Prediction of entrainment fraction in two-phase gas-liquid co-current annular flow-A machine learning approach

Abstract

Entrainment fraction is a crucial parameter to determine liquid holdup, pressure drop, and dryout in two-phase annular flow. It determines the fraction of liquid flows as liquid droplets in the gas core. Usually, empirical correlations are used to predict entrainment fraction. However, these correlations forecast entrainment fractions with large Mean Absolute Percentage Error (MAPE), especially when one correlation is used to predict entrainment fractions for different operating conditions. For instance, existing models considered in this study predict entrainment with MAPE of as much as 120.7 %. Besides around 40 % and 24 % of data were outside of ±30 % and ±50 % ranges. In the framework of this study, a Deep Neural Network (DNN model) has been developed to predict entrainment fraction in annular flow that is applicable for different operating conditions with good accuracy. The neural network is trained and tested for around 1213 entrainment fraction data points extracted from 7 different literature studies. It covers pressure ranges 0.1–20 MPa, tube diameter 5–32 mm with working fluid- air/water, steam/water, and Freon-113. The developed DNN model predicts entrainment fraction with the lowest MAPE compared to the existing models on unseen 155 data from 6 authors that are not used to train or test the model. Furthermore, only 17.61 % and 30.81 % of data remain outside of ±30 % and ±20% limits respectively whereas these values are at least 35.66 % and 52.86 % for existing models that are examined for comparison here.