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Expansion of Preisach density in magnetic hysteresis using general basis functions

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  • Bhattacharjee, Arindam
  • Mohanty, Atanu K.
  • Chatterjee, Anindya

Abstract

The Preisach model of hysteresis has two parts: a geometrical staircase and a density or weighting function. In typical applications, the underlying density function of hysteresis operators is estimated through partial derivatives of first order reversal curves, or a priori assumed to obey simple functional forms like Gaussian, Lorenzian etc. Here we take a more agnostic and empirical approach, and expand the density in a general form using the spectrum of the Laplace operator on a bounded triangular domain. Transforming the input to the same bounded domain, we have a nonlinear parameter fitting problem. We fit parameters to our own magnetic hysteresis data directly using complex waveforms, for both soft and hard loops. For hard loops, if the Preisach density is to be kept strictly nonnegative (as is usual), a nonlinear transformation of the Preisach output is needed. Additionally, the Preisach density is consists of a single hump for soft loops and three distinct humps for hard loops. Our fitted density function, based on a general expansion, contains several coefficients, but subsequent simulation is quick. The main contribution of this paper is a direct demonstration of fitting the density function without making a priori assumptions about the functional form.

Suggested Citation

  • Bhattacharjee, Arindam & Mohanty, Atanu K. & Chatterjee, Anindya, 2019. "Expansion of Preisach density in magnetic hysteresis using general basis functions," Applied Mathematics and Computation, Elsevier, vol. 341(C), pages 418-427.
  • Handle: RePEc:eee:apmaco:v:341:y:2019:i:c:p:418-427
    DOI: 10.1016/j.amc.2018.09.009
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    References listed on IDEAS

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    1. Novak, Miroslav & Eichler, Jakub & Kosek, Miloslav, 2018. "Difficulty in identification of Preisach hysteresis model weighting function using first order reversal curves method in soft magnetic materials," Applied Mathematics and Computation, Elsevier, vol. 319(C), pages 469-485.
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