Abstract
Even though Isaak Mayergoyz described it as: “much more accurate for the description of superconducting hysteresis than for the description of hysteresis of magnetic materials”, Preisach modeling of superconducting hysteresis is not a popular investigative tool. This might be due to the complexity of identifying the Preisach distribution function or due to lack of convincing physical reasoning behind pure phenomenological versions. In this paper, a two-component Preisach-type model is presented which is computationally-efficient and physically-sound. The change in the slope of the minor hysteresis loops is incorporated in the model and is attributed to reversible fluxoid motion. The model presented is clearly capable of simulating various shapes of superconducting hysteresis loops and could be easily coupled with finite element method (FEM) numerical software.
Highlights
The continuous advancement of high-temperature superconductors (HTS) technology has empowered numerous military applications
The change in the slope of the minor hysteresis loops is incorporated in the model and is attributed to reversible fluxoid motion
The model presented is clearly capable of simulating various shapes of superconducting hysteresis loops and could be coupled with finite element method (FEM) numerical software
Summary
The continuous advancement of high-temperature superconductors (HTS) technology has empowered numerous military applications. The proposed higher-level model provides a computationally-efficient tool capable of predicting the hysteretic magnetization of HTS, an increasingly-used technology on naval vessels. The Preisach model is the most common and probably the most important hysteresis model in the literature It is not a physical one, it has been used extensively as a fundamental tool for modeling and interpreting several complex magnetizing processes in magnetic materials. Preisach modeling of superconducting hysteresis has not proven popular This might be because it provides little-to-no physical insight into the processes of interest or because it requires extensive experimental data. A different phenomenological Preisach-type approach has been recently published.[4] The new model uses “two separate modified Preisach algorithms” and requires the limiting (major) hysteresis loop as training experimental data.
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