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Supervised learning method for prediction of heat transfer characteristics of nanofluids

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dc.contributor.author Aylı, Ece
dc.contributor.author Koçak, Eyup
dc.date.accessioned 2024-01-26T07:57:02Z
dc.date.available 2024-01-26T07:57:02Z
dc.date.issued 2023-05
dc.identifier.citation Aylı, E.; Koçak, E. (2023). "Supervised learning method for prediction of heat transfer characteristics of nanofluids", Journal of Mechanical Science and Technology, vol.37, No.5, pp.2687-2697. tr_TR
dc.identifier.issn 1738494X
dc.identifier.uri http://hdl.handle.net/20.500.12416/7009
dc.description.abstract This study focuses on the alication and investigation of the predictive ability of artificial intelligence in the numerical modelling of nanofluid flows. Numerical and experimental methods are powerful tools from an accuracy point of view, but they are also time- and cost-consuming methods. Therefore, using soft-computing techniques can improve such CFD drawbacks by patterning the CFD data. After obtaining the aropriate ANN and ANFIS architecture using the CFD data, many new data can be created without requiring numerical and experimental methods. In the scope of this research, the FCM-ANFIS and ANN methods are used to predict the thermal behaviour of the turbulent flow in a heated pipe with several nanoparticles. A parametric CFD study is carried out for water-TiO2, water-CuO, and water-SiO2 nanofluid through a pipe. The Reynolds number is varied between 7000 and 15000, and the nanofluid concentration is varied between 0.25 % and 4 %. The effects of using nanofluid on local values of Nusselt number and shear stress distribution were investigated. Numerical results indicate that with the increasing nanoparticle volume fraction of nanofluid, the average Nusselt number increases, but the required pumping power also increases. The obtained soft computing results demonstrate that the FCM clustering ANFIS has given better results both in training and testing when it is compared to the ANN architecture with an R2 of 0.9983. Regarding this, the FCM-ANFIS is an excellent candidate for calculating the Nusselt number in heat transfer problems tr_TR
dc.language.iso eng tr_TR
dc.relation.isversionof 10.1007/s12206-023-0442-5 tr_TR
dc.rights info:eu-repo/semantics/closedAccess tr_TR
dc.subject ANFIS tr_TR
dc.subject ANN tr_TR
dc.subject CFD tr_TR
dc.subject FCM tr_TR
dc.subject Forced Convection tr_TR
dc.subject Nanofluid tr_TR
dc.title Supervised learning method for prediction of heat transfer characteristics of nanofluids tr_TR
dc.type article tr_TR
dc.relation.journal Journal of Mechanical Science and Technology tr_TR
dc.contributor.authorID 265836 tr_TR
dc.contributor.authorID 283455 tr_TR
dc.identifier.volume 37 tr_TR
dc.identifier.issue 5 tr_TR
dc.identifier.startpage 2687 tr_TR
dc.identifier.endpage 2697 tr_TR
dc.contributor.department Çankaya Üniversitesi, Mühendislik Fakültesi, Makine Mühendisliği Bölümü tr_TR


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