Data-Driven-Based Forecasting of Two-Phase Flow Parameters in Rectangular Channel

Abstract

In the current nuclear reactor system analysis codes, the interfacial area concentration and void fraction are mainly obtained through empirical relations based on different flow regime maps. In the present research, the data-driven method has been proposed, using four machine learning algorithms (lasso regression, support vector regression, random forest regression and back propagation neural network) in the field of artificial intelligence to predict some important two-phase flow parameters in rectangular channels, and evaluate the performance of different models through multiple metrics. The random forest regression algorithm was found to have the strongest ability to learn from the experimental data in this study. Test results show that the prediction errors of the random forest regression model for interfacial area concentrations and void fractions are all less than 20%, which means the target parameters have been forecasted with good accuracy.