An efficient scheme to optimize the superconducting levitation via genetic algorithm
Chang-Qing Ye, Guang-Tong Ma, Xing-Tian Li, Han Zhang, Peng-Bo Zhou,, Chen Yang, and Jia-Su Wang

TL;DR
This paper presents an efficient genetic algorithm-based scheme for optimizing superconducting levitation systems, directly modeling HTS elements without simplifications, leading to robust and quick results for practical maglev applications.
Contribution
The study introduces a novel optimization scheme that accurately models HTS behavior and efficiently searches for optimal PMG configurations using genetic algorithms.
Findings
Optimization time is only 3.6 hours on a standard desktop.
The scheme effectively finds optimal PMG designs for maximum levitation force.
Case studies reveal how HTS properties influence levitation performance.
Abstract
The superconducting levitation consisting of high-temperature superconductors (HTSs) and permanent magnet guideway (PMG) is deemed promising technique for the advancement of the maglev transit. To improve the cost-efficiency and thus reduce the investment of this superconducting levitation transit, the optimization of the PMG is the most critical issue of practical interest since it serves as the continuous rail to generate the magnetic field by the rare-earth magnets. By the use of a generalized vector potential within the quasistatic approximation as the state variable to mathematically describe the HTS as well as the surrounding medium, an efficient scheme for optimizing the superconducting levitation has been developed with the genetic algorithm as a strategy to perform the global search of the PMG. This scheme directly describes the HTS element without simplification of its…
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Taxonomy
TopicsInduction Heating and Inverter Technology · Particle accelerators and beam dynamics · Heat Transfer and Optimization
