Magnetization Current Simulation of High Temperature Bulk Superconductors Using A-V-A Formulation and Iterative Algorithm Method: Critical State Model and Flux Creep Model

TL;DR

This paper introduces an A-V-A formulation based iterative algorithm for simulating magnetization currents in high-temperature superconductors, improving efficiency and flexibility over existing methods.

Contribution

The paper presents a novel A-V-A formulation iterative algorithm embedded in ANSYS for efficient simulation of superconductor magnetization, including critical state and flux creep models.

Findings

Simulation results agree with existing H-formulation models.

Computation time is significantly reduced.

The method is adaptable for complex material and field conditions.

Abstract

In this work we will introduce the A-V-A formulation based iterative algorithm method (IAM) for simulating the magnetization current of high temperature superconductors. This new method embedded in ANSYS can simulate the critical state model by forcing the trapped current density to the critical current density Jc for all meshed superconducting elements after each iterative load step, as well as simulate the flux creep model by updating the E-J power law based resistivity values. The simulation results of a disk-shaped ReBCO bulk during zero field cooling (ZFC) or field cooling (FC) magnetization agree well with the simulation results from using the H-formulation in COMSOL. The computation time is shortened by using the A-V formulation in superconductor areas and the A-formulation in non-superconductor areas. This iterative method is further proved friendly for adding ferromagnetic materials into the FEA model or taking into account the magnetic field-dependent or mechanical strain-related critical current density of the superconductors. The influence factors for the magnetization simulation, including the specified iterative load steps, the initial resistivity, the ramping time and the updating coefficient, are discussed in detail. The A-V-A formulation based IAM, implemented in ANSYS, shows its unique advantages in adjustable computation time, multi-frame restart analysis and easy-convergence.