An experimental and new study on thermal conductivity and zeta potential of Fe3O4/water nanofluid: Machine learning modeling and proposing a new correlation

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dc.authoridGENC, Omer/0000-0003-0849-6867
dc.authoridGokcek, Murat/0000-0002-7951-4236
dc.authoridColak, Andac Batur/0000-0001-9297-8134
dc.authoridSAHIN, FEVZI/0000-0002-4808-4915
dc.contributor.authorSahin, Fevzi
dc.contributor.authorGenc, Omer
dc.contributor.authorGokcek, Murat
dc.contributor.authorColak, Andac Batur
dc.date.accessioned2024-11-07T13:32:55Z
dc.date.available2024-11-07T13:32:55Z
dc.date.issued2023
dc.departmentNiğde Ömer Halisdemir Üniversitesi
dc.description.abstractIt is important to predict the thermophysical properties of nanofluids, which have higher heat transfer perfor-mance compared to the base fluid, without the need for experimental studies. In this study, two different artificial neural networks were created to predict the thermal conductivity and zeta potential of Fe3O4/water nanofluid. The thermal conductivity and zeta potential of the Fe3O4/water nanofluid prepared at three different concen-trations were experimentally measured. An innovative mathematical correlation is proposed to calculate thermal conductivity based on temperature and concentration using the obtained experimental data. Considering that the correlations in the literature can generally be calculated according to concentration, the novelty of the proposed model stands out. The calculated values for thermal conductivity and zeta potential of the created artificial neural network and the new mathematical correlation were compared with the results of the experiments. In addition, a comprehensive performance analysis was made by calculating different performance parameters. The R values of the neural network models were above 0.99 and mean squared error values were obtained as 1.47E-05 and 1.58E-06, respectively. In addition, the mean deviation values calculated for the thermal conductivity of the network model were 0.03%, while it was 0.05% for the new mathematical correlation. The study results showed that ANN models can predict the thermal conductivity and zeta potential of Fe3O4/water nanofluid with high accuracy. The proposed new mathematical correlation was also found to have higher error rates compared to the ANN model, although it was able to calculate thermal conductivity values with high accuracy.
dc.identifier.doi10.1016/j.powtec.2023.118388
dc.identifier.issn0032-5910
dc.identifier.issn1873-328X
dc.identifier.scopus2-s2.0-85149439060
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.powtec.2023.118388
dc.identifier.urihttps://hdl.handle.net/11480/15671
dc.identifier.volume420
dc.identifier.wosWOS:000953751100001
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.subjectNanofluid
dc.subjectThermal conductivity
dc.subjectZeta potential
dc.subjectArtificial neural network
dc.titleAn experimental and new study on thermal conductivity and zeta potential of Fe3O4/water nanofluid: Machine learning modeling and proposing a new correlation
dc.typeArticle

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