Improving Nanofluid Stability and Thermal Efficiency: An Experimental Study Employing ANN Modeling for Industrial Development

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dc.contributor.authorSahin, Fevzi
dc.contributor.authorKapusuz, Murat
dc.date.accessioned2026-04-25T14:19:54Z
dc.date.available2026-04-25T14:19:54Z
dc.date.issued2025
dc.departmentSinop Üniversitesi
dc.description.abstractNanofluids are very promising as advanced heat transfer fluids; however, their widespread industrial application is hampered by their intrinsic instability issue. This study uses artificial neural networks (ANN) to evaluate nanofluid stability while accounting for surfactant content. Experimental measurements of the thermal characteristics of the most stable nanofluids were made between 20 and 60 degrees C. ANN models were then improved to predict the experimental data, yielding high regression values: R = 0.99984 for stability, R = 0.99849 for thermal conductivity, and R = 0.99975 for viscosity. The discovered correlations provide valuable new insights into the intricate link between surfactant concentration and nanofluid stability. By developing new correlations for viscosity, thermal conductivity, and stability, this work shows how nanofluids can be used for better heat transfer applications. The thermal performance of nanofluids was evaluated using performance indicators such as Mouromtseff number (Mo) and the properties enhancement ratio (PER). The highest PER value, which hit an incredible 3.941, demonstrated the nanofluids improved heat transmission capabilities. The use of nanofluids across various flow regimes is further supported by the computation of the minimal Mo value, which were found to be 1.016 and 0.981 for laminar and turbulent flow conditions, respectively.
dc.identifier.doi10.1007/s13369-025-10797-4
dc.identifier.issn2193-567X
dc.identifier.issn2191-4281
dc.identifier.orcid0000-0002-2243-8551
dc.identifier.scopus2-s2.0-105022059935
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1007/s13369-025-10797-4
dc.identifier.urihttps://hdl.handle.net/11486/8233
dc.identifier.wosWOS:001613172300001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSpringer Heidelberg
dc.relation.ispartofArabian Journal for Science and Engineering
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WOS_20260420
dc.subjectArtificial neural networks (ANN)
dc.subjectThe properties enhancement ratio (PER)
dc.subjectViscosity
dc.subjectSedimentation ratio
dc.subjectStability of nanofluids
dc.subjectThermal conductivity
dc.titleImproving Nanofluid Stability and Thermal Efficiency: An Experimental Study Employing ANN Modeling for Industrial Development
dc.typeArticle

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