The aim of the present paper is to validate the Bouc-Wen (BW) hysteresis model when it is applied to predict dynamic ferromagnetic loops. Indeed, although the Bouc-Wen model has had an increasing interest in the last few years, it is usually adopted in mechanical and structural systems and very rarely for magnetic applications. Thus, for addressing this goal the Bouc-Wen model is compared with the dynamic Jiles-Atherton model that, instead, was ideated exactly for simulating magnetic hysteresis. The comparative analysis has involved saturated and symmetric hysteresis loops in ferromagnetic materials. In addition in order to identify Bouc-Wen parameters a very effective recent heuristic, called MeTEO (Metric-Topological and Evolutionary Optimization has been utilized. It is based on a hybridization of three meta-heuristics: the Flock-of-Starlings Optimization, the Particle Swarm Optimization and the Bacterial Chemotaxis Algorithm. Thanks to the specific properties of these heuristic, MeTEO allow us to achieve effective identification of such kind of models. Several hysteresis loops have been utilized for final validation tests with the aim to investigate if BW model can follow the different hysteresis behaviors of both static (quasi-static) and dynamic cases.

Comparative analysis of Bouc-Wen and Jiles-Atherton models under symmetric excitations

LAUDANI, ANTONINO;
2014-01-01

Abstract

The aim of the present paper is to validate the Bouc-Wen (BW) hysteresis model when it is applied to predict dynamic ferromagnetic loops. Indeed, although the Bouc-Wen model has had an increasing interest in the last few years, it is usually adopted in mechanical and structural systems and very rarely for magnetic applications. Thus, for addressing this goal the Bouc-Wen model is compared with the dynamic Jiles-Atherton model that, instead, was ideated exactly for simulating magnetic hysteresis. The comparative analysis has involved saturated and symmetric hysteresis loops in ferromagnetic materials. In addition in order to identify Bouc-Wen parameters a very effective recent heuristic, called MeTEO (Metric-Topological and Evolutionary Optimization has been utilized. It is based on a hybridization of three meta-heuristics: the Flock-of-Starlings Optimization, the Particle Swarm Optimization and the Bacterial Chemotaxis Algorithm. Thanks to the specific properties of these heuristic, MeTEO allow us to achieve effective identification of such kind of models. Several hysteresis loops have been utilized for final validation tests with the aim to investigate if BW model can follow the different hysteresis behaviors of both static (quasi-static) and dynamic cases.
2014
hysteresis model
evolutionary computation
model identification
optimization
swarm intelligence
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11769/575465
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