Phenomenological modelling of first order phase transitions in magnetic systems

Melikhov, Yevgen; Hadimani, Ravi L.; Raghunathan, Arun

doi:10.1063/1.4875678

Cited by 5 publications

(1 citation statement)

References 24 publications

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“…The Jiles-Atherton model employs functions and parameters based on physics [25,26,[33][34][35]. However, the parameters are obtained by phenomenological methods [36][37][38], such that the model is considered a phenomenological model [6,36,[39][40][41][42].…”

Section: Phenomenological or Physical Models?mentioning

confidence: 99%

Review of Hysteresis Models for Magnetic Materials

Mörée

Leijon

2023

Energies

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There are several models for magnetic hysteresis. Their key purposes are to model magnetization curves with a history dependence to achieve hysteresis cycles without a frequency dependence. There are different approaches to handling history dependence. The two main categories are Duhem-type models and Preisach-type models. Duhem models handle it via a simple directional dependence on the flux rate, without a proper memory. While the Preisach type model handles it via memory of the point where the direction of the flux rate is changed. The most common Duhem model is the phenomenological Jiles–Atherton model, with examples of other models including the Coleman–Hodgdon model and the Tellinen model. Examples of Preisach type models are the classical Preisach model and the Prandtl–Ishlinskii model, although there are also many other models with adoptions of a similar history dependence. Hysteresis is by definition rate-independent, and thereby not dependent on the speed of the alternating flux density. An additional rate dependence is still important and often included in many dynamic hysteresis models. The Chua model is common for modeling non-linear dynamic magnetization curves; however, it does not define classical hysteresis. Other similar adoptions also exist that combine hysteresis modeling with eddy current modeling, similar to how frequency dependence is included in core loss modeling. Most models are made for scalar values of alternating fields, but there are also several models with vector generalizations that also consider three-dimensional directions.

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