Logo - springer
Slogan - springer

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

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

Gao, Zhong-Ke, Jin, Ning-De, Wang, Wen-Xu

2014, XIII, 103 p. 73 illus., 41 illus. in color.

Available Formats:

Springer eBooks may be purchased by end-customers only and are sold without copy protection (DRM free). Instead, all eBooks include personalized watermarks. This means you can read the Springer eBooks across numerous devices such as Laptops, eReaders, and tablets.

You can pay for Springer eBooks with Visa, Mastercard, American Express or Paypal.

After the purchase you can directly download the eBook file or read it online in our Springer eBook Reader. Furthermore your eBook will be stored in your MySpringer account. So you can always re-download your eBooks.


(net) price for USA

ISBN 978-3-642-38373-1

digitally watermarked, no DRM

Included Format: PDF and EPUB

download immediately after purchase

learn more about Springer eBooks

add to marked items


Softcover (also known as softback) version.

You can pay for Springer Books with Visa, Mastercard, American Express or Paypal.

Standard shipping is free of charge for individual customers.


(net) price for USA

ISBN 978-3-642-38372-4

free shipping for individuals worldwide

usually dispatched within 3 to 5 business days

add to marked items

  • 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
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.

Content Level » Professional/practitioner

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

Related subjects » Applied & Technical Physics - Condensed Matter Physics - Industrial Chemistry and Chemical Engineering - Materials - Mechanics

Table of contents / Sample pages 

Popular Content within this publication 



Read this Book on Springerlink

Services for this book

New Book Alert

Get alerted on new Springer publications in the subject area of Engineering Fluid Dynamics.