Modeling Liquid Droplet Sizes in Gas–Liquid Annular Flow

Abstract

Understanding droplet sizes is crucial for modeling gas–liquid annular flow and has many applications across various industries. This paper presents a comprehensive review and analysis of experimental data on droplet-size characteristics in gas–liquid annular flow from the open-source literature. The existing models for droplet-size predictions are evaluated against experimental data. Based on the data analysis, we propose two new correlations to predict the maximum droplet diameter and Sauter mean droplet diameter. Model evaluation and parametric study show that the new correlations capture the effects of fluid properties and flowing conditions on the droplet diameters well, outperforming other existing models. The average absolute relative errors are reduced to 14.8% and 29% for the maximum droplet diameter and the Sauter mean droplet diameter, respectively.