Optimization-Based Network Identification for Thermal Transient Measurements
Network identification by deconvolution is a proven method for determining the thermal structure function of a given device. The method allows to derive the thermal capacitances as well as the resistances of a one-dimensional thermal path from the thermal step response of the device. However, the re...
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Published in | Energies (Basel) Vol. 14; no. 22; p. 7648 |
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Main Authors | , , |
Format | Journal Article |
Language | English |
Published |
Basel
MDPI AG
01.11.2021
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Abstract | Network identification by deconvolution is a proven method for determining the thermal structure function of a given device. The method allows to derive the thermal capacitances as well as the resistances of a one-dimensional thermal path from the thermal step response of the device. However, the results of this method are significantly affected by noise in the measured data, which is unavoidable to a certain extent. In this paper, a post-processing procedure for network identification from thermal transient measurements is presented. This so-called optimization-based network identification provides a much more accurate and robust result compared to approaches using Fourier or Bayesian deconvolution in combination with Foster-to-Cauer transformation. The thermal structure function obtained from network identification by deconvolution is improved by repeatedly solving the inverse problem in a multi-dimensional optimization process. The result is a non-diverging thermal structure function, which agrees well with the measured thermal impedance. In addition, the associated time constant spectrum can be calculated very accurately. This work shows the potential of inverse optimization approaches for network identification. |
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AbstractList | Network identification by deconvolution is a proven method for determining the thermal structure function of a given device. The method allows to derive the thermal capacitances as well as the resistances of a one-dimensional thermal path from the thermal step response of the device. However, the results of this method are significantly affected by noise in the measured data, which is unavoidable to a certain extent. In this paper, a post-processing procedure for network identification from thermal transient measurements is presented. This so-called optimization-based network identification provides a much more accurate and robust result compared to approaches using Fourier or Bayesian deconvolution in combination with Foster-to-Cauer transformation. The thermal structure function obtained from network identification by deconvolution is improved by repeatedly solving the inverse problem in a multi-dimensional optimization process. The result is a non-diverging thermal structure function, which agrees well with the measured thermal impedance. In addition, the associated time constant spectrum can be calculated very accurately. This work shows the potential of inverse optimization approaches for network identification. |
Author | Nolte, Peter W. Ziegeler, Nils J. Schweizer, Stefan |
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Cites_doi | 10.1109/THERMINIC49743.2020.9420505 10.1109/THERMINIC.2008.4669872 10.1093/comjnl/7.2.155 10.1109/THERMINIC52472.2021.9626491 10.1109/STHERM.2008.4509389 10.1109/6144.868862 10.3390/en14217068 10.1016/0038-1101(88)90099-8 10.1109/THERMINIC49743.2020.9420508 10.1109/THERMINIC.2019.8923671 10.1007/978-94-015-8330-5_4 10.1063/1.3176463 10.1109/THERMINIC52472.2021.9626508 10.1109/TCT.1967.1082650 10.1016/j.mejo.2011.08.010 10.1109/THERMINIC.2019.8923646 10.1109/TCT.1967.1082651 |
ContentType | Journal Article |
Copyright | 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
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References | Protonotarios (ref_15) 1967; 14 Protonotarios (ref_16) 1967; 14 ref_14 ref_13 (ref_1) 1988; 31 Ezzahri (ref_11) 2009; 80 ref_12 Vladimir (ref_17) 2012; 43 ref_10 ref_21 ref_3 ref_2 ref_18 Rencz (ref_19) 2000; 23 ref_9 ref_8 ref_5 ref_4 Powell (ref_20) 1964; 7 ref_7 ref_6 |
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SubjectTerms | Accuracy Bayesian analysis compact thermal models Deconvolution Identification Inverse problems network identification by deconvolution Noise Optimization Step response Structure-function relationships thermal impedance thermal structure function Thermal transformations Thermal transients Time constant time constant spectrum transient thermal measurement |
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