IDEAS home Printed from https://ideas.repec.org/a/bpj/mcmeap/v28y2022i3p211-219n5.html
   My bibliography  Save this article

Sensitivity analysis of the concentration transport estimation in a turbulent flow

Author

Listed:
  • Kolyukhin Dmitriy

    (Trofimuk Institute of Petroleum Geology and Geophysics SB RAS, Koptug ave. 3, 630090, Novosibirsk, Russia)

  • Sabelfeld Karl K.

    (Institute of Computational Mathematics and Mathematical Geophysics, Lavrentiev Prosp. 6, 630090, Novosibirsk, Russia)

  • Dimov Ivan

    (Institute of Information and Communication Technologies, Bulgarian Academy of Sciences, 1113, Sofia, Bulgaria)

Abstract

The present study addresses the sensitivity analysis of particle concentration dispersion in the turbulent flow. A stochastic spectral model of turbulence is used to simulate the particle transfer. Sensitivity analysis is performed by estimations of Morris and Sobol indices. This study allows to define the significant and nonsignificant model parameters. It also gives an idea of the qualitative behavior of the stochastic model used.

Suggested Citation

  • Kolyukhin Dmitriy & Sabelfeld Karl K. & Dimov Ivan, 2022. "Sensitivity analysis of the concentration transport estimation in a turbulent flow," Monte Carlo Methods and Applications, De Gruyter, vol. 28(3), pages 211-219, September.
  • Handle: RePEc:bpj:mcmeap:v:28:y:2022:i:3:p:211-219:n:5
    DOI: 10.1515/mcma-2022-2116
    as

    Download full text from publisher

    File URL: https://doi.org/10.1515/mcma-2022-2116
    Download Restriction: For access to full text, subscription to the journal or payment for the individual article is required.

    File URL: https://libkey.io/10.1515/mcma-2022-2116?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.

    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:bpj:mcmeap:v:28:y:2022:i:3:p:211-219:n:5. 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: Peter Golla (email available below). General contact details of provider: https://www.degruyter.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.