Intelligent Optimization of an HTS Maglev System With Translational Symmetry
| Autor: | Jiasu Wang, Chang-Qing Ye, Kun Liu, Guangtong Ma |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
010302 applied physics
Computer science Vertical distance Condensed Matter Physics Governing equation 01 natural sciences Electronic Optical and Magnetic Materials Control theory Maglev 0103 physical sciences Genetic algorithm Levitation Electrical and Electronic Engineering 010306 general physics Translational symmetry Magnetic levitation |
| Zdroj: | IEEE Transactions on Applied Superconductivity. 26:1-5 |
| ISSN: | 2378-7074 1051-8223 |
| DOI: | 10.1109/tasc.2016.2519280 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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