A NOVEL MACHINE LEARNING STUDY: MAXIMIZING THE EFFICIENCY OF PARABOLIC TROUGH SOLAR COLLECTORS WITH ENGINE OIL-BASED COPPER AND SILVER NANOFLUIDS

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dc.contributor.authorColak, Andac Batur
dc.contributor.authorBayrak, Mustafa
dc.date.accessioned2024-10-12T19:43:04Z
dc.date.available2024-10-12T19:43:04Z
dc.date.issued2024
dc.departmentİstanbul Ticaret Üniversitesien_US
dc.description.abstractEstimating the heat transfer parameters of parabolic trough solar collectors with machine learning is crucial for improving the efficiency and performance of these renewable energy systems, optimizing their design and operation, and reducing costs while increasing the use of solar energy as a sustainable power source. In this study, the heat transfer characteristics of two different nanofluids flowing through the porous media in a straight plane underneath thermal jump conditions were investigated by machine learning methods. For the flow in the parabolic trough solar collector, two different nanofluids obtained from silver- and copper-based motor oil are considered. Flow characteristics were obtained by nonlinear surface tension, thermal radiation, and Cattaneo-Christov heat flow, which was used to calculate the heat flow in the thermal boundary layer. A neural network structure was established to estimate the skin friction and Nusselt number determined for the analysis of the flow characteristic. The data used in the multilayer neural network, which was developed using a total of 30 data sets, were divided into three groups as training, validation, and testing. In the input layer of the network model with 15 neurons in the hidden layer, 10 parameters were defined and four different results were obtained for two different nanofluids in the output layer. The prediction performance of the established neural network model has been comprehensively studied by means of several performance parameters. The study findings presented that the established artificial neural network can predict the heat transfer characteristics of two different nanofluids obtained from silver- and copper-based motor oil with deviation rates less than 0.06%.en_US
dc.description.sponsorshipACKNOWLEDGMENT The author expresses gratitude to Abu-Hamdeh et al. (2021) for their research in the field of renewable energy, their scientific contribution, and for providing an open source dataseten_US
dc.identifier.issn1064-2285
dc.identifier.issn2162-6561
dc.identifier.issue16en_US
dc.identifier.scopus2-s2.0-85201582280en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.urihttps://hdl.handle.net/11467/8753
dc.identifier.volume55en_US
dc.identifier.wosWOS:001285295200003en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherBegell House Incen_US
dc.relation.ispartofHeat Transfer Researchen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmzWoS_2024en_US
dc.subjectparabolic trough solar collectorsen_US
dc.subjectnanofluidsen_US
dc.subjectengine oilen_US
dc.subjectheat transferen_US
dc.subjectmachine learningen_US
dc.titleA NOVEL MACHINE LEARNING STUDY: MAXIMIZING THE EFFICIENCY OF PARABOLIC TROUGH SOLAR COLLECTORS WITH ENGINE OIL-BASED COPPER AND SILVER NANOFLUIDSen_US
dc.typeArticleen_US

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