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Comparative machine learning prediction study of hybrid nanofluid flow in a magnetized dimpled tube    
Yazarlar (5)
Doç. Dr. Mehmet GÜRDAL Doç. Dr. Mehmet GÜRDAL
Kastamonu Üniversitesi, Türkiye
Dr. Öğr. Üyesi Muhammed TAN Dr. Öğr. Üyesi Muhammed TAN
Kastamonu Üniversitesi, Türkiye
Emrehan Gürsoy
Karabük Üniversitesi, Türkiye
Kamil Arslan
Karabük Üniversitesi, Türkiye
Engin Gedik
Karabük Üniversitesi, Türkiye
Devamını Göster
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
Makale Türü Özgün Makale
Makale Alt Türü SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale
Dergi Adı Applied Thermal Engineering
Dergi ISSN 1359-4311 Wos Dergi Scopus Dergi
Dergi Tarandığı Indeksler SCOPUS, SCI-Exp
Dergi Grubu Q1
Makale Dili İngilizce
Basım Tarihi 12-2025
Cilt No 281
Sayı 1
Sayfalar 128569 / 0
Doi Numarası 10.1016/j.applthermaleng.2025.128569
Makale Linki https://doi.org/10.1016/j.applthermaleng.2025.128569