Multivariate recurrence network analysis for characterizing horizontal oil-water two-phase flow

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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

64 Citations (Scopus)


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.

Original languageEnglish
Article number032910
Number of pages12
JournalPhysical Review. E, Statistical, Nonlinear and Soft Matter Physics
Issue number3
Publication statusPublished - 13 Sept 2013


  • time-series analysis
  • complex networks
  • interdependent networks
  • visibility graph
  • dynamics
  • pipe
  • patterns
  • entropy
  • systems
  • energy


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