title: Multivariate recurrence network analysis for characterizing horizontal oil-water two-phase flow.
abstract: Characterizing complex patterns arising from horizontal oil-water two-phase flows is a contemporary and challenging problem of paramount importance. We design a new multisector conductance sensor and systematically carry out horizontal oil-water two-phase flow experiments for measuring multivariate signals of different flow patterns. We then infer multivariate recurrence networks from these experimental data and investigate local cross-network properties for each constructed network. Our results demonstrate that a cross-clustering coefficient from a multivariate recurrence network is very sensitive to transitions among different flow patterns and recovers quantitative insights into the flow behavior underlying horizontal oil-water flows. These properties render multivariate recurrence networks particularly powerful for investigating a horizontal oil-water two-phase flow system and its complex interacting components from a network perspective.
authors.affiliation: School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072, China and Department of Physics, Humboldt University, Berlin 12489, Germany and Potsdam Institute for Climate Impact Research, Potsdam 14473, Germany.
authors.forename: Zhong-Ke
authors.initials: ZK
authors.lastname: Gao
authors.forename: Xin-Wang
authors.initials: XW
authors.lastname: Zhang
authors.forename: Ning-De
authors.initials: ND
authors.lastname: Jin
authors.forename: Norbert
authors.initials: N
authors.lastname: Marwan
authors.forename: Jürgen
authors.initials: J
authors.lastname: Kurths