| Makale Türü | Özgün Makale (SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale) | ||
| Dergi Adı | Applied Thermal Engineering (Q1) | ||
| Dergi ISSN | 1359-4311 Wos Dergi Scopus Dergi | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | İngilizce | Basım Tarihi | 12-2025 |
| Cilt / Sayı / Sayfa | 281 / 1 / 128569–0 | DOI | 10.1016/j.applthermaleng.2025.128569 |
| Makale Linki | https://doi.org/10.1016/j.applthermaleng.2025.128569 | ||
| UAK Araştırma Alanları |
Yapay Zeka
Makine Öğrenmesi
Nanoteknoloji
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| Özet |
| This study experimentally examines thermo-hydraulic performance of mono and hybrid nanofluids (Fe3O4/H2O, Cu/H2O, and Fe3O4–Cu/H2O) flowing through smooth (ST) and dimpled tubes (DT) under laminar conditions (Re = 1131–2102) with constant heat flux. A total of 95 cases were tested while a constant direct magnetic field (MF = 0.03, 0.16, 0.3 T) was applied via twin coils; performance was assessed using the Heat Convection Ratio (HCR), Pressure Ratio (PR), and Performance Evaluation Criterion (PEC). Baseline validation against Shah–London and Hagen–Poiseuille correlations showed deviations ≤ 5.85 % (Nu) and ≤ 4.11 % (f). DTs enhanced heat transfer substantially: with Fe3O4/H2O, HCR in DT exceeded ST by up to 43.2 % at Re = 2102, while pressure penalties remained moderate. MF strength critically shaped outcomes: 0.16 T consistently improved HCR and yielded the best … |
| Anahtar Kelimeler |
| Constant magnetic field | Dimpled tube | Machine learning approach | Nanofluid | Polynomial regression | XGBoost |
| Atıf Sayıları | |
| Google Scholar | 2 |
| Dergi Adı | APPLIED THERMAL ENGINEERING |
| Yayıncı | Elsevier Ltd |
| Açık Erişim | Hayır |
| ISSN | 1359-4311 |
| E-ISSN | 1873-5606 |
| CiteScore | 11,0 |
| SJR | 1,579 |
| SNIP | 1,990 |