From experimental data to predictions: Artificial intelligence supported new mathematical approaches for estimating thermal conductivity, viscosity and zeta potential in Fe3O4-water magnetic nanofluids

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dc.authoridSAHIN, FEVZI/0000-0002-4808-4915
dc.authoridColak, Andac Batur/0000-0001-9297-8134
dc.authoridGENC, Omer/0000-0003-0849-6867
dc.authoridGokcek, Murat/0000-0002-7951-4236
dc.contributor.authorSahin, Fevzi
dc.contributor.authorGenc, Omer
dc.contributor.authorGokcek, Murat
dc.contributor.authorColak, Andac Batur
dc.date.accessioned2024-11-07T13:32:37Z
dc.date.available2024-11-07T13:32:37Z
dc.date.issued2023
dc.departmentNiğde Ömer Halisdemir Üniversitesi
dc.description.abstractMagnetic nanofluids (MNs) are considered advanced heat transfer fluids of the future due to their ability to function as intelligent fluids, with the applied external magnetic field effect being readily manageable. In this study, firstly, the stabilities of Fe3O4-water MNs prepared at 0.1, 0.25, 0.5, 0.75 and 1 mass ratios were determined by zeta potential measurement. The thermal conductivity and viscosities of MNs with appropriate stability were measured at 20-60 degrees C for all mass ratios. Secondly, using experimental data, two different artificial neural network (ANN) models were developed: one for thermal conductivity and viscosity depending on the temperature (20-60 degrees C) and mass ratio values and one for zeta potential depending on pH and mass ratio. Finally, using the obtained ANN data, two new mathematical correlations are proposed to predict thermal conductivity and viscosity. The study's results revealed that the developed ANN model has MSE and R values of 4.51E-06 and 0.99968, respectively, for thermal conductivity and viscosity of Fe3O4-water MNs can be accurately predicted by novel mathematical correlations.
dc.identifier.doi10.1016/j.powtec.2023.118974
dc.identifier.issn0032-5910
dc.identifier.issn1873-328X
dc.identifier.scopus2-s2.0-85171158071
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.powtec.2023.118974
dc.identifier.urihttps://hdl.handle.net/11480/15521
dc.identifier.volume430
dc.identifier.wosWOS:001150061600001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherElsevier
dc.relation.ispartofPowder Technology
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241106
dc.subjectMagnetic nanofluid
dc.subjectThermal conductivity
dc.subjectViscosity
dc.subjectZeta potential
dc.subjectArtificial neural network
dc.titleFrom experimental data to predictions: Artificial intelligence supported new mathematical approaches for estimating thermal conductivity, viscosity and zeta potential in Fe3O4-water magnetic nanofluids
dc.typeArticle

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