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Characterization of flow pattern transitions for horizontal liquid–liquid pipe flows by using multi-scale distribution entropy in coupled 3D phase space

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  • Zhai, Lu-Sheng
  • Zong, Yan-Bo
  • Wang, Hong-Mei
  • Yan, Cong
  • Gao, Zhong-Ke
  • Jin, Ning-De

Abstract

Horizontal oil–water two-phase flows often exist in many industrial processes. Uncovering the dynamic mechanism of the flow pattern transition is of great significance for modeling the flow parameters. In this study we propose a method called multi-scale distribution entropy (MSDE) in a coupled 3D phase space, and use it to characterize the flow pattern transitions in horizontal oil–water two-phase flows. Firstly, the proposed MSDE is validated with Lorenz system and ARFIMA processes. Interestingly, it is found that the MSDE is dramatically associated with the cross-correlations of the coupled time series. Then, through conducting the experiment of horizontal oil–water two-phase flows, the upstream and downstream flow information is collected using a conductance cross-correlation velocity probe. The coupled cross-correlated signals are investigated using the MSDE method, and the results indicate that the MSDE is an effective tool uncovering the complex dynamic behaviors of flow pattern transitions.

Suggested Citation

  • Zhai, Lu-Sheng & Zong, Yan-Bo & Wang, Hong-Mei & Yan, Cong & Gao, Zhong-Ke & Jin, Ning-De, 2017. "Characterization of flow pattern transitions for horizontal liquid–liquid pipe flows by using multi-scale distribution entropy in coupled 3D phase space," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 469(C), pages 136-147.
    • Handle: RePEc:eee:phsmap:v:469:y:2017:i:c:p:136-147
    DOI: 10.1016/j.physa.2016.11.020
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    Cited by:

    1. Charakopoulos, A.K. & Katsouli, G.A. & Karakasidis, T.E., 2018. "Dynamics and causalities of atmospheric and oceanic data identified by complex networks and Granger causality analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 495(C), pages 436-453.
    2. Zhai, Lusheng & Wu, Yinglin & Yang, Jie & Xie, Hailin, 2020. "Characterizing initiation of gas–liquid churn flows using coupling analysis of multivariate time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 540(C).

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