Comparison between different models of magnetic hysteresis in the solution of the TEAM 32 problem

Cardelli, Ermanno; Faba, A.; Laudani, Antonino; Antonio, S. Quondam; Ghanim, AbdelRahman M.

doi:10.1002/jnm.3103

Cited by 3 publications

(5 citation statements)

References 40 publications

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“…To measure magnetic inductions, a large number of collection coils with five turns are positioned at various locations within the magnetic core. The numerical calculations were conducted using a FEM code implemented in MATLAB by the authors (Silvester and Ferrari, 1996;Cardelli et al, 2023). To demonstrate the importance of modeling as precisely as possible, we will compare the results obtained by the same FEM simulator using DJAM for the magnetic material.…”

Section: Finite Element Analysismentioning

confidence: 99%

“…Additionally, the prediction of a hysteresis loop requires large computational and memory resources. Due to these concerns, the implementation of those numerical models of hysteresis is difficult to realize and computationally expensive in reality, especially in FEM-based numerical computations with dense meshes, as in the case of many practical applications (Antonio et al , 2020; Cardelli et al , 2023).…”

Section: Introductionmentioning

confidence: 99%

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Limitations of Jiles–Atherton models to study the effect of hysteresis in electrical steels under different excitation regimes

Atyia,

Ghanim

2023

COMPEL

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Purpose The accurate modeling of magnetic hysteresis in electrical steels is important in several electrical and electronic applications. Numerical models have long been known that can correctly reproduce some typical behaviours of these magnetic materials. Among these, the model proposed by Jiles and Atherton must certainly be mentioned. This model is intuitive and fairly easy to implement and identify with relatively few experimental data. Also, for this reason, it has been extensively studied in different formulations. The developments and numerical tests made on this hysteresis model have indicated that it is able to accurately reproduce symmetrical cycles, especially the major loop, but often it fails to reproduce non-symmetrical cycles. This paper aims to show the positive aspects and highlight the defects of the different formulations in predicting the minor loops of electrical steels excited by non-sinusoidal currents. Design/methodology/approach The different formulations are applied to different electrical steels, and the data coming from the simulations are compared with those measured experimentally. The direct and inverse Jiles–Atherton models, including the introduction of the dissipative factor approach, are presented, and their limitations are proposed and validated using the measurements of three non-grain-oriented materials. Only the measured major loop is used to identify the parameters of the Jiles–Atherton model. Furthermore, the direct and inverse Jiles–Atherton models were used to simulate the minor loops as well as the hysteresis cycles with direct component (DC) bias excitation. Finally, the simulation results are discussed and compared to measurements for each study case. Findings The paper indicates that both the direct and the inverse Jiles–Atherton model formulations provide a good agreement with the experimental data for the major loop representation; nevertheless, both models can not accurately predict the minor loops even when the modification approaches proposed in the literature were implemented. Originality/value The Jiles–Atherton model and its modifications are widely discussed in the literature; however, some limitations of the model and its modification in the case of the distorted current waveform are not completely highlighted. Furthermore, this paper contains an original discussion on the accuracy of the prediction of minor loops from distorted current waveforms, including DC bias.

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Section: Finite Element Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Limitations of Jiles–Atherton models to study the effect of hysteresis in electrical steels under different excitation regimes

Atyia,

Ghanim

2023

COMPEL

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“…In order to solve the partial differential equation (PDE), the formulation of the Galerkin for finite elements is used for solving the equation numerically by discretization of the function into a combination of basis functions 28 , 29 .…”

