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Experimental analysis of a mixed-plate gasketed plate heat exchanger and artificial neural net estimations of the performance as an alternative to classical correlations

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dc.contributor.author Turk, Caner
dc.contributor.author Aradağ, Selin
dc.contributor.author Kakaç, Sadık
dc.date.accessioned 2019-06-26T08:07:02Z
dc.date.available 2019-06-26T08:07:02Z
dc.date.issued 2016-11
dc.identifier.citation Turk, C., Aradag, S., & Kakac, S. (2016). Experimental analysis of a mixed-plate gasketed plate heat exchanger and artificial neural net estimations of the performance as an alternative to classical correlations. International Journal of Thermal Sciences, 109, 263-269. en_US
dc.identifier.issn 1290-0729
dc.identifier.uri https://www.sciencedirect.com/science/article/pii/S1290072916301764
dc.identifier.uri http://hdl.handle.net/20.500.11851/1501
dc.description.abstract In this study, experiments are performed to test the thermal and hydraulic performance of gasketed plate heat exchangers (GPHE). A heat exchanger composed of two different plate types is used for the experiments, for a Reynolds number range of 500-5000. The results are compared to the experimental results obtained for plate heat exchangers which are composed of plates that have the same geometry instead of mixing two different plates. Two methods are used to investigate the thermal and hydraulic characteristics based on the obtained experimental data. One of them is the classical correlation development for Nusselt number and friction factors. Artificial neural networks (ANNs) are also used to estimate the performance as an alternative to correlations. Different networks with various numbers of hidden neurons and layers are used to find the best configuration for predictions. The results show that, artificial neural networks can be an alternative to experimental correlations for predicting thermal and hydraulic characteristics of plate heat exchangers. They give better performance when compared to correlations which are very common in heat transfer applications. Especially for mixed plate configurations studied in this research, where different plate types are used as a combination in the complete heat exchanger, it is difficult to obtain a single correlation that represents all the plates in the heat exchanger. However, when ANN's are used, it is easier to predict the performance of mixed plate HEX and the predictions are more reliable when compared to correlations. (C) 2016 Elsevier Masson SAS. All rights reserved. en_US
dc.description.sponsorship This work is supported by Turkish Academy of Sciences (TUBA-GEBIP program) and Turkish Scientific and Research Council under grant 112M173.
dc.language.iso eng en_US
dc.publisher Elsevier France-Editions Scientifiques Medicales Elsevier en_US
dc.rights info:eu-repo/semantics/closedAccess
dc.subject Artificial neural network en_US
dc.subject Gasketed plate heat exchanger en_US
dc.subject Correlation en_US
dc.subject Nusselt number en_US
dc.subject Friction factor en_US
dc.subject Experiment en_US
dc.title Experimental analysis of a mixed-plate gasketed plate heat exchanger and artificial neural net estimations of the performance as an alternative to classical correlations en_US
dc.type article en_US
dc.relation.journal International Journal Of Thermal Sciences en_US
dc.contributor.department TOBB ETU, Faculty of Engineering, Department of Mechanical Engineering en_US
dc.contributor.department TOBB ETÜ, Mühendislik Fakültesi, Makine Mühendisliği Bölümü tr_TR
dc.identifier.volume 109
dc.identifier.startpage 263
dc.identifier.endpage 269
dc.relation.tubitak Turkish Scientific and Research Council [112M173] en_US
dc.contributor.orcid http://orcid.org/0000-0002-2034-0008
dc.contributor.orcid https://orcid.org/0000-0002-7839-8034
dc.identifier.wos WOS:000381530500023
dc.identifier.scopus 2-s2.0-84974705174
dc.contributor.tobbetuauthor Kakaç, Sadık
dc.contributor.tobbetuauthor Aradağ, Selin
dc.contributor.YOKid 143685
dc.contributor.YOKid 143565
dc.identifier.doi 10.1016/j.ijthermalsci.2016.06.016
dc.relation.publicationcategory Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı tr_TR


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