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An investigation of oil–water two-phase flow instability using multivariate multi-scale weighted permutation entropy

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  • Han, Yun-Feng
  • Jin, Ning-De
  • Zhai, Lu-Sheng
  • Ren, Ying-Yu
  • He, Yuan-Sheng

Abstract

The present study is devoted to the investigation of oil–water two-phase flow instability using multivariate multi-scale weighted permutation entropy (MWMPE). Four typical multivariate multi-scale entropies, namely MWMPE, multivariate multi-scale permutation entropy (MMPE), multivariate multi-scale sample entropy (MMSE) and multivariate multi-scale approximate entropy (MMAE) are applied in Lorenz system with the addition of different kinds of noises. The comparison results indicate that MWMPE presents the superiorities of being sensitive to the variation in scale, showing a monotonous increasing trend as well as the best anti-noise ability. Accordingly, with the fluctuating signals from an eight-electrode rotating electrical field conductance sensor used for the measurement of oil–water flows, we extract MWMPE for the whole experimental flow conditions, in terms of which the effects of mixture velocity and water-cut on oil–water two-phase flow instability are illuminated. Our research provides an effective method for uncovering the underlying evolution instability of the flow structures in oil–water flows.

Suggested Citation

  • Han, Yun-Feng & Jin, Ning-De & Zhai, Lu-Sheng & Ren, Ying-Yu & He, Yuan-Sheng, 2019. "An investigation of oil–water two-phase flow instability using multivariate multi-scale weighted permutation entropy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 518(C), pages 131-144.
    • Handle: RePEc:eee:phsmap:v:518:y:2019:i:c:p:131-144
    DOI: 10.1016/j.physa.2018.11.053
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    References listed on IDEAS

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    1. Yin, Yi & Shang, Pengjian, 2016. "Weighted permutation entropy based on different symbolic approaches for financial time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 443(C), pages 137-148.
    2. Chen, Xin & Jin, Ning-De & Zhao, An & Gao, Zhong-Ke & Zhai, Lu-Sheng & Sun, Bin, 2015. "The experimental signals analysis for bubbly oil-in-water flow using multi-scale weighted-permutation entropy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 417(C), pages 230-244.
    3. Niu, Hongli & Wang, Jun & Liu, Cheng, 2018. "Analysis of crude oil markets with improved multiscale weighted permutation entropy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 494(C), pages 389-402.
    4. Gao, Zhong-Ke & Ding, Mei-Shuang & Geng, He & Jin, Ning-De, 2015. "Multivariate multiscale entropy analysis of horizontal oil–water two-phase flow," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 417(C), pages 7-17.
    5. Chen, Shijian & Shang, Pengjian & Wu, Yue, 2018. "Weighted multiscale Rényi permutation entropy of nonlinear time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 496(C), pages 548-570.
    6. Zhang, Yongping & Shang, Pengjian, 2018. "Refined composite multiscale weighted-permutation entropy of financial time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 496(C), pages 189-199.
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