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 gene...
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Published in | IEEE transactions on applied superconductivity Vol. 26; no. 4; pp. 1 - 5 |
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Main Authors | , , , |
Format | Journal Article |
Language | English |
Published |
New York
IEEE
01.06.2016
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subjects | |
Online Access | Get full text |
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Summary: | 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. |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
ISSN: | 1051-8223 1558-2515 |
DOI: | 10.1109/TASC.2016.2519280 |