Automated
            <i>B</i>
            –
            <i>H</i>
            curve identification algorithm combining field simulation with optimisation methods and exploiting parallel computation

Barba, Paolo Di; Komęza, Krzysztof; Juszczak, Ewa Napieralska; Lecointe, Jean‐Philippe; Napieralski, Piotr; Hihat, N.

doi:10.1049/iet-smt.2011.0109

Cited by 8 publications

(6 citation statements)

References 22 publications

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“…Finally, as a second case study, we considered a 1D time transient analysis where the JA model is implemented. Equation 5is solved, now keeping also Equations (33) and (34) into account. A known H sinusoidal field is imposed at x = 0 with a peak value of 5 kA/m.…”

Section: Non-linear Maxwell's Equations Including Hysteresismentioning

confidence: 99%

“…In particular, heating carbon steels allows to reach efficiencies up to 90% below the Curie point. A correct material properties knowledge improves the accuracy of numerical simulations [33]. Referring to the AISI 4140 (whose carbon content is approximately 0.42%), the B-H relation (Equation (15)) is described with a = 270 and b = 0.577 [43].…”

Section: Inverse B − H Curve Identification Of Carbon Steel Aisi 4140mentioning

confidence: 99%

“…In this section, two case studies are subject of investigation. In the first example, the system of Equations (33) and (34) CONFLICT OF INTEREST Authors declare no conflict of interest. ORCID Marco Baldan https://orcid.org/0000-0002-5803-3150…”

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confidence: 99%

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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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Section: Non-linear Maxwell's Equations Including Hysteresismentioning

confidence: 99%

Section: Inverse B − H Curve Identification Of Carbon Steel Aisi 4140mentioning

confidence: 99%

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

Baldan

Nacke

2021

IET Science Measure & Tech

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“…There are two kinds of signal approximation: one is the estimation of parameters [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18], and the other is the interpolation [19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36].…”

Section: Introductionmentioning

confidence: 99%

“…In [1], a biologically plausible thalamocortical neural population model is extended. The study of [2] proposes an automated procedure for linking an identification algorithm for magnetic field analysis. In [3], they consider distributed signal estimation in sensor networks.…”

Section: Introductionmentioning

confidence: 99%

Acquisition system and approximation of brain signals

Rubio

Vázquez

Mújica-Vargas

2013

IET Science, Measurement & Technology

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In this study, one acquisition system of one channel is introduced to obtain the biological brain signals. Such system consists of a low-pass filter, an amplifier, a high-pass filter and a data acquisition board. A novel slopes algorithm with guaranteed bounded output is proposed for the approximation of the brain signals. The proposed method is compared with both the leastsquares and nearest-neighbour algorithms.

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

Laudani

Fulginei

Salvini

2014

Physica B: Condensed Matter

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Automated B – H curve identification algorithm combining field simulation with optimisation methods and exploiting parallel computation

Cited by 8 publications

References 22 publications

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

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

Acquisition system and approximation of brain signals

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

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