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Large Eddy Simulation of Scalar Transport in a Turbulent Jet Flow

  • S. C. Garrick
  • F. A. Jaberi
  • P. Givi
Part of the Fluid Mechanics and its Applications book series (FMIA, volume 54)

Abstract

Large eddy simulation (LES) of turbulent reacting flows has been the subject of widespread investigation (McMurtry et al., 1992: Galperin and Orszag, 1993; Menon et al., 1993; McMurtry et al., 1993; Gao and O’Brien, 1993; Madnia and Givi, 1993; Frankel et al., 1993; Cook and Riley. 1994; Givi, 1994; Fureby and Lofstrom, 1994; Möller et al., 1996: Branley and Jones, 1997; Cook et al., 1997; Jiménez et al., 1997: Mathey and Choilet, 1997; Colucci et al., 1998; DesJardin and Frankel, 1998: Jaberi and James. 1998; Réveillon and Vervisch, 1998; Vervisch and Poinsot, 1988). Amongst these, recently Colucci et al. (1998) developed a methodology, termed the “filtered density function” (FDF). The fundamental property of the FDF is to account for the effects of subgrid scale (SGS) scalar fluctuations in a probabilistic manner. This is similar to probability density function (PDF) methods which have proven to be very useful in Reynolds averaging procedures (Libby and Williams, 1980; Libby and Williams. 1994: O’Brien. 1980; Pope, 1985; Dopazo, 1994). Colucci et al. (1998) developed a transport equation for the FDF in constant density flows in which the effects of unresolved convection and subgrid mixing are modeled similarly to those in “conventional” LES, and Reynolds averaging procedures. This transport equation was solved numerically by a Lagrangian Monte Carlo procedure and the results were compared with those obtained by direct numerical simulation (DNS) and by a conventional finite difference LES in which the effects of SGS scalar fluctuations are ignored (LES-FD).

Keywords

Probability Density Function Large Eddy Simulation Direct Numerical Simulation Turbulent Combustion Direct Numerical Simulation Data 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media Dordrecht 1999

Authors and Affiliations

  • S. C. Garrick
    • 1
  • F. A. Jaberi
    • 2
  • P. Givi
    • 2
  1. 1.Department of Mechanical EngineeringUniversity of MinnesotaMinneapolisUSA
  2. 2.Department of Mechanical & Aerospace EngineeringState University of New York — BuffaloBuffaloUSA

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