000 04110Cam#a22005535i#4500
001 INGC-EBK-000628
003 AR-LpUFI
005 20220927110012.0
007 cr nn 008mamaa
008 131226s2014 gw | s |||| 0|eng d
020 _a9783642383731
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
100 1 _aGao, Zhong-Ke.
_9261605
245 1 0 _aNonlinear Analysis of Gas-Water/Oil-Water Two-Phase Flow in Complex Networks
_h[libro electrónico] /
_cby Zhong-Ke Gao, Ning-De Jin, Wen-Xu Wang.
260 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg :
_bImprint: Springer,
_c2014.
300 _axiii, 103 p. :
_bil.
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 FDCNâ_Ts 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 _aChemical engineering.
_9259863
650 0 _aAmorphous substances.
_9259899
650 0 _aComplex fluids.
_9259900
650 0 _aPhase transitions (Statistical physics).
_9261005
650 0 _aPhysical measurements.
_9260755
650 0 _aMeasurement.
_9260756
650 0 _aFluid mechanics.
_9259906
650 1 4 _aEngineering.
_9259622
650 2 4 _aIndustrial Chemistry
_9259864
650 2 4 _aPhase Transitions and Multiphase Systems.
_9261006
650 2 4 _aMeasurement Science and Instrumentation.
_9260757
650 2 4 _aSoft and Granular Matter, Complex Fluids and Microfluidics.
_9259901
700 1 _aJin, Ning-De.
_9261606
700 1 _aWang, Wen-Xu.
_9261607
776 0 8 _iPrinted edition:
_z9783642383724
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-642-38373-1
912 _aZDB-2-ENG
929 _aCOM
942 _cEBK
999 _aSKV
_c28056
_d28056