Abstract

The injection flow rates of two liquid phases play a decisive role in the slug generation of the liquid-liquid slug flow. However, most injection flow rates so far have been constant. In order to investigate the effects of dynamic injection flow rates on the slug generation, including the slug size, separation distance and slug generation cycle time, a transient numerical model of a cross-junction square microchannel is established. The Volume of Fluid method is adopted to simulate the interface between two phases, i.e., butanol and water. The model is validated by experiments at a constant injection flow rate. Three different types of dynamic injection flow rates are applied for butanol, which are triangle, rectangular and sine wave flow rates. The dynamic injection flow rate cycles, which are related to the constant slug generation cycle time t0, are investigated. Results show that when the cycle of the disperse phase flow rate is larger than t0, the slug generation changes periodically, and the period is influenced by the cycle of the disperse phase flow rate. Among the three kinds of dynamic disperse flow rate, the rectangular wave influences the slug size most significantly, while the triangle wave influences the separation distance and the slug generation time more prominently.

Highlights

  • Due to the excellent uniform size and dynamic flow characteristics, slug flow inside microchannels has been widely used in a variety of fields [1,2]

  • The images captured by the digital camera were used to measure the slug size and separation distance

  • According to the above description, when the cycle is larger than t0, the dynamic dispersed phase injection flow rate will affect the slug generation obviously

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Summary

Introduction

Due to the excellent uniform size and dynamic flow characteristics, slug flow inside microchannels has been widely used in a variety of fields [1,2]. Ryo et al [3] conducted experiments to measure the void fraction and pressure drop of gas–liquid slug flow in circular microchannels with different diameters. They proposed a model to predict the distributions very well. Liu et al [4] investigated the effects of capillary number and flow rate ratio on the slug–bubble flow by conducting experiments and numerical simulations. They developed a correlation to describe the relationship between bubble length, capillary number and flow rate ratio

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