Fast Optimization of Null-Flux Coils for Superconducting Electrodynamic Suspension Train With RSM and AMGA

In this article, a fast design optimization of null-flux coils (NFCs) was investigated to improve the performance of electrodynamic suspension (EDS) based on response surface method (RSM) and archive-based micro genetic algorithm (AMGA). The approximate model of levitation force was established using RSM. Six geometry parameters of NFCs were selected; the mass of NFCs per kilometer and the levitation force were chosen as the optimization objectives. In order to improve the efficiency of design optimization, the comprehensive sensitivity of the selected six variables was analyzed. Hence, four size parameters were determined as the design variables. A total of 1099 Pareto points were obtained through AMGA, and the optimal design variables were selected from the Pareto front. Finally, a finite element model was established to verify the authenticity of the mathematical model, and the electromagnetic characteristics before and after optimization were compared. The results indicated that the proposed optimization method could greatly improve the performance of EDS and reduce the construction cost per kilometer. The whole optimization design cycle only needs about 43 s, and has great significance for the efficient design of EDS.