A Bayesian approach for the stochastic modeling error reduction of magnetic material identification of an electromagnetic device
- Author
- Ahmed Mohamed Abouelyazied Abdallh (UGent) , Guillaume Crevecoeur (UGent) and Luc Dupré (UGent)
- Organization
- Abstract
- Magnetic material properties of an electromagnetic device can be recovered by solving an inverse problem where measurements are adequately interpreted by a mathematical forward model. The accuracy of these forward models dramatically affects the accuracy of the material properties recovered by the inverse problem. The more accurate the forward model is, the more accurate recovered data are. However, the more accurate ‘fine’ models demand a high computational time and memory storage. Alternatively, less accurate ‘coarse’ models can be used with a demerit of the high expected recovery errors. This paper uses the Bayesian approximation error approach for improving the inverse problem results when coarse models are utilized. The proposed approach adapts the objective function to be minimized with the a priori misfit between fine and coarse forward model responses. In this paper, two different electromagnetic devices, namely a switched reluctance motor and an EI core inductor, are used as case studies. The proposed methodology is validated on both purely numerical and real experimental results. The results show a significant reduction in the recovery error within an acceptable computational time.
- Keywords
- magnetic material identification, inverse problem, coarse and fine models, Bayesian approximation error approach, DESIGN, modeling error
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Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-1983236
- MLA
- Mohamed Abouelyazied Abdallh, Ahmed, et al. “A Bayesian Approach for the Stochastic Modeling Error Reduction of Magnetic Material Identification of an Electromagnetic Device.” MEASUREMENT SCIENCE & TECHNOLOGY, vol. 23, no. 3, 2012, doi:10.1088/0957-0233/23/3/035601.
- APA
- Mohamed Abouelyazied Abdallh, A., Crevecoeur, G., & Dupré, L. (2012). A Bayesian approach for the stochastic modeling error reduction of magnetic material identification of an electromagnetic device. MEASUREMENT SCIENCE & TECHNOLOGY, 23(3). https://doi.org/10.1088/0957-0233/23/3/035601
- Chicago author-date
- Mohamed Abouelyazied Abdallh, Ahmed, Guillaume Crevecoeur, and Luc Dupré. 2012. “A Bayesian Approach for the Stochastic Modeling Error Reduction of Magnetic Material Identification of an Electromagnetic Device.” MEASUREMENT SCIENCE & TECHNOLOGY 23 (3). https://doi.org/10.1088/0957-0233/23/3/035601.
- Chicago author-date (all authors)
- Mohamed Abouelyazied Abdallh, Ahmed, Guillaume Crevecoeur, and Luc Dupré. 2012. “A Bayesian Approach for the Stochastic Modeling Error Reduction of Magnetic Material Identification of an Electromagnetic Device.” MEASUREMENT SCIENCE & TECHNOLOGY 23 (3). doi:10.1088/0957-0233/23/3/035601.
- Vancouver
- 1.Mohamed Abouelyazied Abdallh A, Crevecoeur G, Dupré L. A Bayesian approach for the stochastic modeling error reduction of magnetic material identification of an electromagnetic device. MEASUREMENT SCIENCE & TECHNOLOGY. 2012;23(3).
- IEEE
- [1]A. Mohamed Abouelyazied Abdallh, G. Crevecoeur, and L. Dupré, “A Bayesian approach for the stochastic modeling error reduction of magnetic material identification of an electromagnetic device,” MEASUREMENT SCIENCE & TECHNOLOGY, vol. 23, no. 3, 2012.
@article{1983236,
abstract = {{Magnetic material properties of an electromagnetic device can be recovered by solving an inverse problem where measurements are adequately interpreted by a mathematical forward model. The accuracy of these forward models dramatically affects the accuracy of the material properties recovered by the inverse problem. The more accurate the forward model is, the more accurate recovered data are. However, the more accurate ‘fine’ models demand a high computational time and memory storage. Alternatively, less accurate ‘coarse’ models can be used with a demerit of the high expected recovery errors. This paper uses the Bayesian approximation error approach for improving the inverse problem results when coarse models are utilized. The proposed approach adapts the objective function to be minimized with the a priori misfit between fine and coarse forward model responses. In this paper, two different electromagnetic devices, namely a switched reluctance motor and an EI core inductor, are used as case studies. The proposed methodology is validated on both purely numerical and real experimental results. The results show a significant reduction in the recovery error within an acceptable computational time.}},
articleno = {{035601}},
author = {{Mohamed Abouelyazied Abdallh, Ahmed and Crevecoeur, Guillaume and Dupré, Luc}},
issn = {{0957-0233}},
journal = {{MEASUREMENT SCIENCE & TECHNOLOGY}},
keywords = {{magnetic material identification,inverse problem,coarse and fine models,Bayesian approximation error approach,DESIGN,modeling error}},
language = {{eng}},
number = {{3}},
title = {{A Bayesian approach for the stochastic modeling error reduction of magnetic material identification of an electromagnetic device}},
url = {{http://doi.org/10.1088/0957-0233/23/3/035601}},
volume = {{23}},
year = {{2012}},
}
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