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

Gao Zhongke; Zhang Xinwang; Jin Ningde; Donner Reik V.; Marwan Norbert; Kurths Jürgen

Chinese︱Feedback︱Save this page

• Elaborating Academic Views 　　　　 • Exchanging Innovative Ideas
• Protecting Intellectual Properties 　　• Fast Sharing Science Papers

Sponsored by the Center for Science and Technology Development of the Ministry of Education
Supervised by Ministry of Education of the People's Republic of China

Home > Papers

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

Gao Zhongke 1 * #,Zhang Xinwang 2,Jin Ningde 2,Donner Reik V. 3,Marwan Norbert 3,Kurths Jürgen 4

1.School of Electrical Engineering and Automation, Tianjin University, TianJin 300072;Potsdam Institute for Climate Impact Research, Potsdam 14473, Germany;Department of Physics, Humboldt University, Berlin 12489, Germany

2.School of Electrical Engineering and Automation, Tianjin University, TianJin 300072

3.Potsdam Institute for Climate Impact Research, Potsdam 14473, Germany

4.Potsdam Institute for Climate Impact Research, Potsdam 14473, Germany;Department of Physics, Humboldt University, Berlin 12489, Germany;Institute for Complex Systems and Mathematical Biology, University of Aberdeen, Aberdeen 10 AB243UE, UK

*Correspondence author

#Submitted by

Subject:

Funding: none

Opened online:14 August 2013

Accepted by: none

Citation: Gao Zhongke,Zhang Xinwang,Jin Ningde.Recurrence networks from multivariate signals for uncovering dynamic transitions of horizontal oil-water stratified flows[OL]. [14 August 2013] http://en.paper.edu.cn/en_releasepaper/content/4554103

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 flow behavior from the energy and frequency point of view. Then, we infer multivariate recurrence networks from experimental data and investigate the cross-transitivity for each constructed network. We find that the cross-transitivity from recurrence network analysis allows quantitatively uncovering the flow behavior when the stratified flow evolves from stable state to unstable state and recovers deeper insights into the mechanism governing the formation of drops at the interface of stratified flows, a task that existing method based on AOK TFR fails to work. These interesting and significant 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

Keywords: Multivariate recurrence network; Horizontal oil-water stratified flow; Cross-transitivity; Experiments

For this paper

● PDF (0B)
● Revision 0 　　
● Print this paper
● Recommend this paper to a friend
● Add to my favorite list

Saved Papers

Please enter a name for this paper to be shown in your personalized Saved Papers list

Tags

Add yours

Related Papers

Statistics

PDF Downloaded	240
Bookmarked	0
Recommend	6
Comments	Array

Submit your papers

Alert Name:
Alerting to:
Authentication email will be sent to your email address in 24 hours
Frequency:
Email Message Format:	Plain text Graphical(HTML)

Complete the form below and we will recommend the selected titles to your friends on your behalf. * Indicates a required field.
Your name*:
Your email address*:
Recipient's name*:
Recipient's email address*:
(multiple recipient's names and email addresses should be separated with semicolons)
Your comments:	I thought you would find the page(s) useful.

Your name:
Your email address:
Recipient's name:
Recipient's email address:
(multiple recipient's names and email addresses should be separated with semicolons)
Your comments:	I thought you would find this page useful.

Disclaimer: This message was sent to your friend using the "Send it to a friend" facility on the Sciencepaper Online’ WWW site, http://www.paper.edu.cn/en. The Sciencepaper Online is not responsible for the content of this email, and anything said in this email does not necessarily reflect the Sciencepaper Online's views.

	Check out RSS, or use RSS reader to subscribe this item

Saved Papers