基于数据驱动的移动式微型核反应堆屏蔽智能优化设计研究  

Research on Data-driven Intelligent Optimization Design of Mobile Microreactor Shielding

作　　者：雷铠灰 吴宏春[1] 贺清明[1] 曹毅[2] 李晓静 刘国明 Lei Kaihui;Wu Hongchun;He Qingming;Cao Yi;Li Xiaojing;Liu Guoming(School of Nuclear Science and Technology,Xi’an Jiaotong University,Xi’an,710049,China;State Key Laboratory for Manufacturing System Engineering,Xi’an Jiaotong University,Xi’an,710054,China;China Nuclear Power Engineering Co.,Ltd.,Beijing,100840,China)

机构地区：[1]西安交通大学核科学与技术学院,西安710049 [2]西安交通大学机械制造系统工程国家重点实验室,西安710054 [3]中国核电工程有限公司,北京100840

出　　处：《核动力工程》2025年第2期193-201,共9页Nuclear Power Engineering

基　　金：国家自然科学基金(12375175);中核集团领创科研项目基金。

摘　　要：为快速获得满足工程偏好的移动式微型核反应堆(微堆)轻量化屏蔽设计方案,本研究采用基于数据驱动的代理模型和多目标智能优化算法,对陆基移动式微堆的运行屏蔽开展具有多约束和工程偏好的多目标优化设计。首先通过对先进屏蔽材料参数和屏蔽几何参数采样构建变规模优化空间下的数据集,其次基于此数据集训练多频率尺度神经网络MscaleDNN,并耦合基于图形处理器(GPU)并行的一维离散纵标法(SN)中子-光子耦合输运求解器,以建立稳定的高效高精度剂量率预测代理模型SN-MscaleDNN,之后与引入罚函数法和工程偏好模型的第二代非支配排序(NSGA-II)遗传算法耦合,实现满足剂量率安全、材料和力学限制等多约束以及工程偏好的屏蔽优化设计。研究结果表明,代理模型在变规模优化空间下可实现单个屏蔽方案毫秒级评估且预测泛化误差整体在10%以内,其与优化算法耦合后优化得到的多个屏蔽方案满足各项指标限值和工程偏好,本研究建立的方法能够用于变规模优化空间下移动式微堆的轻量化屏蔽优化设计。In order to quickly obtain the lightweight shielding design scheme of mobile microreactor(microreactor)that meets the engineering preferences,a multi-objective intelligent optimization algorithm coupled with the data-driven surrogate model is employed to optimize the operational shielding of a land-based mobile microreactor based on multiple constraints and engineering preferences.We initially construct the dataset by sampling advanced shielding material and geometry’s parameters in the variable-scale optimization space and train the surrogate model(SN-MscaleDNN),which consists of the multi-frequency scale neural network called MscaleDNN and the GPU-parallel 1-D neutron-photon coupling transport SN solver,to achieve stable,accurate,and efficient dose rate prediction.This model is then coupled with the NSGA-II genetic algorithm,incorporating penalty functions and engineering preference models,to achieve the final shielding optimization that satisfies multiple constraints such as dose rate safety,material and mechanical limitations and engineering preferences.The results confirm the surrogate model's ability to accurately predict dose rates of one shielding scheme at a millisecond level with its generalization error under 10%.Furthermore,the coupled optimization algorithm enables the efficient search for more shielding schemes that meet engineering constraints and preferences.The method established in this study can be used for lightweight shielding optimization design of mobile microreactor in a variable-scale optimization space.

关 键 词：屏蔽优化设计 多目标优化 神经网络 移动式微型核反应堆 代理模型 数据驱动 

分 类 号：TL328[核科学技术—核技术及应用]