The classical Preisach model of hysteresis and reversibility

Mayergoyz, I.D.

doi:10.1063/1.348323

Cited by 21 publications

(13 citation statements)

References 3 publications

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“…Mayergoyz [31][32][33] proposed the current standard FORC measurement as an identification technique via the classical Preisach model [34], which describes magnetic hysteresis loops as a superposition of numerous independent relays, called hysterons. Hysterons represent the switching of single MNWs with rectangular hysteresis loops, such as those of isolated MNWs acting like Stoner-Wohlfarth particles.…”

Section: Introductionmentioning

confidence: 99%

Beyond the qualitative description of complex magnetic nanoparticle arrays using FORC measurement

Kouhpanji

Stadler

2020

Nano Ex.

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First-order reversal curve (FORC) measurements are broadly used for the characterization of complex magnetic nanostructures, but they can be inconclusive when quantifying the amount of different magnetic phases present in a sample. In this paper, we first establish a framework for extracting quantitative parameters from FORC measurements conducted on samples composed of a single type of magnetic nanostructure to interpret their magnetic properties. We then generalize our framework for the quantitative characterization of samples that are composed of 2-4 types of FeCo magnetic nanowires to determine the most reliable and reproducible parameters for a detailed analysis of samples. Finally, we conclude that the parameter with the best quantification potential, backfield remanence coercivity, does not require the full FORC measurement. Our approach provides an insightful path for fast, quantitative analysis of complex magnetic nanostructures, especially determination of the ratios of magnetic subcomponents present in multi-phase samples.

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Section: Introductionmentioning

confidence: 99%

Beyond the qualitative description of complex magnetic nanoparticle arrays using FORC measurement

Kouhpanji

Stadler

2020

Nano Ex.

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“…The Preisach hysteresis operator,P, 31,46 has proved especially robust in hysteresis modeling. Although significant work has been done to interpret the Preisach model's parameters within the context of energy landscapes 47,48 and entropy production, 41,49,50 it remains essentially phenomenological. Despite this, experimental hysteresis loops approximately demonstrate the necessary conditions of the model: (1) larger input variations erase memory of smaller input variations (wiping out property), and (2) no matter the previous history, equivalent input variations create output loops that differ at most by a constant (congruency property).…”

Section: Hysteresis and The Preisach Modelmentioning

confidence: 99%

A Preisach-Based Nonequilibrium Methodology for Simulating Performance of Hysteretic Magnetic Refrigeration Cycles

Brown

Bruno

Chen

et al. 2015

JOM

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In giant magnetocaloric effect (GMCE) materials a large entropy change couples to a magnetostructural first-order phase transition, potentially providing a basis for magnetic refrigeration cycles. However, hysteresis loss greatly reduces the availability of refrigeration work in such cycles. Here, we present a methodology combining a Preisach model for rate-independent hysteresis with a thermodynamic analysis of nonequilibrium phase transformations which, for GMCE materials exhibiting hysteresis, allows an evaluation of refrigeration work and efficiency terms for an arbitrary cycle. Using simplified but physically meaningful descriptors for the magnetic and thermal properties of a Ni 45 Co 5 Mn 36.6 In 13.4 at.% single-crystal alloy, we relate these work/efficiency terms to fundamental material properties, demonstrating the method's use as a materials design tool. Following a simple two-parameter model for the alloy's hysteresis properties, we compute and interpret the effect of each parameter on the cyclic refrigeration work and efficiency terms. We show that hysteresis loss is a critical concern in cycles based on GMCE systems, since the resultant lost work can reduce the refrigeration work to zero; however, we also find that the lost work may be mitigated by modifying other aspects of the transition, such as the width over which the one-way transformation occurs.

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“…A really substantial observation in the development of more general Preisach models was that, along the first bisector of the Preisach plane, one can add a second distribution of degenerated hysterons of zero coercivity (step functions). As the step functions are perfectly reversible, this second distribution is accounting for the reversible component [8].…”

Section: Introductionmentioning

confidence: 99%

Reversible Magnetization Processes Evaluation Using High-Order Magnetization Curves

Bodale

Stancu

2013

IEEE Trans. Magn.

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The main goal of this paper is to study the reversible magnetization processes in a ferromagnetic system. A deconvolution of total magnetization into reversible and irreversible components is obtained with a new technique based on measurements performed on a few families of minor hysteresis loops (mHL) near first-order reversal curve (FORC). This mHL/FORC technique has the potential to determine the reversible/irreversible ratio in each point of the hysteresis area. We have tested the method on experimental data measured on Barium ferrite powder samples, which exhibit a significant reversible magnetization component. The results are discussed within a modified version of scalar Preisach-type model.Index Terms-First-order reversal curve (FORC), irreversible magnetization, minor hysteresis loop, reversible magnetization.

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The classical Preisach model of hysteresis and reversibility

Cited by 21 publications

References 3 publications

Beyond the qualitative description of complex magnetic nanoparticle arrays using FORC measurement

Beyond the qualitative description of complex magnetic nanoparticle arrays using FORC measurement

A Preisach-Based Nonequilibrium Methodology for Simulating Performance of Hysteretic Magnetic Refrigeration Cycles

Reversible Magnetization Processes Evaluation Using High-Order Magnetization Curves

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