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Guangtong Ma

Researcher at Southwest Jiaotong University

Publications -  173
Citations -  1682

Guangtong Ma is an academic researcher from Southwest Jiaotong University. The author has contributed to research in topics: Levitation & Magnet. The author has an hindex of 17, co-authored 151 publications receiving 1205 citations. Previous affiliations of Guangtong Ma include Chengdu University of Technology.

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Proceedings ArticleDOI

3-D dynamic simulation of an integrated levitation, guidance and propulsion system by a superconducting transverse flux linear motor

TL;DR: In this article, a transverse flux linear motor (TFLM) with the superconductor-aluminum hybrid as the secondary and E-shaped ferromagnetic yokes wrapped the conductor windings as the primary is presented.
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Structure Optimization of a Superconducting Linear Generator With YBCO Tape Windings

TL;DR: In this article, a double-sided superconducting linear generator with YBCO tape windings was proposed, and a parameter-scanning method was used to search for optimal dimension parameters with the purpose of minimizing the non-sinusoidal components of the induced voltage curve.
Journal ArticleDOI

Performance enhancement of coated conductor magnet with double-layer metal insulation

TL;DR: In this article , a double-layer metal-insulation method using brass sheets as the double layer insulators is proposed, which can enhance the contact resistivity while preserving greater thermal conductivity merit.
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A High-Temperature Superconducting Magnetic Transmission Device

TL;DR: In this paper, a high-temperature superconducting (HTS) magnetic transmission device is introduced, which consists of a magnetic bolt and an HTS nut, and its strong points are being frictionless and having high efficiency.
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Design Optimization of Null-Flux Superconducting Electrodynamic Suspension for Cost-Saving and Performance Improvement

TL;DR: In this article , the multiobjective optimization of a null-flux superconducting electrodynamic suspension (EDS) system was investigated to improve the performance using the non-nominated sorting genetic algorithm-II (NSGA-II) method.