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dc.contributor.authorTurk, Caner
dc.contributor.authorAradağ, Selin
dc.contributor.authorKakaç, Sadık
dc.date.accessioned2019-06-26T08:07:02Z
dc.date.available2019-06-26T08:07:02Z
dc.date.issued2016-11
dc.identifier.citationTurk, 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.issn1290-0729
dc.identifier.urihttps://www.sciencedirect.com/science/article/pii/S1290072916301764
dc.identifier.urihttp://hdl.handle.net/20.500.11851/1501
dc.description.abstractIn 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.sponsorshipThis work is supported by Turkish Academy of Sciences (TUBA-GEBIP program) and Turkish Scientific and Research Council under grant 112M173.
dc.language.isoengen_US
dc.publisherElsevier France-Editions Scientifiques Medicales Elsevieren_US
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectArtificial neural networken_US
dc.subjectGasketed plate heat exchangeren_US
dc.subjectCorrelationen_US
dc.subjectNusselt numberen_US
dc.subjectFriction factoren_US
dc.subjectExperimenten_US
dc.titleExperimental analysis of a mixed-plate gasketed plate heat exchanger and artificial neural net estimations of the performance as an alternative to classical correlationsen_US
dc.typearticleen_US
dc.relation.journalInternational Journal Of Thermal Sciencesen_US
dc.contributor.departmentTOBB ETU, Faculty of Engineering, Department of Mechanical Engineeringen_US
dc.contributor.departmentTOBB ETÜ, Mühendislik Fakültesi, Makine Mühendisliği Bölümütr_TR
dc.identifier.volume109
dc.identifier.startpage263
dc.identifier.endpage269
dc.relation.tubitakTurkish Scientific and Research Council [112M173]en_US
dc.contributor.orcidhttp://orcid.org/0000-0002-2034-0008
dc.contributor.orcidhttps://orcid.org/0000-0002-7839-8034
dc.identifier.wosWOS:000381530500023
dc.identifier.scopus2-s2.0-84974705174
dc.contributor.tobbetuauthorKakaç, Sadık
dc.contributor.tobbetuauthorAradağ, Selin
dc.contributor.YOKid143685
dc.contributor.YOKid143565
dc.identifier.doi10.1016/j.ijthermalsci.2016.06.016
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıtr_TR


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