Recurrence networks from multivariate signals for uncovering dynamic transitions of horizontal oil-water stratified flows

Zhong-Ke Gao*, Xin-Wang Zhang, Ning-De Jin, Reik V. Donner, Norbert Marwan, Juergen Kurths

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

74 Citations (Scopus)


Characterizing the mechanism of drop formation at the interface of horizontal oilwater stratified flows is a fundamental problem eliciting a great deal of attention from different disciplines. We experimentally and theoretically investigate the formation and transition of horizontal oil-water stratified flows. We design a new multi-sector conductance sensor and measure multivariate signals from two different stratified flow patterns. Using the Adaptive Optimal Kernel Time-Frequency Representation (AOK TFR) we first characterize the flow behavior from an energy and frequency point of view. Then, we infer multivariate recurrence networks from the experimental data and investigate the cross-transitivity for each constructed network. We find that the cross-transitivity allows quantitatively uncovering the flow behavior when the stratified flow evolves from a stable state to an unstable one and recovers deeper insights into the mechanism governing the formation of droplets at the interface of stratified flows, a task that existing methods based on AOK TFR fail to work. These findings present a first step towards an improved understanding of the dynamic mechanism leading to the transition of horizontal oil-water stratified flows from a complex-network perspective. Copyright (C) EPLA, 2013

Original languageEnglish
Article number50004
Number of pages6
JournalEurophysics Letters
Issue number5
Publication statusPublished - 25 Sep 2013


  • time-series
  • interdependent networks
  • complex networks
  • patterns
  • systems
  • pipes

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