Complex Networks with Community Structure from Multivariate Signals in Gas-liquid Two-phase Flow

摘要

Based on the multivariate time series measured from gas-liquid two-phase flow, we construct complex networks to investigate different gas-liquid flow patterns. The results indicate that our approach can well distinguish and characterize different flow patterns in the sense that different communities correspond to different flow patterns and the community structure can characterize the flow pattern dynamical properties. In this paper, we provide a new perspective and a novel way for understanding the complex dynamics underlying two-phase flow in terms of complex network theory.