Time harmonic eddy currents in non‐linear media

Paoli, G.; Bíró, Oszkár

doi:10.1108/03321649810220865

Cited by 6 publications

(5 citation statements)

References 10 publications

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“…There are many methods to create effective magnetization curve in literature [19], [20], but in this paper, the effective permeability is calculated as equation 9, which considers sinusoidal magnetic flux density and considers the fundamental frequency only [21], [22]. ν eff is the effectve reluctivity, H is the magnetic field, T is the time period andB is the peak magnetic flux density, respectively.…”

Section: Methodsmentioning

confidence: 99%

Experimental and theoretical study of interlaminar eddy current loss in laminated cores

Shah

Rasilo

Belahcen

et al. 2017

2017 20th International Conference on Electrical Machines and Systems (ICEMS)

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Insulation failure between the electrical sheets of electrical machines or transformers might occur due to the burrs formed during the cutting process. Together with welding seams or screws used to hold the stack together, the burrs provide a conducting path for eddy currents. In this paper, equivalent conductivities of the EI core with and without interlaminar contacts are determined using 3D finite element computations and measurements. The core loss of the EI core is measured, and the eddy current loss is segregated from the measurements. Based on the acquired eddy current loss, the equivalent conductivities are determined using an iterative approach. In the case of interlaminar fault at one limb, eddy current loss coefficient increased by 2% and in the case of interlaminar fault at two limbs, eddy current loss coefficient increased by 2.7% compared to the healthy case.

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Section: Methodsmentioning

confidence: 99%

Experimental and theoretical study of interlaminar eddy current loss in laminated cores

Shah

Rasilo

Belahcen

et al. 2017

2017 20th International Conference on Electrical Machines and Systems (ICEMS)

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“…Time stepping, harmonic balance and the time-periodic FEM all provide a good estimate of the actual waveforms when the eddy current region is nonlinear. Alternatively, it is possible to define an equivalent magnetic permeability that incorporates the impact of the material nonlinearity (El-Markaby et al, 1982;Paoli and Biro, 1998). With this approach, excellent estimates of induced power density, for example, can be obtained using a conventional time harmonic formulation.…”

Section: Numerical Methods For Eddy Current Problemsmentioning

confidence: 99%

State of the art of numerical modeling for induction processes

Lavers

2008

COMPEL - The International Journal for Computation and Mathematics in Electrical and Electronic Engineering

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Purpose -To provide a selective bibliography for researchers and graduate students who have an interest in induction processes applied to the electromagnetic processing of materials. Design/methodology/approach -The objective is to provide references that identify seminal, early work, and references that represent the current state of the art. References are listed in categories that cover the broad range of induction modeling and application issues. Findings -A brief overview of the key areas in induction processing of materials is provided, but greater emphasis and space is devoted to the references provided.Research limitations/implications -The middle years of each topic area are not covered. Practical implications -A very comprehensive coverage of material is provided to those with an interest in induction processing of materials. Originality/value -This paper fulfils an identified information/resources need.

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“…In fact, locally, in a time harmonic simulation, the value of permeability is constant throughout a period. For this reason, an equivalent B-H relation is needed [44]. The NN in the non-linear case has three outputs ( 1 (x),  2 (x),  3 (x)), relative to |H |, |B|, and 𝜙, respectively, each with a different trial function:…”

Section: Non-linear Maxwell's Equations Neglecting Magnetic Lossesmentioning

confidence: 99%

“…In fact, locally, in a time harmonic simulation, the value of permeability is constant throughout a period. For this reason, an equivalent B–H relation is needed [44]. The NN in the non‐linear case has three outputs

({scriptN}_{1} false(x false)

{scriptN}_{2} false(x false)

{scriptN}_{3} (x) false)

, relative to

false| \underset{̲}{H} false|

false| \underset{̲}{B} false|

, and ϕ, respectively, each with a different trial function:

\begin{matrix} true \\ right T_{normalf, H} = N_{1} (x) m_{H} x (1 - x) + H_{0} (1 - x) \\ right T_{normalf, B} = N_{2} (x) m_{B} (1 - x) \\ right T_{normalf, ϕ} = N_{3} (x) m_{ϕ} x + ϕ_{0} (1 - x) \end{matrix}

…”

Section: Solving Maxwell's Equations In Frequency Domainmentioning

confidence: 99%

“…In literature, several methods have been developed aiming at transforming the DC B–H function into an equivalent dependency for time harmonic simulations. Effective magnetization, RMS method, energy, enhanced co‐energy are the most well‐known approaches [44, 51, 53, 54]. A RMS method consists of assigning, at each

false| \underset{̲}{H} false|

field value, the magnetic flux density it has supposed to have at

false| \underset{̲}{H} false| / \sqrt{2}

.…”

Section: Solving Maxwell's Equations In Frequency Domainmentioning

confidence: 99%

See 1 more Smart Citation

Solving 1D non‐linear magneto quasi‐static Maxwell's equations using neural networks

Baldan

Nacke

2021

IET Science Measure & Tech

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Electromagnetics (EM) can be described, together with the constitutive laws, by four PDEs, called Maxwell's equations. "Quasi-static" approximations emerge from neglecting particular couplings of electric and magnetic field related quantities. In case of slowly time varying fields, if inductive and resistive effects have to be considered, whereas capacitive effects can be neglected, the magneto quasi-static (MQS) approximation applies. The solution of the MQS Maxwell's equations, traditionally obtained with finite differences and elements methods, is crucial in modelling EM devices. In this paper, the applicability of an unsupervised deep learning model is studied in order to solve MQS Maxwell's equations, in both frequency and time domain. In this framework, a straightforward way to model hysteretic and anhysteretic non-linearity is shown. The introduced technique is used for the field analysis in the place of the classical finite elements in two applications: on the one hand, the B-H curve inverse determination of AISI 4140, on the other, the simulation of an induction heating process. Finally, since many of the commercial FEM packages do not allow modelling hysteresis, it is shown how the present approach could be further adopted for the inverse magnetic properties identification of new magnetic flux concentrators for induction applications. Recently, deep learning emerges as a powerful technique in many applications, including computer vision, speech recognition, natural language process, and bioinformatics. There is This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Time harmonic eddy currents in non‐linear media

Cited by 6 publications

References 10 publications

Experimental and theoretical study of interlaminar eddy current loss in laminated cores

Experimental and theoretical study of interlaminar eddy current loss in laminated cores

State of the art of numerical modeling for induction processes

Solving 1D non‐linear magneto quasi‐static Maxwell's equations using neural networks

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