Improving Nanofluid Stability and Thermal Efficiency: An Experimental Study Employing ANN Modeling for Industrial Development
Yazarlar (2)
Fevzi Şahin Ondokuz Mayıs Üniversitesi, Türkiye
Dr. Öğr. Üyesi Murat KAPUSUZ Sinop Üniversitesi, Türkiye
| Makale Türü | Özgün Makale (SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale) | ||
| Dergi Adı | Arabian Journal for Science and Engineering (Q2) | ||
| Dergi ISSN | 2193-567X Dergi Bilgileri (2026) | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | İngilizce | Basım Tarihi | 11-2026 |
| Cilt / Sayı / Sayfa | 51 / 1 / 10273–10294 | DOI | 10.1007/s13369-025-10797-4 |
| Makale Linki | https://doi.org/10.1007/s13369-025-10797-4 | ||
| UAK Araştırma Alanları |
Makine Mühendisliği
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| Özet |
| Nanofluids 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 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 … |
| Anahtar Kelimeler |
| Artificial neural networks (ANN) | Sedimentation ratio | Stability of nanofluids | The properties enhancement ratio (PER) | Thermal conductivity | Viscosity |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Web of Science | 1 |
| Scopus | 1 |
| Google Scholar | 1 |