Intelligent Optimization of an HTS Maglev System With Translational Symmetry

Optimization is essential to reduce the cost of high-temperature superconducting (HTS) maglev systems composed of a permanent magnetic guideway (PMG) and an HTS unit and thus promote their application. In this paper, the geometrical shape of a Halbach-derived PMG is optimized via an intelligent genetic algorithm to devise a more cost-effective system. We compute the levitation force of the HTS unit above the PMG with self-made finite-element codes, using the Kim–Bean current–voltage relationship in the governing equation of the 2-D model of HTS maglev system. Then, we introduce how the genetic algorithm is coupled with this 2-D model. Taking the levitation force of the HTS unit at a vertical distance above the PMG as the objective function, considering some practical geometry and cost constraints, we optimized a kind of Halbach-derived PMG. This optimization approach can provide a more general rule for the future design of the HTS Maglev system.