基于NSGA-Ⅲ的多目标中子屏蔽材料优化设计

Research on optimization design of multi-objective neutron shielding materials based on NSGA-Ⅲ

针对车载/船载、空间等小型模块化核反应堆中子屏蔽设计中的减重降体需求,以碳化硼、铁、钨、铅、氧化铋、聚乙烯和NBS混凝土均匀混合的复合材料为例,开展材料组分和厚度的多目标优化模拟设计研究。基于MCNP5模型模拟计算结果训练自适应RBF神经网络剂量当量预测模型,预测相对偏差在-2%~2%之间。利用基于参考点的非支配排序遗传算法NSGA-Ⅲ对屏蔽材料质量、体积和屏蔽性能3个目标函数进行优化,分析Pareto最优解集,验证优化方法的可行性,为中子屏蔽材料在实际工程应用中的的多目标优化设计提供方法和理论指导。

In order to optimize the design of neutron shielding materials,a study is carried out with a homogeneous mixture of composite materials consisting of boron carbide,iron,tungsten,lead,bismuth oxide,polyethylene and NBS concrete.The evolutionary multi-objective optimization design of material composition and thickness is performed.Based on the simulation results of MCNP5,an adaptive RBF neural network dose prediction model is trained.The reference point-based non-dominated sorting genetic algorithm NSGA-Ⅲ is used to optimize three objective functions:weight,volume,and shielding property of the shielding material.The Pareto-optimal solution set is analyzed to verify the feasibility of the optimization method and provide methods and theoretical guidance for the multi-objective optimization design of neutron shielding materials.