| Yazarlar |
Emrehan Gürsoy
Kardemir Karabük Iron Steel Industry Trade & Co. Inc., Turkey |
Dr. Öğr. Üyesi Muhammed TAN
Kastamonu Üniversitesi, Türkiye |
Doç. Dr. Mehmet GÜRDAL
Kastamonu Üniversitesi, Türkiye |
Dr. Öğr. Üyesi Yücel ÇETİNCEVİZ
Kastamonu Üniversitesi, Türkiye |
| 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 |
| Dergi Tarandığı Indeksler | SCI-Exp, SCOPUS, Curation, Current Contents Engineering Computing & Technology, Essential Science Indicators, Pdf2xml, Pdf2xml, Reference Master, Sophia |
| 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://www.sciencedirect.com/science/article/pii/S0965997824002217 |
| Atıf Sayıları |
