Prediction of graphite nanofluids' dynamic viscosity by means of artificial neural networks

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dc.authorid0000-0002-5743-3937
dc.contributor.authorDalkilic, A. S.
dc.contributor.authorCebi, A.
dc.contributor.authorCelen, A.
dc.contributor.authorYildiz, O.
dc.contributor.authorAcikgoz, O.
dc.contributor.authorJumpholkul, C.
dc.contributor.authorWongwises, S.
dc.date.accessioned2019-08-01T13:38:39Z
dc.date.available2019-08-01T13:38:39Z
dc.date.issued2016
dc.departmentNiğde ÖHÜ
dc.description.abstractRecently, nanofluids have been studied extensively by the researchers as a result of the developments in nano technology. It is essential for researchers to know nanofluids' physical properties in order to make calculations regarding their specific research topics. Determination of viscosity issue is an actual one due to its common usage in heat transfer and thermodynamics. In this study, graphite particles are selected to have nanofluid mixture with its base fluid of pure water. Their volumetric concentrations are varied from 0 to 2% in pure water. Once the stabilized nanofluid is prepared by a sonicator and ultrasonic bath, viscosity is measured by a viscosity meter for the temperatures ranging from 20 degrees C to 60 degrees C. Validation of the experiments have been done by means of the comparison of them with the 32 empirical correlations in the literature. Then, Artificial Neural Network (ANN) analyses have been performed in order to have better empirical correlation than those in the literature. Furthermore, detailed information on the preparation nanofluids, measurement of viscosity, a list of measured data, numerical model by Matiab software, and alteration of viscosity with temperature and concentration have been given in the paper. It was concluded that viscosity correlations in the literature can predict different types of nanofluids' viscosity although they have been derived using specific type and diameter of nano particles and their base fluids. (C) 2016 Elsevier Ltd. All rights reserved.
dc.description.sponsorshipKing Mongkut's University of Technology Thonburi; "Research Chair Grant" National Science and Technology Development Agency; Thailand Research Fund; National Research University Project
dc.description.sponsorshipAll authors would like to thank King Mongkut's University of Technology Thonburi for the support during this research in Thailand. In addition, all authors would like to thank the "Research Chair Grant" National Science and Technology Development Agency, the Thailand Research Fund and the National Research University Project for the support.
dc.identifier.doi10.1016/j.icheatmasstransfer.2016.02.010
dc.identifier.endpage42
dc.identifier.issn0735-1933
dc.identifier.issn1879-0178
dc.identifier.scopus2-s2.0-84959505412
dc.identifier.scopusqualityQ1
dc.identifier.startpage33
dc.identifier.urihttps://dx.doi.org/10.1016/j.icheatmasstransfer.2016.02.010
dc.identifier.urihttps://hdl.handle.net/11480/3674
dc.identifier.volume73
dc.identifier.wosWOS:000374803100005
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthor[0-Belirlenecek]
dc.language.isoen
dc.publisherPERGAMON-ELSEVIER SCIENCE LTD
dc.relation.ispartofINTERNATIONAL COMMUNICATIONS IN HEAT AND MASS TRANSFER
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectNanofluids
dc.subjectGraphite
dc.subjectViscosity
dc.subjectArtificial neural networks
dc.subjectANN
dc.subjectSEM
dc.titlePrediction of graphite nanofluids' dynamic viscosity by means of artificial neural networks
dc.typeArticle

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