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

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  • © 2014

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Authors:
  1. Zhong-Ke Gao

    1. School of Electrical Engineering and Aut, Tianjin University, Tianjin, People's Republic of China

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  2. Ning-De Jin

    1. Tianjin University School of Electrical Engineering and Aut, Tianjin, People's Republic of China

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  3. Wen-Xu Wang

    1. School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, USA

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  • Complex network theory and its applications to multi-phase flow
  • Complex flow behavior in multi-phase flow
  • Details about complex networks from experimental time series signals are provided
  • Understanding the complex dynamics underlying multi-phase flow
  • Includes supplementary material: sn.pub/extras

Part of the book series: SpringerBriefs in Applied Sciences and Technology (BRIEFSAPPLSCIENCES)

Part of the book sub series: SpringerBriefs on Multiphase Flow (BRIEFSMUFLO)

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Table of contents (11 chapters)

  1. Front Matter

    Pages i-xiii
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  2. Introduction

    • Zhong-Ke Gao, Ning-De Jin, Wen-Xu Wang
    Pages 1-6

  3. Definition of Flow Patterns

    • Zhong-Ke Gao, Ning-De Jin, Wen-Xu Wang
    Pages 7-11

  4. The Experimental Flow Loop Facility and Data Acquisition

    • Zhong-Ke Gao, Ning-De Jin, Wen-Xu Wang
    Pages 13-23

  5. Community Detection in Flow Pattern Complex Network

    • Zhong-Ke Gao, Ning-De Jin, Wen-Xu Wang
    Pages 25-34

  6. Nonlinear Dynamics in Fluid Dynamic Complex Network

    • Zhong-Ke Gao, Ning-De Jin, Wen-Xu Wang
    Pages 35-46

  7. Gas-Water Fluid Structure Complex Network

    • Zhong-Ke Gao, Ning-De Jin, Wen-Xu Wang
    Pages 47-62

  8. Oil-Water Fluid Structure Complex Network

    • Zhong-Ke Gao, Ning-De Jin, Wen-Xu Wang
    Pages 63-71

  9. Directed Weighted Complex Network for Characterizing Gas-Liquid Slug Flow

    • Zhong-Ke Gao, Ning-De Jin, Wen-Xu Wang
    Pages 73-83

  10. Markov Transition Probability-Based Network for Characterizing Horizontal Gas-Liquid Two-Phase Flow

    • Zhong-Ke Gao, Ning-De Jin, Wen-Xu Wang
    Pages 85-93

  11. Recurrence Network for Characterizing Bubbly Oil-in-Water Flows

    • Zhong-Ke Gao, Ning-De Jin, Wen-Xu Wang
    Pages 95-102

  12. Conclusions

    • Zhong-Ke Gao, Ning-De Jin, Wen-Xu Wang
    Pages 103-103
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Keywords

About this book

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.

Authors and Affiliations

  • School of Electrical Engineering and Aut, Tianjin University, Tianjin, People's Republic of China

    Zhong-Ke Gao

  • Tianjin University School of Electrical Engineering and Aut, Tianjin, People's Republic of China

    Ning-De Jin

  • School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, USA

    Wen-Xu Wang

About the authors

Prof. Zhong-Ke Gao, Tianjin University, China

Prof. Dr. Ning-De JIN, Tianjin University, China

Prof. Wen-Xu Wang, Arizona State University, USA,

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