Title Nonlinear analysis of gas-water/oil-water two-phase flow in complex networks / Zhong-Ke Gao, Ning-De Jin, Wen-Xu Wang

Published Heidelberg : Springer, 2014 ©2014

Table of Contents
1.	Introduction					1
	References					3
2.	Definition of Flow Patterns					7
2.1.	Vertical Upward Gas-Water Flow Patterns					7
2.2.	Horizontal Gas-Water Flow Patterns					8
2.3.	Inclined Oil-Water Flow Patterns					10
2.4.	Vertical Upward Oil-in-Water Flow Patterns					10
	References					11
3.	The Experimental Flow Loop Facility and Data Acquisition					13
3.1.	Vertical Gas-Water Two-Phase Flow Experiment					13
3.2.	Horizontal Gas-Water Two-Phase Flow Experiment					15
3.3.	Inclined Oil-Water Two-Phase Flow Experiment					15
3.4.	Vertical Upward Oil-Water Two-Phase Flow Experiment					22
	References					23
4.	Community Detection in Flow Pattern Complex Network					25
4.1.	Community Detection in Gas-Water Flow Pattern Complex Network					25
4.1.1.	Flow Pattern Complex Network					25
4.1.2.	Flow Pattern Identification in Flow Pattern Complex Network					28
4.2.	Community Detection in Oil-Water Flow Pattern Complex Network					31
	References					33
5.	Nonlinear Dynamics in Fluid Dynamic Complex Network					35
5.1.	Gas-Water Fluid Dynamic Complex Network					35
5.1.1.	Construction of Fluid Dynamic Complex Network					35
5.1.2.	Network Degree Distribution and Its Physical Implications					37
5.1.3.	Network Information Entropy					41
5.1.4.	Nonlinear Dynamics of Gas-Water Two-Phase Flow in FDCN					42
5.2.	Oil-Water Fluid Dynamic Complex Network					43
	References					46
6.	Gas-Water Fluid Structure Complex Network					47
6.1.	Phase-Space Complex Network from Time Series					47
6.2.	Fluid Structure of Gas-Water Flow in Fluid Structure Complex Network					60
	References					61
7.	Oil-Water Fluid Structure Complex Network					63
	References					70
8.	Directed Weighted Complex Network for Characterizing Gas-Liquid Slug Flow					73
8.1.	Methodology					73
8.2.	Characterizing Chaotic Dynamic Behavior in Slug Flow					77
	References					83
9.	Markov Transition Probability-Based Network for Characterizing Horizontal Gas-Liquid Two-Phase Flow					85
9.1.	Methodology					85
9.2.	Dynamical Characterization of Horizontal Gas-Liquid Flow Patterns					89
	References					92
10.	Recurrence Network for Characterizing Bubbly Oil-in-Water Flows					95
10.1.	Recurrence Network Analysis of Time Series from Dynamic System					95
10.2.	Dynamic Characterization of Flow Patterns					99
	References					102
11.	Conclusions					103

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Description 1 online resource Series SpringerBriefs in applied sciences and technology, Multiphase flow SpringerBriefs in applied sciences and technology. Multiphase flow Contents Definition of flow patterns -- The experimental flow loop facility and data acquisition -- Community detection in flow pattern complex network -- Nonlinear dynamics in fluid dynamic complex network -- Gas-water fluid structure complex network -- Oil-water fluid structure complex network -- Directed weighted complex network for characterizing gas-liquid slug flow -- Markov transition probability-based network for characterizing horizontal gas-liquid two-phase flow -- Recurrence network for characterizing bubbly oil-in-water flows -- Conclusions Summary 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 FDCNs 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 Bibliography Includes bibliographical references Notes Print version record Subject Multiphase flow -- Mathematical models TECHNOLOGY & ENGINEERING -- Hydraulics. Ingénierie. Multiphase flow -- Mathematical models Form Electronic book Author Jin, Ning-De, author Wang, Wen-Xu, author ISBN 9783642383731 3642383734

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