Advanced Predictive Models for Saturated Flow Boiling in Horizontal Small-Scale Channels
Makaleler > Tam Makale · 2025
Yazarlar: Zouhir Zeroual, Özay AKDEMİR, Oguz Emrah Turgut, Mustafa Turhan ÇOBAN
Heat Transfer Engineering, Uluslararası, Wos, 2025, ss 1-27
DOI: 10.1080/01457632.2025.2553978
Özet
Accurate prediction of flow boiling heat transfer in small-scale channels is primordial for optimizing miniature heat exchanger design. This study leverages a comprehensive database to enhance predictive modeling of saturated flow boiling in horizontal small-diameter channels. The primary dataset includes 15,490 data points from 48 published experimental studies, covering 18 pure refrigerants, along with a holdout dataset of 961 data points for model validation. Four machine learning (ML) models were assessed, with Extreme Gradient Boosting model delivering the best performance, yielding a mean absolute error (MAE) of 5.02% and a coefficient of determination of 0.9878 using dimensionless input features. However, a performance drop of 20–30% is observed for all ML models on the holdout dataset, indicating limited generalization. A systematic evaluation of existing empirical correlations also reveals poor predictive performance due to their reliance on limited datasets. To address these limitations, a new correlation is developed, integrating fluid-dependent factors and accounting for flow regime variations across the vapor quality range. The proposed correlation achieves an MAE of 16.61%, with 83.93% of predictions within ±30% error band. Validation against the holdout dataset further confirms its reliability, yielding an MAE of 17.24 and 82% of predictions within the ±30% error band.