Zhai Lusheng

Multi-Scale Time Asymmetry for Detecting the Breakage of Slug Flow Structure

We first employ multi-scale time asymmetry (MSA) to analyze typical chaotic signals from Schuster maps and indicate that the MSA method can characterize the distinct time asymmetry of chaotic time series. Then we propose a modified MSA method, i.e., multi-scale weighted time asymmetry, and a novel time asymmetry index to investigate fractal Brownian motion signals and demonstrate its effects on discriminating between fractal signals with different Hurst exponents. Considering that the dynamic behavior of slug flow exhibits chaotic features, we apply the MSA method to analyze experimental signals from a gas-liquid two-phase flow and find that slug flow presents a unique time asymmetric structure. In addition, we further explore the mechanism leading to the formation of time asymmetry in terms of adaptive optimal kernel time-frequency representation. The results suggest that the MSA method can be a useful tool for detecting the complex flow structure underlying a gas-liquid two-phase flow.

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 Chens 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.