Investigation and prediction of ethylene Glycol based ZnO nanofluidic heat transfer versus magnetic effect by deep learning

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dc.authoridBAYKARA, Muhammet/0000-0001-5223-1343
dc.authorwosidBAYKARA, Muhammet/V-8965-2018
dc.contributor.authorDemirpolat, Ahmet Beyzade
dc.contributor.authorBaykara, Muhammet
dc.date.accessioned2024-08-04T20:59:40Z
dc.date.available2024-08-04T20:59:40Z
dc.date.issued2021
dc.departmentİnönü Üniversitesien_US
dc.description.abstractIn this study, ZnO (zinc oxide) nanoparticle production was performed. Heat transfer coefficients (h) were measured for Ethylene Glycol Based ZnO nanofluids that were produced using pure water, ethanol, and ethylene glycol materials. In the literature, this is the first study in which Nanofluid was produced and experimental results were estimated by using LSTM and CNN-LSTM deep learning models. The study graphs' show the relationship between heat transfer coefficients. Besides, Reynolds numbers were drawn and predictive models were created by using the LSTM and CNN-LSTM deep learning models for h values of nanofluids. In addition, the deep learning architecture that predicts the effects of the magnetic effect on the heat transfer coefficient has been introduced to the literature as an innovation. The results showed that the heat transfer coefficients can be estimated with the LSTM and CNN-LSTM deep learning model with an average error of 0.7342% and 0.2001% respectively. In addition, the relative error of the heat transfer coefficients as a result of the magnetic effect was determined as 0.02944 and 0.01701, respectively, with the same methods and model. Applying the magnetic effect to the system, an irregularity was observed in the flow and as a result of increased heat transfer, the friction on the pipe wall increased. The importance of the study is modeling the heat transfer coefficient values depending on the different pH values that were used during the synthesis of ZnO nanomaterial and observing the effects of the magnetic effect on the system.en_US
dc.identifier.doi10.1016/j.tsep.2021.101034
dc.identifier.issn2451-9049
dc.identifier.urihttps://doi.org/10.1016/j.tsep.2021.101034
dc.identifier.urihttps://hdl.handle.net/11616/103439
dc.identifier.volume25en_US
dc.identifier.wosWOS:000702533600009en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofThermal Science and Engineering Progressen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectNanoparticlesen_US
dc.subjectNanofluidsen_US
dc.subjectHeat transfer coefficienten_US
dc.subjectComputational intelligenceen_US
dc.subjectMagnetic effecten_US
dc.subjectDeep learningen_US
dc.titleInvestigation and prediction of ethylene Glycol based ZnO nanofluidic heat transfer versus magnetic effect by deep learningen_US
dc.typeArticleen_US

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