Motif distributions in phase-space networks for characterizing experimental two-phase flow patterns with chaotic features

Zhong Ke Gao, Ning De Jin, Wen Xu Wang, Ying-Cheng Lai

Research output: Contribution to journalArticle

66 Citations (Scopus)

Abstract

The dynamics of two-phase flows have been a challenging problem in nonlinear dynamics and fluid mechanics. We propose a method to characterize and distinguish patterns from inclined water-oil flow experiments based on the concept of network motifs that have found great usage in network science and systems biology. In particular, we construct from measured time series phase-space complex networks and then calculate the distribution of a set of distinct network motifs. To gain insight, we first test the approach using time series from classical chaotic systems and find a universal feature: motif distributions from different chaotic systems are generally highly heterogeneous. Our main finding is that the distributions from experimental two-phase flows tend to be heterogeneous as well, suggesting the underlying chaotic nature of the flow patterns. Calculation of the maximal Lyapunov exponent provides further support for this. Motif distributions can thus be a feasible tool to understand the dynamics of realistic two-phase flow patterns.

Original languageEnglish (US)
Article number016210
JournalPhysical Review E - Statistical, Nonlinear, and Soft Matter Physics
Volume82
Issue number1
DOIs
StatePublished - Jul 16 2010

Fingerprint

Flow Pattern
two phase flow
Two-phase Flow
Phase Space
flow distribution
Chaotic System
Time series
fluid mechanics
Fluid Mechanics
Systems Biology
Inclined
biology
Lyapunov Exponent
Complex Networks
Nonlinear Dynamics
oils
exponents
Tend
Distinct
Water

ASJC Scopus subject areas

  • Condensed Matter Physics
  • Statistical and Nonlinear Physics
  • Statistics and Probability

Cite this

Motif distributions in phase-space networks for characterizing experimental two-phase flow patterns with chaotic features. / Gao, Zhong Ke; Jin, Ning De; Wang, Wen Xu; Lai, Ying-Cheng.

In: Physical Review E - Statistical, Nonlinear, and Soft Matter Physics, Vol. 82, No. 1, 016210, 16.07.2010.

Research output: Contribution to journalArticle

@article{89809e9a656f4bc1a5c3ab1442265dfe,
title = "Motif distributions in phase-space networks for characterizing experimental two-phase flow patterns with chaotic features",
abstract = "The dynamics of two-phase flows have been a challenging problem in nonlinear dynamics and fluid mechanics. We propose a method to characterize and distinguish patterns from inclined water-oil flow experiments based on the concept of network motifs that have found great usage in network science and systems biology. In particular, we construct from measured time series phase-space complex networks and then calculate the distribution of a set of distinct network motifs. To gain insight, we first test the approach using time series from classical chaotic systems and find a universal feature: motif distributions from different chaotic systems are generally highly heterogeneous. Our main finding is that the distributions from experimental two-phase flows tend to be heterogeneous as well, suggesting the underlying chaotic nature of the flow patterns. Calculation of the maximal Lyapunov exponent provides further support for this. Motif distributions can thus be a feasible tool to understand the dynamics of realistic two-phase flow patterns.",
author = "Gao, {Zhong Ke} and Jin, {Ning De} and Wang, {Wen Xu} and Ying-Cheng Lai",
year = "2010",
month = "7",
day = "16",
doi = "10.1103/PhysRevE.82.016210",
language = "English (US)",
volume = "82",
journal = "Physical Review E - Statistical, Nonlinear, and Soft Matter Physics",
issn = "1539-3755",
publisher = "American Physical Society",
number = "1",

}

TY - JOUR

T1 - Motif distributions in phase-space networks for characterizing experimental two-phase flow patterns with chaotic features

AU - Gao, Zhong Ke

AU - Jin, Ning De

AU - Wang, Wen Xu

AU - Lai, Ying-Cheng

PY - 2010/7/16

Y1 - 2010/7/16

N2 - The dynamics of two-phase flows have been a challenging problem in nonlinear dynamics and fluid mechanics. We propose a method to characterize and distinguish patterns from inclined water-oil flow experiments based on the concept of network motifs that have found great usage in network science and systems biology. In particular, we construct from measured time series phase-space complex networks and then calculate the distribution of a set of distinct network motifs. To gain insight, we first test the approach using time series from classical chaotic systems and find a universal feature: motif distributions from different chaotic systems are generally highly heterogeneous. Our main finding is that the distributions from experimental two-phase flows tend to be heterogeneous as well, suggesting the underlying chaotic nature of the flow patterns. Calculation of the maximal Lyapunov exponent provides further support for this. Motif distributions can thus be a feasible tool to understand the dynamics of realistic two-phase flow patterns.

AB - The dynamics of two-phase flows have been a challenging problem in nonlinear dynamics and fluid mechanics. We propose a method to characterize and distinguish patterns from inclined water-oil flow experiments based on the concept of network motifs that have found great usage in network science and systems biology. In particular, we construct from measured time series phase-space complex networks and then calculate the distribution of a set of distinct network motifs. To gain insight, we first test the approach using time series from classical chaotic systems and find a universal feature: motif distributions from different chaotic systems are generally highly heterogeneous. Our main finding is that the distributions from experimental two-phase flows tend to be heterogeneous as well, suggesting the underlying chaotic nature of the flow patterns. Calculation of the maximal Lyapunov exponent provides further support for this. Motif distributions can thus be a feasible tool to understand the dynamics of realistic two-phase flow patterns.

UR - http://www.scopus.com/inward/record.url?scp=77954828264&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=77954828264&partnerID=8YFLogxK

U2 - 10.1103/PhysRevE.82.016210

DO - 10.1103/PhysRevE.82.016210

M3 - Article

VL - 82

JO - Physical Review E - Statistical, Nonlinear, and Soft Matter Physics

JF - Physical Review E - Statistical, Nonlinear, and Soft Matter Physics

SN - 1539-3755

IS - 1

M1 - 016210

ER -