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.
Original language | English |
---|---|
Article number | 032910 |
Number of pages | 12 |
Journal | Physical Review. E, Statistical, Nonlinear and Soft Matter Physics |
Volume | 88 |
Issue number | 3 |
DOIs | |
Publication status | Published - 13 Sep 2013 |
Keywords
- time-series analysis
- complex networks
- interdependent networks
- visibility graph
- dynamics
- pipe
- patterns
- entropy
- systems
- energy