反应堆辐射屏蔽多目标优化方法研究  

Research of Multi-Objective Optimization Method of Nuclear Reactor Radiation Shielding

作　　者：张泽寰 宋英明[1] 卢川[2] 唐松乾[2] 肖锋[2] 吕焕文[2] 杨俊云 毛婕 Zhang Zehuan;Song Yingming;Lu Chuan;Tang Songqian;Xiao Feng;Lyu Huanwen;Yang Junyun;Mao Jie(School of Nuclear Science and Technology,University of South China,Hengyang,Hunan,421001,China;Science and Technology on Reactor System Design Technology Laboratory,Nuclear Power Institute of China,Chengdu,610213,China)

机构地区：[1]南华大学核科学技术学院,湖南衡阳421001 [2]中国核动力研究设计院核反应堆系统设计技术重点实验室,成都610213

出　　处：《核动力工程》2020年第5期178-184,共7页Nuclear Power Engineering

基　　金：国家自然科学基金青年基金(11905213)。

摘　　要：为解决基于蒙特卡罗方法的传统屏蔽优化方法效率低、可应用性差的缺点,本文基于精英策略的非支配排序遗传算法(NSGA-Ⅱ)和小批量随机梯度下降法(MBGD)对反应堆屏蔽优化方法进行了研究,同时改进了遗传算法自适应变异率算子以增强遗传算法的全局寻优能力,提出了反应堆屏蔽多目标优化方法。构建反应堆二次屏蔽多目标优化模型,将蒙特卡罗方法与神经网络预测方法输出的屏蔽后归一化中子透射率进行对比,验证了MBGD的准确性。通过神经网络与NSGA-Ⅱ的耦合对屏蔽参数进行约束寻优,能够快速找到屏蔽设计模型的Pareto前沿,可实际应用于反应堆辐射屏蔽多目标优化工程设计。To overcome the disadvantages in the efficiency and applicability of the traditional shielding optimization method based on the Monte Carlo method,in this paper,we studied the reactor radiation shielding optimization method by the non-dominated sorting genetic algorithm(NSGA-Ⅱ)based on the elitist strategy and the mini-batch gradient descent(MBGD),and improved the adaptive mutation rate operator of the genetic algorithm to enhance the global optimization ability of the genetic algorithm.A multi-objective optimization model of the reactor secondary shielding is constructed for comparing the output of normalized neutron transmittance between Monte Carlo method and neural network prediction method,which has verified the accuracy of MBGD.Through the coupling of neural network and NSGA-Ⅱ algorithm,the Pareto front of the radiation shielding design model can be found quickly,which can be applied to the multi-objective optimization engineering design of reactor radiation shielding.

关 键 词：辐射屏蔽 多目标优化 小批量随机梯度下降法(MBGD) 非支配排序遗传算法(NSGA-Ⅱ) 

分 类 号：TL99[核科学技术—核技术及应用]