基于神经网络与遗传算法的压水堆堆芯装载模式方案优化研究  

Research on Optimization of Pressurized Water Reactor Core Loading Pattern Based on Neural Network and Genetic Algorithm

作　　者：陈刚 邹建 刘仕倡 蔡云[2] 王连杰[2] Chen Gang;Zou Jian;Liu Shichang;Cai Yun;Wang Lianjie(School of Nuclear Science and Engineering,North China Electric Power University,Beijing,100096,China;Nuclear Power Institute of China,Chengdu,610213,China)

机构地区：[1]华北电力大学核科学与工程学院,北京100096 [2]中国核动力研究设计院,成都610213

出　　处：《核动力工程》2025年第2期164-176,共13页Nuclear Power Engineering

基　　金：国家自然科学基金(U2330117、12175067);中央高校基本科研业务费专项资金(2024MS046)。

摘　　要：堆芯装载模式(LP)优化可以提高反应堆的安全性和经济性,但是其优化过程需要大量耗时计算和丰富的人工经验。针对堆芯LP优化方案的快速评价问题,本研究利用全连接神经网络(FCNN)和卷积神经网络(CNN)生成大亚湾核电站首循环堆芯的中子学参数快速预测模型,实现了压水堆堆芯LP方案的快速评价。通过堆芯计算程序DONJON对预测模型的泛化能力和精度进行验证。针对堆芯LP方案的全局搜索问题,利用非支配排序遗传算法(NSGA)对大亚湾核电站首循环堆芯进行LP方案多目标优化并通过NSGA的参数调整提升了优化效果。结果表明NSGA系列算法可以用于包括堆芯LP优化在内的不同类型的核设计优化问题,并可以弥补人工搜索方案全局性差的缺点,同时NSGA结合超算的并行优化可以极大提高优化效率。针对堆芯LP方案快速优化的问题,利用基于GPU并行下的神经网络预测模型和NSGA开发联合优化程序,实现了大亚湾核电站首循环堆芯LP方案的快速优化。通过联合优化程序与“DONJON+NSGA”的优化结果比较,结果表明神经网络-遗传算法(GA)的联合优化程序能够得到结果比较接近的堆芯LP方案,同时将优化时间缩短了99%以上。Core loading pattern(LP)optimization can enhance the safety and economy of reactors.However,its optimization process demands a considerable amount of time-consuming computations and extensive manual experience.Aiming at the rapid evaluation issue of core LP schemes,this study employed the fully connected neural network(FCNN)and convolutional neural network(CNN)to establish a rapid prediction model for the neutronic parameters of the Daya Bay first-cycle core,enabling a rapid assessment of the pressurized water reactor core LP scheme.The generalization ability and accuracy of the prediction model were verified through the core calculation code DONJON.Regarding the global search issue of the core optimization scheme,the non-dominated sorting genetic algorithm(NSGA)was utilized to carry out multi-objective optimization of the LP scheme for the Daya Bay first-cycle core,and the optimization effect was enhanced by adjusting the parameters of the NSGA algorithm.The results suggest that the NSGA series of algorithms can be applied to various types of nuclear design optimization,including core LP optimization,and can make up for the poor global nature of manual search schemes.Simultaneously,the parallel optimization of the NSGA algorithm in combination with supercomputing can significantly enhance the optimization efficiency.For the rapid optimization of the core LP scheme,a joint optimization code was developed by leveraging the neural network prediction model based on GPU parallelism and the NSGA algorithm,achieving rapid optimization of the Daya Bay first-cycle core LP scheme.By comparing the optimization results of the joint optimization code with those of"DONJON+NSGA",it is shown that the joint optimization code of the neural network-genetic algorithm can obtain core LP schemes with relatively similar results while reducing the optimization time by over 99%.

关 键 词：堆芯装载模式(LP) 神经网络 非支配排序遗传算法(NSGA) 多目标优化 预测模型 

分 类 号：TL334[核科学技术—核技术及应用]