粒子群算法改进及在高温超导磁体优化设计中的应用

Improvement of particle swarm optimization algorithm and its application in optimization design of high temperature superconducting magnets

本文阐述了基本粒子群优化方法及其改进,选用测试函数对该算法及其改进算法的寻优性能作了对比;并利用Comsol软件对单螺线管超导储能磁体进行了电磁计算,阐述了磁场垂直分量最大值的位置及高温超导磁体的磁场垂直分量对临界电流的影响;最后将各种优化方法应用于高温超导磁体的设计中。经联合仿真优化,高温超导磁体减小了体积,提高了材料利用率,节约了材料用量,并减小了制冷时所消耗的电源功率。

In this paper, the basic particle swarm optimization method and its improvement were described. The test function was used to compare the optimization performance of the particle swarm optimization algorithm and its improved algorithm. The Comsol software was used to conduct the electromagnetic calculation of the single solenoid superconducting energy storage magnet. The position of the maximum value of the vertical component of the magnetic field and the influence of the vertical component of the magnetic field of the high-temperature superconducting magnet on the critical current were expounded. Finally, various optimization methods were applied to the design of the high-temperature superconducting magnet. Optimized by joint simulation, the HTS magnet volume and power consumption were reduced, its material utilization was improved and material usage was saved.