Dependence of critical heat flux in vertical flow systems on dimensional and dimensionless parameters using machine learning

• Published in: International journal of heat and mass transfer 2024-06, Vol.225, p.125441, Article 125441
• Main Authors: Khalid, Rehan Zubair, Ullah, Atta, Khan, Asifullah, Al-Dahhan, Muthanna H., Inayat, Mansoor Hameed
• Format: Article
• Language: English
• Subjects: Artificial neural networks; Critical heat flux; Flow boiling; Machine learning; Nuclear power plant
• ISSN: 0017-9310
• DOI: 10.1016/j.ijheatmasstransfer.2024.125441

•A comprehensive database of critical heat flux containing more than 15,000 experimental data points is generated.•Multiple machine learning (ML) algorithms are used to predict critical heat flux trained and tested on the generated database.•Artificial neural network (ANN) and extreme gradient boosting (XGBoost) algorithms outperform other counterpart techniques.•Critical heat flux for vertical flow systems is found to be highly dependent upon Weber number and boiling number. The critical heat flux (CHF) associated with the departure from nucleate boiling (DNB) determines the design and safety aspects of two-phase flow boiling systems. Despite the availability of several predictive tools, within the thermal engineering community, the pursuit of an accurate and robust CHF model remains a significant challenge. In this unique study, we extracted a substantial database from literature to develop machine-learning (ML) models to predict CHF for vertical flows commonly employed in the process industry. The extensive database encompasses 15,006 experimental data points gathered from diverse sources covering wide range of geometric and operational ranges of D, L, P, G, h, and x. This database is then employed to develop five ML – based models, namely Artificial Neural Networks (ANN), K-Nearest Neighbors (KNN), Adaptive Boosting (AdaBoost), Extreme Gradient Boosting (XGBoost), and Random Forests (RF). The selection of optimal input features is conducted by exploring various combinations of both dimensional and dimensionless parameters to identify the most influencing inputs for CHF prediction. The models incorporating features D, L, P, G, h, x, L/D, ρr, We, and BO achieve CHF predictions with Mean Absolute Errors (MAEs) of 8.85 % for the ANN model and 10.39 % for the XGBoost model. The optimized ML models outperformed even the highly reliable CHF correlations and lookup tables. The feature importance analysis of input parameters highlights strong dependence of CHF on dimensionless numbers such as Weber number and Boiling number.