Application of artificial neural networks for prediction modeling and analysis of the thermophysical properties of Fe₃O₄/water ferrofluid using experimental measurement
Scientific Reports, cilt.16, sa.1, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 16 Sayı: 1
- Basım Tarihi: 2026
- Doi Numarası: 10.1038/s41598-026-66623-w
- Dergi Adı: Scientific Reports
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, BIOSIS, Chemical Abstracts Core, EMBASE, MEDLINE, Directory of Open Access Journals, Zoological Record, Academic Search Ultimate (EBSCO), Natural Science Collection (ProQuest), Biological Science Database (ProQuest), Biomedical Reference Collection: Corporate Edition (EBSCO), Health Research Premium Collection (ProQuest)
- Anahtar Kelimeler: Artificial Neural Network (ANN), Dynamic Viscosity, Energy Efficiency, Fe3O4/Water Ferrofluid, Thermal Conductivity
- Açık Arşiv Koleksiyonu: AVESİS Açık Erişim Koleksiyonu
- İstanbul Gelişim Üniversitesi Adresli: Evet
Özet
The use of nanofluids has attracted significant attention due to their improved thermal performance compared with conventional heat transfer fluids. Because nanoparticles are on the nanometer scale, problems such as channel blockage and severe abrasion can be reduced. Among these, Ferrofluid containing Fe3O4 nanoparticles is of particular interest to researchers due to its unique properties, including favorable magnetic properties and environmental friendliness. In this study, an artificial neural network model is developed to predict the thermal behavior of Fe3O4/Water Ferro-nanofluid, and the effective factors, such as volume fraction (φ = 0.05% to 4.0%) and temperature (T = 24 °C to 50 °C), on thermal conductivity and dynamic viscosity are investigated. The performance of the ANN is validated by comparing the obtained results. The results show that when the changes from = 0.05% to 4.0%, the dynamic viscosity increases from 0 cP to 0.77 cP. The results showed that thermal conductivity increased with increasing nanoparticle concentration, while dynamic viscosity decreased with increasing temperature. In contrast, viscosity increased at higher nanoparticle volume fractions due to stronger particle interactions within the base fluid. The ANN models showed reasonable overall performance in estimating thermal conductivity, with correlation coefficients of 0.98 and errors of 1.56 × 10− 5.