯优化是可行的,能找到多因素共同变化约束下的最优目标方案,且极大缩减了设计方案的搜索计算时间。本研究建立的堆芯智能优化方法可为铅铋反应堆多物理、多变量、多约束耦合影响的优化设计提供思路。
In order to solve the complex nonlinear multi-dimensional optimization problem under the influence of multi-factor coupling of lead-bismuth reactor,an intelligent optimization method for reactor core was constructed based on radial basis function(RBF)surrogate model prediction,orthogonal Latin hypercube sampling(OLHS)and niche genetic algorithm optimization.A design optimization platform for lead-bismuth reactor was developed,which included the functions of sampling,Monte Carlo program coupling treatment,and core parameter prediction and optimization.The scheme optimization verification was carried out with the minimum fuel loading of the core as the optimization objective.The results show that the RBF surrogate model can accurately and quickly predict the core characteristic parameters of the lead-bismuth reactor.Compared with the calculated values of the Monte Carlo program,the relative error of the predicted core effective multiplication factor k eff is within±0.1%.This intelligent optimization method is feasible for lead-bismuth reactor core optimization,which can find the optimal target scheme under the constraint of multi-factor co-variation,and greatly reduce the search calculation time of the design scheme.Therefore,the intelligent optimization method established in this study can provide new ideas for the optimization design of multi-physics,multi-variable and multi-constraint coupling effects of lead-bismuth reactor.
作者
李琼
刘紫静
王维嘉
赵鹏程
于涛
常浩彤
Li Qiong;Liu Zijing;Wang Weijia;Zhao Pengcheng;Yu Tao;Chang Haotong(School of Nuclear Science and Technology,University of South China,Hengyang,Hunan,421001,China;Hunan Engineering&Technology Research Center for Virtual Nuclear Reactor,University of South China,Hengyang,Hunan,421001,China)
出处
《核动力工程》
EI
CAS
CSCD
北大核心
2022年第6期93-100,共8页
Nuclear Power Engineering
基金
国家自然科学基金青年项目(12005097)
中央军委装备发展部预研项目(6142A07190106)
湖南省自然科学基金青年项目(2020JJ5465)
湖南省教育厅优秀青年项目(19B494)
湖南省研究生科研创新项目(CX20220991)。
