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Python-based machine learning estimation of thermo-hydraulic performance along varying nanoparticle shape, nanofluid and tube configuration      
Yazarlar (4)
Emrehan Gürsoy Null
Türkiye
Dr. Öğr. Üyesi Muhammed TAN Dr. Öğr. Üyesi Muhammed TAN
Türkiye
Doç. Dr. Mehmet GÜRDAL Doç. Dr. Mehmet GÜRDAL
Türkiye
Doç. Dr. Yücel ÇETİNCEVİZ Doç. Dr. Yücel ÇETİNCEVİZ
Türkiye
Devamını Göster
Özet
In this research article, a Python-based machine learning model prediction study was conducted based on the study results obtained from sudden expansion tubes containing different expansion angles, dimpled fin structures and nanofluids, whose thermo-hydraulic performance was previously examined. In the study, Artificial Neural Network and Ridge regression models were used to make predictions on the average Nusselt number (Nu), average Darcy friction factor (f) and performance evaluation criteria (PEC). Physical variations of the sudden expansion tube were taken into account and a detailed comparison of the results was made. A superior average Nu was acquired as 172.45 %, 22.05 %, 17.18 %, 13.65 %, and 7.76 % compared to Ag-MgO/H2O, Al2O3/H2O (blade), CoFe2O4/H2O, Al2O3/H2O (cylindrical), and Al2O3/H2O (platelet), respectively. The highest Performance Evaluation Criteria (PEC) for Re ...
Anahtar Kelimeler
CFD | Forced convection | Machine learning | Nanofluid | Python | Various dimpled fins
Makale Türü Özgün Makale
Makale Alt Türü SSCI, AHCI, SCI, SCI-Exp dergilerinde yayımlanan tam makale
Dergi Adı Advances in Engineering Software
Dergi ISSN 0965-9978 Wos Dergi Scopus Dergi
Dergi Tarandığı Indeksler SCI-Expanded
Dergi Grubu Q1
Makale Dili İngilizce
Basım Tarihi 01-2025
Cilt No 199
Sayı 1
Sayfalar 103814 / 0
Doi Numarası 10.1016/j.advengsoft.2024.103814
Makale Linki https://doi.org/10.1016/j.advengsoft.2024.103814