Fast computation model for superconducting pinning Maglev mechanical system

Superconducting pinning Maglev, with its passive self-stabilization, low resistance, and noise, holds promise as the next generation of high-speed rail transit. The vehicle dynamic simulation is essential for high-speed operation, and the interaction between the high-temperature superconductor (HTS) and the permanent magnet guideway (PMG), termed the “HTS-PMG Relation,” is fundamental to understanding the system’s dynamics. This paper presents an efficient computation model for the HTS-PMG relation, employing flux penetration characteristics to reduce the dimensionality of the physical field. The model integrates Maxwell’s equations with the Bean E-J constitutive relationship, resulting in reduced-dimensional governing equations and a nonlinear boundary finite difference method (FDM) optimization algorithm. Validated through quasi-static and dynamic experiments, this model accurately simulates the complex hysteresis behaviors of pinning Maglev and significantly enhances computational efficiency compared to traditional finite element methods. The proposed fast HTS-PMG relation computation model enables large-scale dynamic simulations of superconducting Maglev trains and offers a novel approach to studying their dynamic performance and levitation drift.