From experimental data to predictions: Artificial intelligence supported new mathematical approaches for estimating thermal conductivity, viscosity and zeta potential in Fe3O4-water magnetic nanofluids
| dc.contributor.author | Sahin, Fevzi | |
| dc.contributor.author | Genc, Omer | |
| dc.contributor.author | Gokcek, Murat | |
| dc.contributor.author | Colak, Andas Batur | |
| dc.date.accessioned | 2024-02-12T08:51:32Z | |
| dc.date.available | 2024-02-12T08:51:32Z | |
| dc.date.issued | 2023 | en_US |
| dc.department | İstanbul Ticaret Üniversitesi | en_US |
| dc.description.abstract | Magnetic 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 ?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 ?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. | en_US |
| dc.identifier.doi | 10.1016/j.powtec.2023.118974 | en_US |
| dc.identifier.scopus | 2-s2.0-85171158071 | en_US |
| dc.identifier.scopusquality | N/A | en_US |
| dc.identifier.uri | https://hdl.handle.net/11467/7139 | |
| dc.identifier.uri | https://doi.org/10.1016/j.powtec.2023.118974 | |
| dc.identifier.volume | 430 | en_US |
| dc.identifier.wos | WOS:001150061600001 | en_US |
| dc.identifier.wosquality | Q2 | en_US |
| dc.indekslendigikaynak | Web of Science | en_US |
| dc.indekslendigikaynak | Scopus | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | Elsevier | en_US |
| dc.relation.ispartof | Powder Technology | en_US |
| dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Başka Kurum Yazarı | en_US |
| dc.rights | info:eu-repo/semantics/embargoedAccess | en_US |
| dc.subject | Fe3O4, Magnetic nanofluid, Thermal conductivity, Viscosity, Zeta potential, Artificial neural network | en_US |
| dc.title | From experimental data to predictions: Artificial intelligence supported new mathematical approaches for estimating thermal conductivity, viscosity and zeta potential in Fe3O4-water magnetic nanofluids | en_US |
| dc.type | Article | en_US |
