Volume 103, Number 5, September 2013
|Number of page(s)||6|
|Published online||26 September 2013|
Recurrence networks from multivariate signals for uncovering dynamic transitions of horizontal oil-water stratified flows
1 School of Electrical Engineering and Automation, Tianjin University - Tianjin 300072, China
2 Department of Physics, Humboldt University - Berlin 12489, Germany, EU
3 Potsdam Institute for Climate Impact Research - Potsdam 14473, Germany, EU
4 Institute for Complex Systems and Mathematical Biology, University of Aberdeen - Aberdeen AB24 3UE, UK, EU
Received: 16 June 2013
Accepted: 30 August 2013
Characterizing the mechanism of drop formation at the interface of horizontal oil-water 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.
PACS: 05.45.Tp – Time series analysis / 47.55.-t – Multiphase and stratified flows / 89.75.Fb – Structures and organization in complex systems
© EPLA, 2013
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