A prediction model based on data-driven method for velocity and heat transfer coefficient of falling-film liquid on horizontal tube

Abstract

The prediction of liquid film velocity and heat transfer coefficient of horizontal-tube falling-film flow is important for the investigation and enhancement of the flow and heat transfer behavior. To date, the prediction mainly relies on semi-empirical-semi-theoretical formulas based on data fitting. This prediction method has many limitations and assumptions on working conditions, and the error is large when it is extended to practical applications. In this paper, a support vector regression machine prediction method based on sine cosine algorithm optimization is proposed to improve the prediction accuracy of the liquid film velocity and heat transfer coefficient. And the database was established by conducting horizontal-tube falling-film flow and heat transfer experiments. Results show that the support vector regression model has the best prediction performance with small sample size in comparison with other four machine learning methods and traditional correlation based on Least Square method. The RMSEs of liquid film prediction model and heat transfer prediction model are only 0.0281 and 0.0117 respectively. And the MAPEs are 8.58 and 2.63 respectively. The liquid film velocity and heat transfer coefficient were accurately predicted based on relevant parameters. The importance of these parameters was also analyzed.