A self-consistent model for estimating the critical current of superconducting devices

TL;DR

This paper introduces a self-consistent, high-speed model for accurately estimating the critical current in superconducting devices, accounting for self-field effects and magnetic flux dependence, aiding design optimization.

Contribution

The paper presents a novel self-consistent model that accurately predicts the critical current of superconducting devices considering self-field and magnetic flux effects, with high computational efficiency.

Findings

Model accurately estimates critical current in superconducting devices.

High computational speed enables efficient design optimization.

Effectively accounts for self-field and magnetic flux dependence.

Abstract

Nowadays, there is growing interest in using superconducting wires or tapes for the design and manufacture of devices such as cables, coils, rotating machinery, transformers and fault current limiters among others. Their high current capacity has made them the candidates of choice for manufacturing compact and light cables and coils that can be used in the large scale power applications described above. However, the performance of these cables and coils is limited by their critical current, which is determined by several factors, including the conductor's material properties and the geometric layout of the device itself. In this work we present a self-consistent model for estimating the critical current of superconducting devices. This is of large importance when the operating conditions are such that the self-field produced by the current is comparable to the overall background field. The model is based on the asymptotic limit when time approaches infinity of Faraday's equation written in terms of the magnetic vector potential. It uses a continuous E-J relationship and takes the angular dependence of the critical current density on the magnetic flux density into account. The proposed model is used to estimate the critical current of superconducting devices such as cables, coils, and coils made of transposed cables with very high accuracy. The high computing speed of this model makes it an ideal candidate for design optimization.