Section: Theoretical Studymentioning

confidence: 99%

All silicon MIR super absorber using fractal metasurfaces

Ali,

Ghanim,

Othman

et al. 2023

Sci Rep

Self Cite

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Perfect absorbers can be used in photodetectors, thermal imaging, microbolometers, and thermal photovoltaic solar energy conversions. The spectrum of Mid-infrared (MIR) wavelengths offers numerous advantages across a wide range of applications. In this work, we propose a fractal MIR broadband absorber which is composed of three layers: metal, dielectric, and metal (MDM), with the metal being considered as n-type doped silicon (D-Si) and the dielectric is silicon carbide (SiC). The architectural design was derived from the Sierpinski carpet fractal, and different building blocks were simulated to attain optimal absorption. The 3D finite element method (FEM) approach using COMSOL Multiphysics software is used to obtain numerical results. The suggested fractal absorber exhibits high absorption enhancement for MIR in the range between 3 and 9 µm. D-Si exhibits superior performance compared to metals in energy harvesting applications that utilize plasmonics at the mid-infrared range. Typically, semiconductors exhibit rougher surfaces than noble metals, resulting in lower scattering losses. Moreover, silicon presents various advantages, including compatibility with complementary metal–oxide–semiconductor (CMOS) and simple manufacturing through conventional silicon fabrication methods. In addition, the utilization of doped silicon material in the mid-IR region facilitates the development of microscale integrated plasmonic devices.

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“…Laminated cores are assumed to be made of a reversible (no hysteresis) and non‐conducting (no eddy current) material, and magnetic losses are only evaluated a posteriori, by means of Steinmetz–Bertotti like empirical formulas 5,6 . However, in a context where industry is struggling to minutely assess the impacts of magnetic losses on their devices, the a posteriori computation of losses is more and more regarded as oversimplified and unsatisfactory 7–9 …”

Section: Introductionmentioning

confidence: 99%

“…5,6 However, in a context where industry is struggling to minutely assess the impacts of magnetic losses on their devices, the a posteriori computation of losses is more and more regarded as oversimplified and unsatisfactory. [7][8][9] This article proposes an efficient solution to this problem, building on the pragmatic two-step homogenization technique for ferromagnetic laminated cores previously proposed by some of the authors. 10 Instead of a direct multiscale coupling (like HMM or FE2 would do), the proposed approach makes use of an intermediary surrogate model to stand for the detailed mesoscale lamination model in the macroscale simulation.…”

mentioning

confidence: 99%

Neural network‐based simulation of fields and losses in electrical machines with ferromagnetic laminated cores

Purnode,

Henrotte,

Louppe

et al. 2024

Int J Numerical Modelling

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Due to the distribution of eddy currents inside ferromagnetic laminations, the accurate modeling of magnetic fields and losses in the laminated cores of electrical machines requires resolving individual laminations with a fine 3D discretization. This yields finite element models so huge and costly that they are unusable in daily industrial R&D. In consequence, hysteresis and eddy currents in laminations are often simply disregarded in the modeling: the laminated core is assumed to be made of a reversible (non lossy) saturable material, and magnetic losses are evaluated a posteriori, by means of Steinmetz–Bertotti like empirical formulas. However, in a context where industry is struggling to minutely assess the impact of magnetic losses on their devices, this simplified approach is more and more regarded as inaccurate and unsatisfactory. This article proposes a solution to this issue, based on homogenization and on detailed mesoscopic simulations of eddy currents and hysteresis inside the laminations. The proposed approach results in a close‐to‐conventional 2D magnetic vector potential finite element model, but equipped with an irreversible parametric material law to represent the ferromagnetic stack. In each finite element, the parameters of the law are obtained from a neural network trained to best fit the detailed mesoscopic simulations of the laminations subjected to the same local magnetic field. This way, all aspects of the irreversible ferromagnetic response are appropriately accounted for in the finite element simulation, but at a computational cost drastically reduced with regard to a brute force 3D calculation, and comparable to that of conventional 2D finite element simulations.

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Comparison between different models of magnetic hysteresis in the solution of the TEAM 32 problem

Cited by 3 publications

References 40 publications

Limitations of Jiles–Atherton models to study the effect of hysteresis in electrical steels under different excitation regimes

Limitations of Jiles–Atherton models to study the effect of hysteresis in electrical steels under different excitation regimes

All silicon MIR super absorber using fractal metasurfaces

Neural network‐based simulation of fields and losses in electrical machines with ferromagnetic laminated cores

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