IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v518y2019icp131-144.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437118314900
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2018.11.053?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zhang, Ningning & Lin, Aijing & Ma, Hui & Shang, Pengjian & Yang, Pengbo, 2018. "Weighted multivariate composite multiscale sample entropy analysis for the complexity of nonlinear times series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 508(C), pages 595-607.
    2. Liu, Hongzhi & Zhang, Xie & Hu, Huaqing & Zhang, Xingchen, 2022. "Exploring the impact of flow values on multiscale complexity quantification of airport flight flow fluctuations," Chaos, Solitons & Fractals, Elsevier, vol. 165(P1).
    3. Liu, Hongzhi & Zhang, Xingchen & Zhang, Xie, 2020. "Multiscale complexity analysis on airport air traffic flow volume time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 548(C).
    4. Chen, Shijian & Shang, Pengjian & Wu, Yue, 2019. "Multivariate multiscale fractional order weighted permutation entropy of nonlinear time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 515(C), pages 217-231.
    5. Zhou, Qin & Shang, Pengjian, 2020. "Weighted multiscale cumulative residual Rényi permutation entropy of financial time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 540(C).
    6. Sun, Jie & Zhao, Xiaojun & Xu, Chao, 2021. "Crude oil market autocorrelation: Evidence from multiscale quantile regression analysis," Energy Economics, Elsevier, vol. 98(C).
    7. Huang, Shupei & An, Haizhong & Gao, Xiangyun & Huang, Xuan, 2015. "Identifying the multiscale impacts of crude oil price shocks on the stock market in China at the sector level," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 434(C), pages 13-24.
    8. Azami, Hamed & Escudero, Javier, 2017. "Refined composite multivariate generalized multiscale fuzzy entropy: A tool for complexity analysis of multichannel signals," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 465(C), pages 261-276.
    9. Mukherjee, Sayan & Banerjee, Santo & Rondoni, Lamberto, 2018. "Dispersive graded entropy on computing dynamical complexity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 508(C), pages 131-140.
    10. Rong, Lei & Shang, Pengjian, 2018. "New irreversibility measure and complexity analysis based on singular value decomposition," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 913-924.
    11. Zhang, Yali & Wang, Jun, 2019. "Linkage influence of energy market on financial market by multiscale complexity synchronization," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 516(C), pages 254-266.
    12. 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.
    13. 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.
    14. Zhang, Yongping & Shang, Pengjian & Xiong, Hui, 2019. "Multivariate generalized information entropy of financial time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 1212-1223.
    15. Wang, Gangjin & Wei, Daijun & Li, Xiangbo & Wang, Ningkui, 2023. "A novel method for local anomaly detection of time series based on multi entropy fusion," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 615(C).
    16. Shupei Huang & Haizhong An & Xiangyun Gao & Meihui Jiang, 2016. "The Multiscale Fluctuations of the Correlation between Oil Price and Wind Energy Stock," Sustainability, MDPI, vol. 8(6), pages 1-14, June.
    17. 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).
    18. Chengzhao, Zhang & Heping, Pan & Yu, Ma & Xun, Huang, 2019. "Analysis of Asia Pacific stock markets with a novel multiscale model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 534(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:518:y:2019:i:c:p:131-144. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.