Multivariate weighted complex network analysis for characterizing nonlinear dynamic behavior in two-phase flow

Charactering nonlinear dynamic behavior in gas-liquid two-phase flow is a contemporary and challenging problem of significant importance. We in this paper first systematically carry out gas-liquid two-phase flow experiments in a small diameter pipe for measuring local flow information from different flow patterns. Then, we propose a modality transition-based network for mapping the experimental multivariate measurements into a directed weighted complex network. In particular, we derive multivariate complex networks from different flow conditions and demonstrate that the generated networks corresponding to different flow patterns exhibit distinct topological structures. For each generated network, we exploit weighted clustering coefficient and closeness centrality to quantitatively probe the network topological properties associated with dynamic flow behavior. The results suggest that our multivariate complex network analysis allows quantitatively uncovering the transitions of distinct flow patterns and yields deep insights into the nonlinear dynamic behavior underlying gas-liquid flows.