基于遗传算法的聚变驱动次临界堆嬗变包层长寿命锕系元素初装料中子学优化

Neutronic optimization based on genetic algorithm for the initial loading long-lived minor actinides in the blanket for the fusion-driven subcritical system

在聚变驱动次临界堆的多功能核废料嬗变包层中,长寿命锕系废料的嬗变处理是中子学设计中非常关心的问题。利用FDS课题组开发的多功能中子学程序系统VisualBUS1.0,针对该系统燃耗计算过程具有多变量和多目标函数复杂关系的特点,应用遗传算法对嬗变包层的锕系废料嬗变区的初装料量进行了优化处理,使其在一定的物理和工程参数约束下,指导嬗变区的装料份额取值,分析嬗变区的装料份额对锕系废料的年燃耗深度等参数的影响。

The efficient and effective transmutation of Long-Lived Minor Actinides (LL-MA) is an important goal in neutronics design and analysis of dual-cooled multi-functional waste transmutation blanket (DWTB) for the Fusion-Driven Subcritical system (FDS). Considering the characteristics of multi-variables and multi-objectives in the burnup calculation process of nuclear system, the optimization control method based on GA (Genetic Algorithm) is presented as a functional module of the multi-functional (transport/burnup/optimization) neutronics program VisualBUSl. 0 which is home-developed by the FDS Team. The initial loading compositions in transmutation zone are optimized to obtain reasonable neutronics parameters.