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
Cite this
Multivariate recurrence network analysis for characterizing horizontal oil-water two-phase flow. / Gao, Zhong-Ke; Zhang, Xin-Wang; Jin, Ning-De; Marwan, Norbert; Kurths, Juergen.
In: Physical Review. E, Statistical, Nonlinear and Soft Matter Physics, Vol. 88, No. 3, 032910, 13.09.2013.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Multivariate recurrence network analysis for characterizing horizontal oil-water two-phase flow
AU - Gao, Zhong-Ke
AU - Zhang, Xin-Wang
AU - Jin, Ning-De
AU - Marwan, Norbert
AU - Kurths, Juergen
PY - 2013/9/13
Y1 - 2013/9/13
N2 - 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.
AB - 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.
KW - time-series analysis
KW - complex networks
KW - interdependent networks
KW - visibility graph
KW - dynamics
KW - pipe
KW - patterns
KW - entropy
KW - systems
KW - energy
U2 - 10.1103/PhysRevE.88.032910
DO - 10.1103/PhysRevE.88.032910
M3 - Article
VL - 88
JO - Physical Review. E, Statistical, Nonlinear and Soft Matter Physics
JF - Physical Review. E, Statistical, Nonlinear and Soft Matter Physics
SN - 1539-3755
IS - 3
M1 - 032910
ER -