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.accessioned2024-11-07T13:25:29Z
dc.date.available2024-11-07T13:25:29Z
dc.date.issued2024
dc.departmentNiğde Ömer Halisdemir Üniversitesi
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%.
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 dataset
dc.identifier.issn1064-2285
dc.identifier.issn2162-6561
dc.identifier.issue16
dc.identifier.scopus2-s2.0-85201582280
dc.identifier.scopusqualityQ3
dc.identifier.urihttps://hdl.handle.net/11480/14739
dc.identifier.volume55
dc.identifier.wosWOS:001285295200003
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherBegell House Inc
dc.relation.ispartofHeat Transfer Research
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241106
dc.subjectparabolic trough solar collectors
dc.subjectnanofluids
dc.subjectengine oil
dc.subjectheat transfer
dc.subjectmachine learning
dc.titleA NOVEL MACHINE LEARNING STUDY: MAXIMIZING THE EFFICIENCY OF PARABOLIC TROUGH SOLAR COLLECTORS WITH ENGINE OIL-BASED COPPER AND SILVER NANOFLUIDS
dc.typeArticle

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