Recurrence networks from multivariate signals for uncovering dynamic transitions of horizontal oil-water stratified flows
Recurrence networks from multivariate signals for uncovering dynamic transitions of horizontal oil-water stratified flows
复制标题
多元信号的递归网络揭示水平油水分层流的动态转变
DOI：
10.1209/0295-5075/103/50004
复制
发表时间：
2013-09-01
期刊：
影响因子：
1.8
通讯作者：
Kurths, Juergen
中科院分区：
文献类型：
--
作者：
Gao, Zhong-Ke;Zhang, Xin-Wang;Kurths, Juergen
水平油水分层流界面上液滴形成机理的表征是一个引起不同学科高度关注的基础性问题。对水平油水分层流动的形成和转化进行了实验和理论研究。设计了一种新型的多扇区电导传感器，测量了两种不同层状流型的多变量信号。利用自适应最优核时频表示(AOK TFR)，我们首先从能量和频率的角度刻画了流的行为。然后，我们从实验数据中推断出了多变量递归网络，并研究了所构建的每个网络的交叉传递性。我们发现，交叉传递性可以定量地揭示分层流动从稳定状态向不稳定状态演化时的流动行为，并对分层流动界面上液滴形成的机制有更深的了解，这是现有基于AOK TFR的方法无法完成的任务。这些发现为从复杂网络角度更好地理解导致水平油水分层流动转变的动力机制迈出了第一步。
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.