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A Bayesian approach for the stochastic modeling error reduction of magnetic material identification of an electromagnetic device

Ahmed Mohamed Abouelyazied Abdallh, Guillaume Crevecoeur UGent and Luc Dupré UGent (2012) MEASUREMENT SCIENCE & TECHNOLOGY. 23(3).
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.
Please use this url to cite or link to this publication:
author
organization
year
type
journalArticle (original)
publication status
published
subject
keyword
magnetic material identification, inverse problem, coarse and fine models, Bayesian approximation error approach, DESIGN, modeling error
journal title
MEASUREMENT SCIENCE & TECHNOLOGY
Meas. Sci. Technol.
volume
23
issue
3
article number
035601
Web of Science type
Article
Web of Science id
000300614800027
JCR category
ENGINEERING, MULTIDISCIPLINARY
JCR impact factor
1.435 (2012)
JCR rank
21/88 (2012)
JCR quartile
1 (2012)
ISSN
0957-0233
DOI
10.1088/0957-0233/23/3/035601
language
English
UGent publication?
yes
classification
A1
copyright statement
I have transferred the copyright for this publication to the publisher
id
1983236
handle
http://hdl.handle.net/1854/LU-1983236
date created
2012-01-11 11:52:53
date last changed
2016-12-21 15:42:28
@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 {\textquoteleft}fine{\textquoteright} models demand a high computational time and memory storage. Alternatively, less accurate {\textquoteleft}coarse{\textquoteright} 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 Dupr{\'e}, Luc},
  issn         = {0957-0233},
  journal      = {MEASUREMENT SCIENCE \& TECHNOLOGY},
  keyword      = {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://dx.doi.org/10.1088/0957-0233/23/3/035601},
  volume       = {23},
  year         = {2012},
}

Chicago
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).
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).
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).
MLA
Mohamed Abouelyazied Abdallh, Ahmed, Guillaume Crevecoeur, and Luc Dupré. “A Bayesian Approach for the Stochastic Modeling Error Reduction of Magnetic Material Identification of an Electromagnetic Device.” MEASUREMENT SCIENCE & TECHNOLOGY 23.3 (2012): n. pag. Print.