An AI-driven innovative approach for void fraction prediction to estimate drag coefficient

In this paper, we propose an innovative artificial intelligence (AI) -based method to estimate drag coefficients in two-phase flows by predicting void fraction. To solve the problem of the limited application range of traditional empirical relationships, four AI models, namely random forest, Transformer, Mamba and ridge regression, are used in this study to train and predict horizontal gas-liquid two-phase flow void fraction database (6554 data points). By analyzing the influence of input parameter combination, it is found that liquid velocity plays a key role in reducing the prediction error, and the optimal parameter combination is pipe diameter to length ratio, pressure and liquid velocity. The results show that the random forest model has the best performance, with a mean error (ME) is only 1.32 %. Further research has shown that the Transformer model has high accuracy in evaluating the effects of a single parameter. Finally, the high accuracy of random forest in estimation of drag coefficient is verified by the correlation formula between void fraction and drag coefficient. This study reveals the potential of AI model in the prediction of complex two-phase flow parameters, and provides a new idea for intelligent prediction of drag coefficient.