Scale-Resolving Simulations of Wall-Bounded Flows with an Unstructured Compressible Flow Solver

  • Axel ProbstEmail author
  • Silvia Reuß
Conference paper
Part of the Notes on Numerical Fluid Mechanics and Multidisciplinary Design book series (NNFM, volume 130)


The fully-developed channel flow at \(Re_\tau \approx 395\) is used to validate scale-resolving simulations with the unstructured compressible DLR-TAU code. In a sensitivity study based on wall-resolved LES a low-dissipative spatial scheme is derived, which allows to predict the channel flow in fair agreement with DNS. Then the scheme is used in Improved Delayed DES computations in order to assess TAU’s capabilities for wall-modelled LES. As pointed out in a grid study, a tangential resolution of about \(\varDelta x^+ \approx 40\), \(\varDelta z^+ \approx 20\) is required to obtain acceptable mean-flow results. Besides, the combination of IDDES with a vorticity-dependent subgrid filter width is shown to yield consistent results, and the effect of the underlying RANS approach up to Reynolds-stress modelling is analysed.


Plane Channel Flow Compressible Flux Compressible Flow Solver Convective Time Unit Basic Numerical Study 
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.


  1. 1.
    Blazek, J.: Computational Fluid Dynamics: Principles and Applications, 2nd edn. Elsevier, Amsterdam (2005)Google Scholar
  2. 2.
    Deck, S.: Recent improvements in the zonal detached eddy simulation (ZDES) formulation. Theoret. Comput. Fluid Dyn. 26(6), 523–550 (2012)CrossRefGoogle Scholar
  3. 3.
    Ducros, F., Nicoud, F., Poinsot, T.: Wall-adapting local eddy-viscosity models for simulations in complex geometries. In: Proceedings Conference on Numerical Methods Fluid Dynamics, Oxford, UK (1998)Google Scholar
  4. 4.
    Kok, J.: A high-order low-dispersion symmetry-preserving finite-volume method for compressible flow on curvilinear grids. J. Comput. Phys. 228(18), 6811–6832 (2009)CrossRefzbMATHMathSciNetGoogle Scholar
  5. 5.
    Moser, R., Kim, J., Mansour, N.: Direct numerical simulation of turbulent channel flow up to Re = 590. Phys. Fluids 11(4), 11–13 (1999)CrossRefGoogle Scholar
  6. 6.
    Probst, A., Radespiel, R., Knopp, T.: Detached-eddy simulation of aerodynamic flows using a Reynolds-stress background model and algebraic RANS/LES sensors. In: AIAA Paper 2011–3206 (2011)Google Scholar
  7. 7.
    Radespiel, R., Turkel, E., Kroll, N.: Assessment of preconditioning methods. Technical Report, DLR-FB 95–29 (1995)Google Scholar
  8. 8.
    Reuß, S., Knopp, T., Probst, A., Orlt, M.: Assessment of local LES-resolution sensors for hybrid RANS/LES simulations. In: 5th Symposium on Hybrid RANS-LES Methods TEXAS A&M University, College Station, Houston, USA, 19–21 March 2014Google Scholar
  9. 9.
    Schwamborn, D., Gerhold, T., Heinrich, R.: The DLR TAU-code: recent applications in research and industry. In: Wesseling, P., Oñate, E., Périaux, J. (eds.) ECCOMAS CFD. TU Delft, The Netherlands (2006)Google Scholar
  10. 10.
    Shur, M.L., Spalart, P.R., Strelets, M.K., Travin, A.K.: A hybrid RANS-LES approach with delayed-DES and wall-modelled LES capabilities. Int. J. Heat Fluid Flow 29(6), 406–417 (2008)CrossRefGoogle Scholar
  11. 11.
    Swanson, R., Turkel, E.: On central-difference and upwind schemes. J. Comput. Phys. 306, 292–306 (1992)CrossRefMathSciNetGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.DLR (German Aerospace Center)GöttingenGermany

Personalised recommendations