Comparative machine learning prediction study of hybrid nanofluid flow in a magnetized dimpled tube
Yazarlar (5)
Doç. Dr. Mehmet GÜRDAL Kastamonu Üniversitesi, Türkiye
Dr. Öğr. Üyesi Muhammed TAN Kastamonu Üniversitesi, Türkiye
Dr. Öğr. Üyesi Emrehan Gürsoy Karabük Üniversitesi, Türkiye
Prof. Dr. Kamil Arslan Karabük Üniversitesi, Türkiye
Engin Gedik Karabük Üniversitesi, Türkiye
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
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
Science Direct
BM Sürdürülebilir Kalkınma Amaçları
Atıf Sayıları
Google Scholar 2
Comparative machine learning prediction study of hybrid nanofluid flow in a magnetized dimpled tube

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