TY - JOUR UR - http://lib.ugent.be/catalog/pug01:1983236 ID - pug01:1983236 LA - eng TI - A Bayesian approach for the stochastic modeling error reduction of magnetic material identification of an electromagnetic device PY - 2012 JO - (2012) MEASUREMENT SCIENCE & TECHNOLOGY SN - 0957-0233 PB - 2012 AU - Mohamed Abouelyazied Abdallh, Ahmed UGent 000070800502 802000256579 AU - Crevecoeur, Guillaume TW08 001999121193 AU - Dupré, Luc TW08 801000725941 0000-0002-0350-2810 AB - 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. ER -Download RIS file
00000nam^a2200301^i^4500 | |||
001 | 1983236 | ||
005 | 20180813141257.0 | ||
008 | 120111s2012------------------------eng-- | ||
022 | a 0957-0233 | ||
024 | a 000300614800027 2 wos | ||
024 | a 1854/LU-1983236 2 handle | ||
024 | a 10.1088/0957-0233/23/3/035601 2 doi | ||
040 | a UGent | ||
245 | a A Bayesian approach for the stochastic modeling error reduction of magnetic material identification of an electromagnetic device | ||
260 | c 2012 | ||
520 | a 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. | ||
598 | a A1 | ||
700 | a Mohamed Abouelyazied Abdallh, Ahmed u UGent 0 000070800502 0 802000256579 0 972606072324 9 F97B9844-F0ED-11E1-A9DE-61C894A0A6B4 | ||
700 | a Crevecoeur, Guillaume u TW08 0 001999121193 0 801001881756 9 F7378570-F0ED-11E1-A9DE-61C894A0A6B4 | ||
700 | a Dupré, Luc u TW08 0 801000725941 0 0000-0002-0350-2810 9 F46C7A6C-F0ED-11E1-A9DE-61C894A0A6B4 | ||
650 | a Technology and Engineering | ||
653 | a magnetic material identification | ||
653 | a inverse problem | ||
653 | a coarse and fine models | ||
653 | a Bayesian approximation error approach | ||
653 | a DESIGN | ||
653 | a modeling error | ||
773 | t MEASUREMENT SCIENCE & TECHNOLOGY g Meas. Sci. Technol. 2012. 23 (3) q 23:3< | ||
856 | 3 Full Text u https://biblio.ugent.be/publication/1983236/file/1983244 z [open] y 15_Accepted_MST_Bayes.pdf | ||
920 | a article | ||
Z30 | x EA 1 TW08 | ||
922 | a UGENT-EA |
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