🤖 AI Summary
This paper addresses the high degrees of freedom, numerical ill-conditioning, and low computational efficiency arising from geometric complexity in 3D electromagnetic modeling of foil windings. We propose a globally homogenized modeling method that rigorously preserves the magnetic field intensity distribution. Innovatively, we introduce a magnetic scalar potential formulation for non-conductive regions, integrating field-conformity preservation with dimensionality reduction. The resulting model strictly maintains magnetic field intensity consistency while drastically reducing problem size. The framework supports both frequency-domain and transient finite-element solvers and enables multiscale coupled simulation of high-temperature superconducting coils. Benchmark evaluations on 2D axisymmetric and 3D configurations demonstrate accuracy comparable to conventional magnetic flux density–preserving models and full-resolution winding models. In transient simulations, the proposed method reduces degrees of freedom by over an order of magnitude, significantly improving computational efficiency while delivering robust and reliable results.
📝 Abstract
We extend the foil winding homogenization method to magnetic field conforming formulations. We first propose a full magnetic field foil winding formulation by analogy with magnetic flux density conforming formulations. We then introduce the magnetic scalar potential in non-conducting regions to improve the efficiency of the model. This leads to a significant reduction in the number of degrees of freedom, particularly in 3-D applications. The proposed models are verified on two frequency-domain benchmark problems: a 2-D axisymmetric problem and a 3-D problem. They reproduce results obtained with magnetic flux density conforming formulations and with resolved conductor models that explicitly discretize all turns. Moreover, the models are applied in the transient simulation of a high-temperature superconducting coil. In all investigated configurations, the proposed models provide reliable results while considerably reducing the size of the numerical problem to be solved.