A new entrainment rate model for annular two-phase flow. (March 2020)

• Record Type: Journal Article
• Authors: Wang, Guanyi; Sawant, Pravin; Ishii, Mamoru
• Is Part Of: International journal of multiphase flow. Volume 124(2020)
• Publication Date: 2020-03
• Subjects: Droplet entrainment -- Entrainment rate -- Annular flow -- Two-phase flow -- Film extraction method; Multiphase flow -- Periodicals; Écoulement polyphasique -- Périodiques; Multiphase flow; Periodicals; 620.1064
• DOI: 10.1016/j.ijmultiphaseflow.2019.103185
• Languages: English
• ISSNs: 0301-9322

Abstract

Highlights: Available instrumentation methods, database, and models of entrainment rate are reviewed A new entrainment rate correlation that applicable to various fluids and a wide range is developed The performances of various entrainment rate models on multiple databases are evaluated Abstract: Reliable entrainment rate model is of great importance for the prediction of annular flow as it specifies the mass and momentum transfer between the liquid film and the droplet in the gas core. Various entrainment rate models have been proposed, yet few of them have a satisfying performance for a wide range of flow conditions, including gas/liquid phase flow rates, fluid type, channel size, etc. To develop a reliable entrainment rate model that is applicable to various fluids, instrumentation methods and available databases are reviewed in detail. The database for model development is determined by analyzing the instrumentation reliability and eliminating data points with the obvious error. The entrainment rate and the superficial liquid flow rate are nondimensionalized as Reynolds number using Laplace length as length scale, which is a critical wavelength of Taylor instability and related to the droplet size for annular flow. The entrainment rate model is developed based on the new nondimensional numbers, and the performance has been compared with various previous models. The evaluation results show that the prediction accuracy of the new model is better than previous models for a wide range of data.