On large eddy simulation of particle laden flow: taking advantage of spectral properties of interpolation schemes for modeling SGS effects

Conference paper
Part of the ERCOFTAC Series book series (ERCO, volume 15)


This contribution deals with Large-Eddy simulation (LES) of particle-laden flow. State of the art methods are capable to predict the dynamics of small particles in dilute suspensions as long as direct numerical simulation (DNS) is possible. However, as soon as the Reynolds number is too high, DNS is not an alternative and often LES is the method of choice. For LES of particle-laden flow, the effect of the unresolved subgrid scales (SGS) needs to be modeled, a least for small or moderate Stokes numbers of the particles. This means that two turbulence models are necessary. One model for SGS effects on the resolved scales of the carrier fluid flow (such as the Smagorinsky model) and another model for SGS effects on the particle dynamics. Hereinafter, the former type of models is refered to as fluid-LES models and the latter type as particle-LES models.


Direct Numerical Simulation Interpolation Scheme Isotropic Turbulence Smagorinsky Model Target Spectrum 
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  1. 1.
    N. A. Adams, S. Hickel, and S. Franz. Implicit subgrid-scale modeling by adaptive deconvolution. J. Comput. Phys., 200(2):412–431, November 2004. MathSciNetMATHCrossRefGoogle Scholar
  2. 2.
    R. Clift, J. R. Grace, and M. E. Weber. Bubbles, Drops and Particles. Academic Press, New York, 1978. Google Scholar
  3. 3.
    C. Gobert and M. Manhart. Subgrid modelling for particle-les by spectrally optimised interpolation (SOI). submitted to J. Comput. Phys., 2010. Google Scholar
  4. 4.
    E. Hairer and G. Wanner. Solving Ordinary Differential Equations II. Stiff and Differential-Algebraic Problems. Springer Series in Computational Mathematics. Springer, New York, 1990. Google Scholar
  5. 5.
    J. G. M. Kuerten. Subgrid modeling in particle-laden channel flow. Phys. Fluids, 18:025108, 2006. CrossRefGoogle Scholar
  6. 6.
    M. Manhart. A zonal grid algorithm for DNS of turbulent boundary layers. Comput. Fluids, 33(3):435–461, 2004. MATHCrossRefGoogle Scholar
  7. 7.
    C. Meneveau, T. S. Lund, and W. H. Cabot. A Lagrangian dynamic subgrid-scale model of turbulence. J. Fluid Mech., 319:353–385, 1996. MATHCrossRefGoogle Scholar
  8. 8.
    S. B. Pope. Turbulent Flows. Cambridge University Press, Cambridge, UK, 2000. MATHGoogle Scholar
  9. 9.
    B. Shotorban, K. Zhang, and F. Mashayek. Improvement of particle concentration prediction in large-eddy simulation by defiltering. Int. J. Heat Mass Transfer, 50(19-20):3728–3739, September 2007. MATHCrossRefGoogle Scholar
  10. 10.
    S. Stolz and N. A. Adams. An approximate deconvolution procedure for large-eddy simulation. Phys. Fluids, 11(7):1699–1701, 1999. MATHCrossRefGoogle Scholar
  11. 11.
    H. L. Stone. Iterative solution of implicit approximations of multidimensional partial differential equations. SIAM J. Num. Anal., 5(3):530–558, 1968. MATHCrossRefGoogle Scholar
  12. 12.
    N. P. Sullivan, S. Mahalingam, and R. M. Kerr. Deterministic forcing of homogeneous, isotropic turbulence. Phys. Fluids, 6(4):1612–1614, 1994. CrossRefGoogle Scholar
  13. 13.
    J. H. Williamson. Low-storage Runge-Kutta schemes. J. Comput. Phys., 35:48–56, 1980. MathSciNetMATHCrossRefGoogle Scholar

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© Springer Science+Business Media B.V. 2011

Authors and Affiliations

  1. 1.Technische Universität MünchenMünchenGermany

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