Experimental and multioutput explainable machine learning investigation of thermal-hydraulic-entropic analyses in channels equipped with sinusoidal turbulators
Yazarlar (5)
Doç. Dr. Mehmet GÜRDAL Kastamonu Üniversitesi, Türkiye
Dr. Öğr. Üyesi Muhammed TAN Kastamonu Üniversitesi, Türkiye
Doç. Dr. Aziz Hakan Altun Necmettin Erbakan Üniversitesi, Türkiye
Dr. Öğr. Üyesi Emrehan Gürsoy Recep Tayyip Erdoğan Üniversitesi, Türkiye
Prof. Dr. Adnan Berber Selçuk Üniversitesi, Türkiye
| Makale Türü |
Özgün Makale
(SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale)
|
||
| Dergi Adı | International Communications in Heat and Mass Transfer (Q1) | ||
| Dergi ISSN | 0735-1933 Dergi Bilgileri (2026) | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | Ingilizce | Basım Tarihi | 06-2026 |
| Kabul Tarihi | – | Yayınlanma Tarihi | 01-06-2026 |
| Cilt / Sayı / Sayfa | 175 / 0 / – | DOI | 10.1016/j.icheatmasstransfer.2026.111130 |
| Makale Linki | https://doi.org/10.1016/j.icheatmasstransfer.2026.111130 | ||
| UAK Araştırma Alanları |
Isı Transferi
Akışkanlar Mekaniği
Termodinamik
|
||
| Özet |
| This study investigates the thermo-hydraulic and entropic behavior of air flow in channels equipped with sinusoidal turbulators through a combined experimental and machine learning framework. Unlike previous studies that focused on conventional thermohydraulic measurements or single-output black-box machine learning predictions, this study presents the first experimentally validated scope of multi-output explainable machine learning for the simultaneous prediction of heat transfer, pressure loss, and entropy generation. Experiments were conducted for Reynolds numbers between 17,000 and 73,000 and three turbulator widths (a = D/4, D/2, 3D/4) to assess their effects on heat transfer, pressure drop, and entropy generation characteristics relative to a smooth reference channel. The smallest turbulator width provided the most balanced thermo-hydraulic behavior within the investigated range and yielded a … |
| Anahtar Kelimeler |
| Entropy generation | Forced heat convection | Machine learning | Prediction | Pressure drop | SHAP | Turbulent flow |
Science Direct
Experimental and multioutput explainable machine learning investigation of thermal-hydraulic-entropic analyses in channels equipped with sinusoidal turbulators
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Web of Science | 1 |
| Google Scholar | 1 |