Intelligent Optimization of an HTS Maglev System With Translational Symmetry

• Published in: IEEE transactions on applied superconductivity Vol. 26; no. 4; pp. 1 - 5
• Main Authors: Ye, Chang-Qing, Ma, Guang-Tong, Liu, Kun, Wang, Jia-Su
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.06.2016; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Design optimization; Finite element analysis; Force; Genetic algorithms; High temperature superconductor; High-temperature superconductors; Levitation; Maglev optimization; Magnetic levitation systems; Magnetic levitation vehicles; Mathematical analysis; Mathematical model; Mathematical models; Optimization; permanent magnetic guideway; Superconductivity; permanent magnetic guideway; High-temperature superconductor; optimization; Maglev
• ISSN: 1051-8223; 1558-2515
• DOI: 10.1109/TASC.2016.2519280

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.