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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Autores principales:	Nils J. Ziegeler, Peter W. Nolte, Stefan Schweizer
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	compact thermal models thermal impedance transient thermal measurement time constant spectrum thermal structure function network identification by deconvolution Technology T
Acceso en línea:	https://doaj.org/article/0643cc73d04e4dc1bd514e6d47edf812
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id	oai:doaj.org-article:0643cc73d04e4dc1bd514e6d47edf812
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spelling	oai:doaj.org-article:0643cc73d04e4dc1bd514e6d47edf8122021-11-25T17:27:32ZOptimization-Based Network Identification for Thermal Transient Measurements10.3390/en142276481996-1073https://doaj.org/article/0643cc73d04e4dc1bd514e6d47edf8122021-11-01T00:00:00Zhttps://www.mdpi.com/1996-1073/14/22/7648https://doaj.org/toc/1996-1073Network 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.Nils J. ZiegelerPeter W. NolteStefan SchweizerMDPI AGarticlecompact thermal modelsthermal impedancetransient thermal measurementtime constant spectrumthermal structure functionnetwork identification by deconvolutionTechnologyTENEnergies, Vol 14, Iss 7648, p 7648 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	compact thermal models thermal impedance transient thermal measurement time constant spectrum thermal structure function network identification by deconvolution Technology T
spellingShingle	compact thermal models thermal impedance transient thermal measurement time constant spectrum thermal structure function network identification by deconvolution Technology T Nils J. Ziegeler Peter W. Nolte Stefan Schweizer Optimization-Based Network Identification for Thermal Transient Measurements
description	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.
format	article
author	Nils J. Ziegeler Peter W. Nolte Stefan Schweizer
author_facet	Nils J. Ziegeler Peter W. Nolte Stefan Schweizer
author_sort	Nils J. Ziegeler
title	Optimization-Based Network Identification for Thermal Transient Measurements
title_short	Optimization-Based Network Identification for Thermal Transient Measurements
title_full	Optimization-Based Network Identification for Thermal Transient Measurements
title_fullStr	Optimization-Based Network Identification for Thermal Transient Measurements
title_full_unstemmed	Optimization-Based Network Identification for Thermal Transient Measurements
title_sort	optimization-based network identification for thermal transient measurements
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/0643cc73d04e4dc1bd514e6d47edf812
work_keys_str_mv	AT nilsjziegeler optimizationbasednetworkidentificationforthermaltransientmeasurements AT peterwnolte optimizationbasednetworkidentificationforthermaltransientmeasurements AT stefanschweizer optimizationbasednetworkidentificationforthermaltransientmeasurements
_version_	1718412327431176192

Optimization-Based Network Identification for Thermal Transient Measurements

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