Symbolic transition network for characterizing the dynamics behaviors of gas-liquid two-phase flow patterns

Gas-liquid two-phase flows are widely encountered in many industrial applications, and the evolutionary dynamics of the flow patterns are fundamental knowledge for the establishment of the two-phase controlling system. In this work, we propose a symbolic complex network-based method for identifying the evolutionary dynamics of the gas-liquid two-phase flow system. First, we establish a series of symbolic networks with the derived gas-liquid two-phase flow symbolic series. Then we suggest a symbolic network index, which is the number of enumerated elementary cycles, to reveal the periodic oscillation properties of various gas-liquid flow patterns. In addition, we estimate the symbolic network average degree < C > and average shortest path length < L > to reveal the evolutionary dynamics of the flow patterns. The results suggest that our developed method is efficient for detecting the dynamics of the two-phase flow system, which can be further applied to other complex fluid systems. (c) 2023 Published by Elsevier B.V.