Research on Ferromagnetic Components J-A Model - A Review

Duan, Jiandong; Lei, Yang; Li, Hao

doi:10.1109/powercon.2018.8601942

Cited by 7 publications

(12 citation statements)

References 29 publications

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“…In a magnetic component, the core is the key to determine its magnetic properties and performance [ 72 ]. So to achieve an optimal magnetic component’s performance, the core losses effects must be characterized [ 73 ].…”

Section: General Core Losses Modelsmentioning

confidence: 99%

“…Generally, the core loss is provided at a specific frequency and a maximum flux density [ 78 ]. The variation frequency effect in ferromagnetic materials is related to Eddy currents and the wall-domain displacement [ 72 ].…”

Section: General Core Losses Modelsmentioning

confidence: 99%

“…The hysteresis loops give a lot of information about the magnetic properties [ 72 ]. An accurate way to calculate core loss is by measuring the full hysteresis curve [ 85 ].…”

Section: General Core Losses Modelsmentioning

confidence: 99%

“…The Preisach is a scalar-static model that considers several quantities of basic’s domain-walls [ 72 ]. It is an accurate-phenomenological model, such that it could describe any system that shows a hysterical behavior [ 87 , 90 ].…”

Section: General Core Losses Modelsmentioning

confidence: 99%

“…A drawback of the Preisach model is the several measurement data to adapt it to the B-H environment’s changes [ 72 ]. Nonetheless, it can track complex magnetization processes and minor loops [ 78 ].…”

Section: General Core Losses Modelsmentioning

confidence: 99%

See 4 more Smart Citations

Power Losses Models for Magnetic Cores: A Review

et al. 2022

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In power electronics, magnetic components are fundamental, and, unfortunately, represent one of the greatest challenges for designers because they are some of the components that lead the opposition to miniaturization and the main source of losses (both electrical and thermal). The use of ferromagnetic materials as substitutes for ferrite, in the core of magnetic components, has been proposed as a solution to this problem, and with them, a new perspective and methodology in the calculation of power losses open the way to new design proposals and challenges to overcome. Achieving a core losses model that combines all the parameters (electric, magnetic, thermal) needed in power electronic applications is a challenge. The main objective of this work is to position the reader in state-of-the-art for core losses models. This last provides, in one source, tools and techniques to develop magnetic solutions towards miniaturization applications. Details about new proposals, materials used, design steps, software tools, and miniaturization examples are provided.

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Section: General Core Losses Modelsmentioning

confidence: 99%

Section: General Core Losses Modelsmentioning

confidence: 99%

“…The hysteresis loops give a lot of information about the magnetic properties [ 72 ]. An accurate way to calculate core loss is by measuring the full hysteresis curve [ 85 ].…”

Section: General Core Losses Modelsmentioning

confidence: 99%

Section: General Core Losses Modelsmentioning

confidence: 99%

Section: General Core Losses Modelsmentioning

confidence: 99%

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Power Losses Models for Magnetic Cores: A Review

et al. 2022

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Piezo-actuated smart mechatronic systems: Nonlinear modeling, identification, and control

Yuan,

Zhou,

Zhang

et al. 2024

Mechanical Systems and Signal Processing

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A stress defect state measurement method based on low-frequency ACMFL excitation and Hall sensor array collection

Zhang

Jian

et al. 2023

Meas. Sci. Technol.

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The safety testing of ferromagnetic materials, which are the main materials for various machines and equipment, is particularly important. Stress concentration zone (stress defects) cause stress corrosion of ferromagnetic materials, and it also has the possible to cause direct damage to ferromagnetic materials. Estimation of stress sources state using electromagnetic nondestructive measurement method s is a critical and difficult problem. In this paper, a visual and intelligent identification method of stress defects in ferromagnetic materials by low frequency AC magnetic flux leakage (ACMFL) technique is proposed. A new three-point compression experiment was designed in this paper. Time-difference vision is established to analyze the ACMFL signal caused by stress defects. A visual transformed convolutional neural network (vt-CNN) deep learning algorithm has been proposed to identify grayscale patterns pre-processed by the time-difference vision. The results show that the method proposed in this paper elucidates the relationship between the time-difference vision of a stress defect and the stress source state of the mechanical stress. Our proposed method allows to analyze the pressure indenter size of the pressure source of stress defects.

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Research on Ferromagnetic Components J-A Model - A Review

Cited by 7 publications

References 29 publications

Power Losses Models for Magnetic Cores: A Review

Power Losses Models for Magnetic Cores: A Review

Piezo-actuated smart mechatronic systems: Nonlinear modeling, identification, and control

A stress defect state measurement method based on low-frequency ACMFL excitation and Hall sensor array collection

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