基于GA的Tokamak聚变堆芯参数优化方法研究

摘要

托卡马克(Tokamak)聚变堆芯参数优化设计是聚变及聚变驱动次临界堆设计的重要步骤之一.本文发展了基于遗传算法(GA)的堆芯参数优化方法并与中国科学院核能安全技术研究所·FDS团队研发的系统程序SYSCODE堆芯物理模块相耦合,对堆芯参数进行优化.通过优化指定的聚变堆芯设计参数(如几何尺寸、等离子体电流、环向磁场等),并满足给定的约束条件,使得单个或多个目标达到全局"最优",对于堆芯设计具有一定参考价值.%It is an important step for fusion reactor and fusion-fission hybrid reactor to parameters optimization for Tokamak fusion reactor core.A parameter optimization procedure for fusion reactor core based on genetic algorithm (GA)was developed and coupled with reactor core physics model of SYSCODE which has been developed by FDS team,and is used for optimizing reactor core parameters.With adjusting the value of reactor core parameters such as geometric parameters, plasma current, toroidal magnetic field and so on, this optimization method can obtain global "optimum solution"about single or multiple objectives and which can provide reference value for design of fusion reactor core.