English

Thin-Shell Approach for Modeling Superconducting Tapes in the $H$-$\phi$ Finite-Element Formulation

Computational Physics 2022-01-12 v3

Abstract

This paper presents a novel finite-element approach for the electromagnetic modeling of superconducting coated conductors. We combine a thin-shell (TS) method to the HH-ϕ\phi formulation to avoid the meshing difficulties related to the high aspect ratio of these conductors and reduce the computational burden in simulations. The interface boundary conditions in the TS method are defined using an auxiliary 1-D finite-element (FE) discretization of NN elements along the thinnest dimension of the conductor. This procedure permits the approximation of the superconductor's nonlinearities inside the TS in a time-transient analysis. Four application examples of increasing complexity are discussed: (i) single coated conductor, (ii) two closely packed conductors carrying anti-parallel currents, (iii) a stack of twenty superconducting tapes and a (iv) full representation of a HTS tape comprising a stack of thin films. In all these examples, the profiles of both the tangential and normal components of the magnetic field show good agreement with a reference solution obtained with standard 22-D HH-ϕ\phi formulation. Results are also compared with the widely used TT-AA formulation. This formulation is shown to be dual to the TS model with a single FE (N=1N=1) in the auxiliary 1-D systems. The increase of NN in the TS model is shown to be advantageous at small inter-tape separation and low transport current since it allows the tangential components of the magnetic field to penetrate the thin region. The reduction in computational cost without compromising accuracy makes the proposed model promising for the simulation of large-scale superconducting applications.

Keywords

Cite

@article{arxiv.2108.08828,
  title  = {Thin-Shell Approach for Modeling Superconducting Tapes in the $H$-$\phi$ Finite-Element Formulation},
  author = {Bruno de Sousa Alves and Valtteri Lahtinen and Marc Laforest and Frédéric Sirois},
  journal= {arXiv preprint arXiv:2108.08828},
  year   = {2022}
}
R2 v1 2026-06-24T05:15:46.247Z