IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v629y2023ics0378437123007458.html
   My bibliography  Save this article

Laminar to turbulent transition in terms of information theory

Author

Listed:
  • Bahamonde, Adolfo D.
  • Cornejo, Pablo
  • Sepúlveda, Héctor H.

Abstract

In the present study we investigate turbulence using information theory. We propose a methodology to study mutual information, entropy and complexity utilizing data from direct numerical simulations. These quantities are defined in their continuous form and estimated using the Kozachenko–Leonenko and Kraskov–Stögbauer–Grassberger estimators, for entropy and mutual information, respectively. To validate our tools and methodology, we compare the entropy of three direct numerical simulations of forced isotropic turbulence at different Reynolds numbers (Reλ=433, Reλ=648 y Reλ=1300) with results previously reported in the literature. The main body of the study is the analysis of the transition to turbulence in a transitional boundary layer, our results show that the mutual information between the velocity field and temporal velocity increments reaches a maximum close to the transition to turbulence followed by a decrease during the transition, while at the same time there is a change in entropy due to both, the change in variance of the velocity field and the deformation of the probability density function. Finally, we examine the vertical profiles of the information-theoretic quantities in a turbulent channel flow (Reτ=5200). Here we study spatial velocity increments and observe an increase of the entropy due to the deformation of the probability density function of the velocity field, as it becomes more Gaussian away from the wall.

Suggested Citation

  • Bahamonde, Adolfo D. & Cornejo, Pablo & Sepúlveda, Héctor H., 2023. "Laminar to turbulent transition in terms of information theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 629(C).
    • Handle: RePEc:eee:phsmap:v:629:y:2023:i:c:s0378437123007458
    DOI: 10.1016/j.physa.2023.129190
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437123007458
    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.2023.129190?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. Tao, Xingtian & Wu, Huixuan, 2019. "Quantification of the complexity and unpredictability of a turbulent cylinder wake using excess entropy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 211-221.
    2. Bialek, William & Nemenman, Ilya & Tishby, Naftali, 2001. "Complexity through nonextensivity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 302(1), pages 89-99.
    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. Dingle, Kamaludin & Kamal, Rafiq & Hamzi, Boumediene, 2023. "A note on a priori forecasting and simplicity bias in time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 609(C).
    2. Łukasz Dębowski, 2014. "On Hidden Markov Processes with Infinite Excess Entropy," Journal of Theoretical Probability, Springer, vol. 27(2), pages 539-551, June.
    3. Ghosh, Abhik, 2023. "Optimal guessing under nonextensive framework and associated moment bounds," Statistics & Probability Letters, Elsevier, vol. 197(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:629:y:2023:i:c:s0378437123007458. 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.