Abstract

The entrained droplet fraction (
) is an important quantity in annuar gas-liquid two-phase flows as it allows more precise calculation of the gas core density. This results in more accurate calculation of pressure drop in pipes involving such flows. Accurate pressure drop modelling which incorporates the entrained liquid fraction is crucial for the appropriate design of downstream oil and gas facilities and for predicting the inception of dry-out in heat transfer applications involving boiling two-phase flows. While experimentation and correlations from the experimental data are widely used for closure relationships in prediction models (such as the two-fluid model), this method has drawback of the prediction limited to the range of data and discontinuities when mechanistic models (embedded with these correlations) are solved. Furthermore, correlation with a large number of input variables is usually difficult as the prediction contains a large amount of scatter. Machine learning methods are known to overcome this under-fitting problem. This study proposes an artificial neural network (ANN) model for the entrained liquid fraction in annular gas-liquid flows. Using the superficial gas velocity, superficial liquid velocity, gas viscosity, liquid viscosity, gas density, liquid density, pipe diameter, and liquid surface tension as input variables, 6 neurons (chosen after a sensitivity analysis) were used to relate these to the output variable, e. The results show that the ANN model performed well exhibiting much less scatter than previous widely used correlations. Furthermore, it was demonstrated from a sensitivity analysis that has the most impact on the ANN model when removed, and is the most significant variable. To varying degrees, other variables such as the superficial liquid velocity and the gas density were shown to have lesser effects on the accuracy of the ANN model. Based on the 1367 data points gathered, it was quantitatively shown that the new ANN model gave superior predictions of the entrained droplet fraction when compared to two previous correlations developed from even larger datasets.