Uncovering dynamic behaviors underlying experimental oil–water two-phase flow based on dynamic segmentation algorithm

Characterizing complex dynamic behaviors arising from various inclined oil–water two-phase flow patterns is a challenging problem in the fields of nonlinear dynamics and fluid mechanics. We systematically carried out inclined oil–water two-phase flow experiments for measuring the time series conductance fluctuating signals of different flow patterns. We using the dynamic segmentation algorithm incorporating with phase space reconstruction analyze the measured experimental signals to uncover the dynamic behaviors underlying different flow patterns. Specifically, given a time series from a two-phase flow, we move a sliding pointer over the time series and for each position of the pointer we calculate the dynamic difference measure of the phase space orbits generated from the segment to the left and to the right of the pointer. A number of experimental signals under different flow conditions are investigated in order to reveal the dynamical characteristics of inclined oil–water flows. The results indicate that the heterogeneity of dynamic difference measure series is sensitive to the transition among different flow patterns and the standard deviation of dynamic difference measure series can yield quantitative insights into the nonlinear dynamics of the two-phase flow. These properties render the dynamic segmentation algorithm-based approach particularly useful for uncovering the dynamic behaviors of inclined oil–water two-phase flows.

[1]  N. Brauner,et al.  Multi-holdups in co-current stratified flow in inclined tubes , 2003 .

[2]  Zhong-Ke Gao,et al.  Scaling analysis of phase fluctuations in experimental three-phase flows , 2011 .

[3]  Zhong-Ke Gao,et al.  Nonlinear dynamic analysis of large diameter inclined oil–water two phase flow pattern , 2010 .

[4]  Michael Small,et al.  Superfamily phenomena and motifs of networks induced from time series , 2008, Proceedings of the National Academy of Sciences.

[5]  Boris Podobnik,et al.  Statistical tests for power-law cross-correlated processes. , 2011, Physical review. E, Statistical, nonlinear, and soft matter physics.

[6]  Wei-Xing Zhou,et al.  Statistical properties of visibility graph of energy dissipation rates in three-dimensional fully developed turbulence , 2009, 0905.1831.

[7]  James P. Crutchfield,et al.  Geometry from a Time Series , 1980 .

[8]  L. Durlofsky,et al.  Experimental study of two and three phase flows in large diameter inclined pipes , 2003 .

[9]  James P. Brill,et al.  CHARACTERIZATION OF OIL-WATER FLOW PATTERNS IN VERTICAL AND DEVIATED WELLS , 1999 .

[10]  Robert Savit,et al.  Stationarity and nonstationarity in time series analysis , 1996 .

[11]  M. Small,et al.  Node importance for dynamical process on networks: a multiscale characterization. , 2011, Chaos.

[12]  Oscar Mauricio Hernandez Rodriguez,et al.  Geometrical and kinematic properties of interfacial waves in stratified oil-water flow in inclined pipe , 2012 .

[13]  Zhong-Ke Gao,et al.  Characterization of chaotic dynamic behavior in the gas–liquid slug flow using directed weighted complex network analysis , 2012 .

[14]  Josua P. Meyer,et al.  Two-phase flow in inclined tubes with specific reference to condensation: A review , 2011 .

[15]  R. Savit,et al.  Time series and dependent variables , 1991 .

[16]  Jean-Pierre Hulin,et al.  Liquid‐liquid flows in an inclined pipe , 1988 .

[17]  J. Salas,et al.  Nonlinear dynamics, delay times, and embedding windows , 1999 .

[18]  Michael Small,et al.  Rhythmic Dynamics and Synchronization via Dimensionality Reduction: Application to Human Gait , 2010, PLoS Comput. Biol..

[19]  Grebogi,et al.  Self-organization and chaos in a fluidized bed. , 1995, Physical review letters.

[20]  Bo Hu,et al.  General dynamics of topology and traffic on weighted technological networks. , 2005, Physical review letters.

[21]  Zhongke Gao,et al.  Flow-pattern identification and nonlinear dynamics of gas-liquid two-phase flow in complex networks. , 2009, Physical review. E, Statistical, nonlinear, and soft matter physics.

[22]  H. Stanley,et al.  Scale invariance in the nonstationarity of human heart rate. , 2000, Physical review letters.

[23]  F. Lillo,et al.  Segmentation algorithm for non-stationary compound Poisson processes , 2010, 1001.2549.

[24]  L. T. Fan,et al.  Stochastic analysis of a three-phase fluidized bed; Fractal approach , 1990 .

[25]  Norbert Marwan,et al.  The geometry of chaotic dynamics — a complex network perspective , 2011, 1102.1853.

[26]  P. Das,et al.  The transition from water continuous to oil continuous flow pattern , 2006 .

[27]  Morten Christian Melaaen,et al.  Particle image velocimetry for characterizing the flow structure of oil–water flow in horizontal and slightly inclined pipes , 2010 .

[28]  Ying-Cheng Lai,et al.  Motif distributions in phase-space networks for characterizing experimental two-phase flow patterns with chaotic features. , 2010, Physical review. E, Statistical, nonlinear, and soft matter physics.

[29]  Y. Lai,et al.  Abnormal synchronization in complex clustered networks. , 2006, Physical review letters.

[30]  Wen-Xu Wang,et al.  Predicting catastrophes in nonlinear dynamical systems by compressive sensing. , 2011, Physical review letters.

[31]  Lucas Lacasa,et al.  Description of stochastic and chaotic series using visibility graphs. , 2010, Physical review. E, Statistical, nonlinear, and soft matter physics.

[32]  Bin Xu,et al.  Process flow diagram of an ammonia plant as a complex network , 2005 .

[33]  Ying-Cheng Lai,et al.  Information propagation on modular networks. , 2006, Physical review. E, Statistical, nonlinear, and soft matter physics.

[34]  Gary Lucas,et al.  Measurement of the homogeneous velocity of inclined oil-in-water flows using a resistance cross correlation flow meter , 2001 .

[35]  B. Luque,et al.  Horizontal visibility graphs: exact results for random time series. , 2009, Physical review. E, Statistical, nonlinear, and soft matter physics.

[36]  Zhong-Ke Gao,et al.  Nonlinear characterization of oil–gas–water three-phase flow in complex networks , 2011 .

[37]  Zhong-Ke Gao,et al.  Multi-scale cross entropy analysis for inclined oil–water two-phase countercurrent flow patterns , 2011 .

[38]  N. Jin,et al.  Design and geometry optimization of a conductivity probe with a vertical multiple electrode array for measuring volume fraction and axial velocity of two-phase flow , 2008 .

[39]  Lucas Lacasa,et al.  From time series to complex networks: The visibility graph , 2008, Proceedings of the National Academy of Sciences.

[40]  Wen-Jie Xie,et al.  Horizontal visibility graphs transformed from fractional Brownian motions: Topological properties versus the Hurst index , 2010, 1012.3850.

[41]  C. Peng,et al.  Mosaic organization of DNA nucleotides. , 1994, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[42]  J. Kurths,et al.  Analytical framework for recurrence network analysis of time series. , 2012, Physical review. E, Statistical, nonlinear, and soft matter physics.

[43]  H. Stanley,et al.  Detrended cross-correlation analysis: a new method for analyzing two nonstationary time series. , 2007, Physical review letters.

[44]  H. Abarbanel,et al.  Determining embedding dimension for phase-space reconstruction using a geometrical construction. , 1992, Physical review. A, Atomic, molecular, and optical physics.

[45]  M Small,et al.  Complex network from pseudoperiodic time series: topology versus dynamics. , 2006, Physical review letters.