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Vector hysteresis model identification for iron–silicon thin films from micromagnetic simulations
- Source :
- Physica B: Condensed Matter. 486:97-100
- Publication Year :
- 2016
- Publisher :
- Elsevier BV, 2016.
-
Abstract
- In this paper a phenomenological approach, based on a generalization in two dimensions of the classical scalar Preisach model, is exploited and identified to reproduce the magnetization curves obtained by accurate micromagnetic simulations of both isotropic and anisotropic polycrystalline Fe–Si films with different values of the anisotropy constants. The identification problem is realized using a suitable set of analytical equations and performing a best fit procedure to the data obtained from micromagnetic simulations of both scalar and rotational loops. The correct reconstruction of all the magnetization processes, as well as of the associated magnetic losses, is achieved through the choice of a small number of either circular or elliptical hysterons, as well as by the implementation of a simple “moving technique” that is necessary to take into account the non-collinearity between the field and the magnetization that occurs in presence of a global uniaxial anisotropy.
- Subjects :
- Preisach model of hysteresis
Field (physics)
020209 energy
02 engineering and technology
01 natural sciences
Magnetization
0103 physical sciences
Electronic
0202 electrical engineering, electronic engineering, information engineering
Optical and Magnetic Materials
Electrical and Electronic Engineering
Anisotropy
Magnetic anisotropy
010302 applied physics
Physics
Micromagnetic modeling
Condensed matter physics
Isotropy
Scalar (physics)
Preisach vector modeling
Vector magnetic hysteresis
Condensed Matter Physics
Electronic, Optical and Magnetic Materials
Hysteresis
Subjects
Details
- ISSN :
- 09214526
- Volume :
- 486
- Database :
- OpenAIRE
- Journal :
- Physica B: Condensed Matter
- Accession number :
- edsair.doi.dedup.....85939b44bec4889dd34a14ade2cbaf18
- Full Text :
- https://doi.org/10.1016/j.physb.2015.09.028