Nonlinear Analysis of Gas-Water/Oil-Water Two-Phase Flow in Complex Networks

  • Zhong-Ke Gao
  • Ning-De Jin
  • Wen-Xu Wang
Part of the SpringerBriefs in Applied Sciences and Technology book series (BRIEFSAPPLSCIENCES)

Table of contents

  1. Front Matter
    Pages i-xiii
  2. Zhong-Ke Gao, Ning-De Jin, Wen-Xu Wang
    Pages 1-6
  3. Zhong-Ke Gao, Ning-De Jin, Wen-Xu Wang
    Pages 7-11
  4. Zhong-Ke Gao, Ning-De Jin, Wen-Xu Wang
    Pages 13-23
  5. Zhong-Ke Gao, Ning-De Jin, Wen-Xu Wang
    Pages 25-34
  6. Zhong-Ke Gao, Ning-De Jin, Wen-Xu Wang
    Pages 35-46
  7. Zhong-Ke Gao, Ning-De Jin, Wen-Xu Wang
    Pages 47-62
  8. Zhong-Ke Gao, Ning-De Jin, Wen-Xu Wang
    Pages 63-71
  9. Zhong-Ke Gao, Ning-De Jin, Wen-Xu Wang
    Pages 73-83
  10. Zhong-Ke Gao, Ning-De Jin, Wen-Xu Wang
    Pages 95-102
  11. Zhong-Ke Gao, Ning-De Jin, Wen-Xu Wang
    Pages 103-103

About this book

Introduction

Understanding the dynamics of multi-phase flows has been a challenge in the fields of nonlinear dynamics and fluid mechanics. This chapter reviews our work on two-phase flow dynamics in combination with complex network theory. We systematically carried out gas-water/oil-water two-phase flow experiments for measuring the time series of flow signals which is studied in terms of the mapping from time series to complex networks. Three network mapping methods were proposed for the analysis and identification of flow patterns, i.e. Flow Pattern Complex Network (FPCN), Fluid Dynamic Complex Network (FDCN) and Fluid Structure Complex Network (FSCN). Through detecting the community structure of FPCN based on K-means clustering, distinct flow patterns can be successfully distinguished and identified. A number of FDCN’s under different flow conditions were constructed in order to reveal the dynamical characteristics of two-phase flows. The FDCNs exhibit universal power-law degree distributions. The power-law exponent and the network information entropy are sensitive to the transition among different flow patterns, which can be used to characterize nonlinear dynamics of the two-phase flow. FSCNs were constructed in the phase space through a general approach that we introduced. The statistical properties of FSCN can provide quantitative insight into the fluid structure of two-phase flow. These interesting and significant findings suggest that complex networks can be a potentially powerful tool for uncovering the nonlinear dynamics of two-phase flows.

Keywords

Complex Networks Multi-phase Flow Nonlinear Dynamics Sensors Integration Time Series Analysis

Authors and affiliations

  • Zhong-Ke Gao
    • 1
  • Ning-De Jin
    • 2
  • Wen-Xu Wang
    • 3
  1. 1.School of Electrical Engineering and AutTianjin UniversityTianjinPeople's Republic of China
  2. 2.Tianjin University School of Electrical Engineering and AutTianjinPeople's Republic of China
  3. 3.School of Electrical, Computer and Energy EngineeringArizona State UniversityTempeUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-642-38373-1
  • Copyright Information The Author(s) 2014
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Engineering
  • Print ISBN 978-3-642-38372-4
  • Online ISBN 978-3-642-38373-1
  • Series Print ISSN 2191-530X
  • Series Online ISSN 2191-5318
  • About this book