Local Property of Recurrence Network for Investigating Gas-Liquid Two-Phase Flow Characteristics

Understanding the dynamics of gas-liquid two-phase flows is a challenge in the fields of nonlinear dynamics. We first construct and analyze a recurrence network from Chen's chaotic system and find that the network local statistic is feasible to characterize chaotic dynamics associated with unstable periodic orbits. Then we construct recurrence networks from gas-liquid two-phase flow experimental signals and associate the network topological statistic with the flow pattern dynamics. The results indicate that the recurrence network could be a powerful tool for the dynamic characterization of experimental gas-liquid two-phase flows.