A new method for the study of two-phase flow patterns based on the chaotic characteristic method of image fields

Abstract

The phenomenon of two-phase flow is widespread in industrial production. Different flow regimes have different mechanisms for heat-transfer and flowing. Changes of flow regimes in a channel can lead to flow resistance, a change of stability and heat transfer problems. Therefore, understanding the dynamics and identification of the flow regime in a gas–liquid two-phase flow is vital in industrial production. Because of complex interfacial effects and relative motion between the phases, it is difficult to accurately identify the two-phase flow patterns. This paper emphasizes the study of flow dynamics, and flow pattern identification. This paper also proposes a new method for extracting time series. Each frame of the video signal is divided into smaller areas. The gray scale difference of two adjacent frames is calculated to obtain the maximum points in the smaller areas and form a time series. The maximal Lyapunov exponents of time series are respectively extracted, and its matrix is composed. The videos of gas–liquid two-flow patterns are divided into different chaotic areas by the characteristics of the Lyapunov exponent. Then the overall and detailed analyses are conducted respectively by zero and one distribution map and contour map. The mechanism of gas–liquid two-phase flow is analyzed by combining the fractal box dimension, Shannon entropy, and the average value of the maximal Lyapunov exponent matrix. As there are chaotic characteristics with different intensities in the background of the gas–liquid two-phase flow video and changed phase interfaces, the results show that the extracting method of the maximum distance series of small areas combined with the maximal Lyapunov exponent can be used to distinguish the characteristics of different flow patterns, which is proven to be an effective method for analyzing the gas–liquid two-phase flow signals.