Complex networks from multivariate time series for characterizing nonlinear dynamics of two-phase flow patterns
多元时间序列复杂网络流型动力学分析

We use finite element analysis method to optimize and design a new curve half-ring conductance sensor for gas-liquid two-phase flow system. Then we carry out gas-liquid two-phase flow experiment in multiphase flow loop facility, and use the designed sensor to measure multivariate time series corresponding to different flow patterns. According to the measured signals, we construct complex networks from multivariate time series for different flow patterns by a network inference method. Through investigating the community structures of the constructed networks, we find that different communities correspond to different flow patterns and the network statistics in community can be used to effectively characterize the dynamic behavior of different flow patterns. In this regard, our method can be a powerful tool for identifying flow patterns and uncovering the nonlinear dynamics governing the evolution of different flow patterns.