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Recherche, Développement et Innovation en Génie Electrique

Seminar in OMN TEAM

The research team OMN works on different numerical methods associated to electromagnetic field computation. We organize the Junior Seminar in LILLIAD Learning center innovation by our Ph.D. students, as well as our postdocs, and Invited Seminar by the external researchers.

If you would like to give us a talk or have some collaboration ideas about our work, please contact Zuqi who is in charge of the seminar, we can invite you to Lille.
The seminar can be held in English (or French) as you like. There is no limit for the duration of the seminar.

Upcoming seminars:

February 21, 2022, Junior Seminar

Meryeme JAMIL

January 25, 2022, Junior Seminar



December 13, 2021, Junior Seminar


Iron loss Modelling of Anisotropic Soft Magnetic Steels in FEM Simulation

This presentation will serve to introduce an recently develop anisotropic iron loss model from the INRIM laboratory in Italy. The model has been integrated into the finite element software code_Carmel in post-processing, and its implementation have been tested and validated by comparison with experimental results. This developpement are part of the work realized during my PhD thesis.


December 13, 2021, Junior Seminar


Development of metamodels for low-frequency electromagnetic devices

In recent years, the electric vehicles market is witnessing a large expansion. One of the points of interest is to reduce the electromagnetic noise generated by electric motors. Our work is a part of the project « E-Silence », consisting in reducing the electromagnetic noise in electric machines without deteriorating the performances or increasing the cost.
In order to include the noise issue in the early design phases of a machine, it is required to compute the electromagnetic force for a large set of parameters, which may generate a prohibitive time cost if using a finite element model. A solution is to create a parametric metamodel using model order reduction or interpolation approaches.
In this presentation, geometric parametric metamodels are built based on the proper orthogonal decomposition combined with the radial basis functions interpolation method (POD-RBF) and the proper generalized decomposition method (PGD). These approaches are evaluated for academic examples, then applied to the case of an electric machine.


November 18, 2021, Junior Seminar

Houssein TAHA

Mise en oeuvre du modèle de Darwin par la méthode des éléments finis en vue de modéliser les machines électriques à des fréquences intermédiaires

Dans les dernières années, la modélisation des composants magnétiques et électriques suscite beaucoup d’intérêt dans la recherche scientifique. Un modèle magnétodynamique est suffisant pour décrire le comportement des machines électriques dans les basses fréquences, mais, avec l’apparition de l’électronique de puissance, les machines sont soumises à des tensions hautes fréquences, cela nécessite une modélisation des isolants surtout en raison du vieillissement auquel ils seront exposés.
L’objectif de ces travaux est de calculer le champ électrique dans les milieux non conducteurs, en particulier, les effets capacitifs. En effet, vu que les modèles classiques tels que la magnétostatique et la magnéto-quasistatique ne prennent pas en compte la modélisation de ces effets, il est indispensable de mettre en œuvre des formulations en potentiels adaptées dans le code_Carmel pour calculer simultanément les champs électriques et magnétiques, telle que le modèle de Darwin. Ce modèle est capable de capturer les effets capacitifs-inductifs couplés à des fréquences intérmédiaires, en particulier, autour de la fréquence de résonnance. En revenche, l’électrostatique et l’électro-quasistatique sont parmi les modèles connus qui sont capables à modéliser les effets capacitifs.
Différentes applications industrielles ont été présentées afin de valider les résultats de simulation obtenus par le modèle de Darwin en les comparant aux résultats de mesures.


October 01, 2021, Junior Seminar

Ruohan GONG

Investigation about Deep learning application in the field of computational electromagnetics

In recent years, deep learning (DL) has been developed rapidly and has conquered many fields and achieved state-of-the-art performance, such as visual recognition, natural language processing, etc. Particularly, convolutional neural network (CNN) has gained tremendous popularity and has been widely used because of its capacity to automatically execute feature engineering on its own.
In this presentation, the possibility of applying CNN in the field of computational electromagnetics will be investigated, which can be used as an efficient tool with only a small database. Some typical applications will be presented: the magneto-thermal coupled analysis for transformer, the anisotropy magnetostatics analysis, and the prediction of the hysteresis loops of ferromagnetic materials. The feasibility of the proposed approach are analyzed and discussed in term of the influence of various network hyperparameters. The presented work can provide guides for other DL scenarios in electrical engineering.

Past seminars:


2019 2018 2017