000 04117nam a22005415i 4500
001 978-3-642-38373-1
003 DE-He213
005 20140220082517.0
007 cr nn 008mamaa
008 131226s2014 gw | s |||| 0|eng d
020 _a9783642383731
_9978-3-642-38373-1
024 7 _a10.1007/978-3-642-38373-1
_2doi
050 4 _aTA357-359
072 7 _aTGMF
_2bicssc
072 7 _aTGMF1
_2bicssc
072 7 _aTEC009070
_2bisacsh
072 7 _aSCI085000
_2bisacsh
082 0 4 _a620.1064
_223
100 1 _aGao, Zhong-Ke.
_eauthor.
245 1 0 _aNonlinear Analysis of Gas-Water/Oil-Water Two-Phase Flow in Complex Networks
_h[electronic resource] /
_cby Zhong-Ke Gao, Ning-De Jin, Wen-Xu Wang.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg :
_bImprint: Springer,
_c2014.
300 _aXIII, 103 p. 73 illus., 41 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSpringerBriefs in Applied Sciences and Technology,
_x2191-530X
505 0 _aIntroduction -- 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. .
520 _aUnderstanding 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.
650 0 _aEngineering.
650 0 _aChemical engineering.
650 0 _aHydraulic engineering.
650 1 4 _aEngineering.
650 2 4 _aEngineering Fluid Dynamics.
650 2 4 _aIndustrial Chemistry/Chemical Engineering.
650 2 4 _aPhase Transitions and Multiphase Systems.
650 2 4 _aMeasurement Science and Instrumentation.
650 2 4 _aSoft and Granular Matter, Complex Fluids and Microfluidics.
700 1 _aJin, Ning-De.
_eauthor.
700 1 _aWang, Wen-Xu.
_eauthor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783642383724
830 0 _aSpringerBriefs in Applied Sciences and Technology,
_x2191-530X
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-642-38373-1
912 _aZDB-2-ENG
999 _c93237
_d93237