Prediction of heat transfer coefficient and pressure drop of flow boiling and condensation using machine learning

Abstract

Heat transfer coefficient and pressure drop are two critical parameters in diverse thermal engineering applications, and the prediction of these parameters plays a crucial role in the development of heat exchangers. Traditional methods are often reliant on empirical correlations based on limited refrigerants and experimental conditions. The present research explores the application of machine learning methods to predict the heat transfer coefficient and pressure drop of a wide range of pure and mixed refrigerants. The methodology involves the compilation of over 20, 000 experimental observations encompassing an extensive range of operating conditions, heat exchanger geometries, and refrigerants. A total of 27 machine learning algorithm models, such as regression tree, support vector machines, and deep neural networks, are trained on this diverse dataset to discern intricate patterns and dependencies. After dividing the dataset into 80% for training and the rest for validation, the machine learning prediction shows an ability to predict the heat transfer coefficient and the pressure drop with high accuracy in most of the refrigerants. Notably, the accuracy achieved by the machine learning models surpassed that of conventional correlations, highlighting their superior predictive capabilities. Furthermore, the accurate prediction of heat transfer enhancement methods added another layer of validation to the model’s effectiveness. It is noteworthy that the dataset employed in this study has been made publicly accessible online.