IDEAS home Printed from https://ideas.repec.org/a/nat/nature/v409y2001i6823d10.1038_35059027.html
   My bibliography  Save this article

Fluid particle accelerations in fully developed turbulence

Author

Listed:
  • A. La Porta

    (Laboratory of Atomic and Solid State Physics, Laboratory of Nuclear Studies, Cornell University, Ithaca)

  • Greg A. Voth

    (Laboratory of Atomic and Solid State Physics, Laboratory of Nuclear Studies, Cornell University, Ithaca)

  • Alice M. Crawford

    (Laboratory of Atomic and Solid State Physics, Laboratory of Nuclear Studies, Cornell University, Ithaca)

  • Jim Alexander

    (Laboratory of Atomic and Solid State Physics, Laboratory of Nuclear Studies, Cornell University, Ithaca)

  • Eberhard Bodenschatz

    (Laboratory of Atomic and Solid State Physics, Laboratory of Nuclear Studies, Cornell University, Ithaca)

Abstract

The motion of fluid particles as they are pushed along erratic trajectories by fluctuating pressure gradients is fundamental to transport and mixing in turbulence. It is essential in cloud formation and atmospheric transport1,2, processes in stirred chemical reactors and combustion systems3, and in the industrial production of nanoparticles4. The concept of particle trajectories has been used successfully to describe mixing and transport in turbulence3,5, but issues of fundamental importance remain unresolved. One such issue is the Heisenberg–Yaglom prediction of fluid particle accelerations6,7, based on the 1941 scaling theory of Kolmogorov8,9. Here we report acceleration measurements using a detector adapted from high-energy physics to track particles in a laboratory water flow at Reynolds numbers up to 63,000. We find that, within experimental errors, Kolmogorov scaling of the acceleration variance is attained at high Reynolds numbers. Our data indicate that the acceleration is an extremely intermittent variable—particles are observed with accelerations of up to 1,500 times the acceleration of gravity (equivalent to 40 times the root mean square acceleration). We find that the acceleration data reflect the anisotropy of the large-scale flow at all Reynolds numbers studied.

Suggested Citation

  • A. La Porta & Greg A. Voth & Alice M. Crawford & Jim Alexander & Eberhard Bodenschatz, 2001. "Fluid particle accelerations in fully developed turbulence," Nature, Nature, vol. 409(6823), pages 1017-1019, February.
    • Handle: RePEc:nat:nature:v:409:y:2001:i:6823:d:10.1038_35059027
    DOI: 10.1038/35059027
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/35059027
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1038/35059027?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.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Carvalho, Jonas C. & Rizza, Umberto & Lovato, Rodrigo & Degrazia, Gervásio A. & Filho, Edson P.M. & Campos, Cláudia R.J., 2009. "Estimation of the Kolmogorov constant by large-eddy simulation in the stable PBL," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(8), pages 1500-1508.
    2. Na, Ji Sung & Koo, Eunmo & Ko, Seung Chul & Linn, Rodman & Muñoz-Esparza, Domingo & Jin, Emilia Kyung & Lee, Joon Sang, 2019. "Stochastic characteristics for the vortical structure of a 5-MW wind turbine wake," Renewable Energy, Elsevier, vol. 133(C), pages 1220-1230.
    3. Simone Ferrari & Riccardo Rossi & Annalisa Di Bernardino, 2022. "A Review of Laboratory and Numerical Techniques to Simulate Turbulent Flows," Energies, MDPI, vol. 15(20), pages 1-56, October.
    4. Chen, W., 2006. "Time–space fabric underlying anomalous diffusion," Chaos, Solitons & Fractals, Elsevier, vol. 28(4), pages 923-929.
    5. Huang, Diangui, 2019. "A new turbulence analysis method based on the mean speed and mean free path theory of the molecule thermal motion," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 66-74.
    6. Sun, HongGuang & Hao, Xiaoxiao & Zhang, Yong & Baleanu, Dumitru, 2017. "Relaxation and diffusion models with non-singular kernels," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 468(C), pages 590-596.

    More about this item

    Statistics

    Access and download statistics

    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:nat:nature:v:409:y:2001:i:6823:d:10.1038_35059027. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    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.