URANS Investigation of the Transonic M219 Cavity

  • L. TemmermanEmail author
  • B. Tartinville
  • Ch. Hirsch
Part of the Notes on Numerical Fluid Mechanics and Multidisciplinary Design book series (NNFM, volume 117)


A transonic cavity flow with a 5:1:1 aspect ratio is studied in the present work using a 2nd generation URANS modeling technique adapted to an EARSM model. An unstructured mesh made from hexahedral cells is used to perform these computations. The first part of the paper reports on recent improvement brought to the code. Results obtained on the M219 cavity are then presented and include the prediction of the mean flow and the tonal modes. The study also briefly looked at the influence of the time-step on the the prediction of the flow features.


Large Eddy Simulation Sound Pressure Level Reynolds Stress Model Numerical Dissipation Cavity Floor 
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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  1. de Henshaw, M.J.C.: M219 cavity case : Verification and validation data for computational unsteady aerodynamics. Tech. Rep. RTO-TR-26, AC/323(AVT)TP/19, QinetiQ, UK, pp. 453–472 (2002)Google Scholar
  2. Larcheveque, L., Sagaut, P., Le, H., Comte, P.: Large eddy simulation of a compressible flow in a three-dimensional open cavity at high Reynolds number. J. Fl. Mech. 301, 265–301 (2004)CrossRefGoogle Scholar
  3. Jameson, A., Schmidt, W., Turkel, E.: Numerical Solutions of the Euler Equations by Finite Volume Methods Using Runge-Kutta Time-Stepping Schemes. AIAA Paper 81-1259 (1981)Google Scholar
  4. Patel, A., Léonard, B., Esden, M., Hirsch, C.: A Parallel MultigridAdaptative Industrial Flow Solver on all-hexahedra meshes. In: Wyberg, N.-E., Díez, P. (eds.) International Conference on Adaptative Modelling and Simulation, ADMOES 2003 (2005)Google Scholar
  5. Temmerman, L., Hirsch, C.: Towards a successful implementation of DES strategies in industrial RANS solvers. In: Peng, S.H., Haase, W. (eds.) Advances in Hybrid RANS-LES Modeling, pp. 232–241. Springer, Berlin (2008)CrossRefGoogle Scholar
  6. Weinmann, K.A., Valentino, M.: Comparison of Hybrid RANS-LES calculations within the framework of compressible and incompressible unstructured solvers. In: Peng, S.H., Doerffer, P., Haase, W. (eds.) Progress in Hybrid RANS-LES Modeling, pp. 329–338. Springer, Berlin (2010)CrossRefGoogle Scholar
  7. Baurle, R.A., Tam, C.-J., Edwards, J.R., Hassan, H.A.: Hybrid simulation approach for cavity flows: blending, algorithm, and boundary treatment issues. AIAA J. 41, 1463–1480 (2003)CrossRefGoogle Scholar
  8. Travin, A., Shur, M., Strelets, M., Spalart, P.R.: Physical and numerical upgrades in the Detached Eddy Simulation of complex turbulent flows. In: Friedrich, R., Rodi, W. (eds.) Advances in LES complex Flows. Proc. of EUROMECH Colloquium, vol. 142, Kluwer Academic Publishers, Dordrecht (2002)Google Scholar
  9. Ducros, F., Ferrand, V., Nicoud, F., Weber, C., Darracq, D., Gacherieu, C., Poinsot, T.: Large-eddy simulation of the shock/turbulence interaction. J. Comp. Phys. 152, 517–549 (1999)zbMATHCrossRefGoogle Scholar
  10. Weber, C., Ducros, F., Corjon, A.: Large eddy simulation of complex turbulent flows. AIAA-Paper 98-2651 (1998)Google Scholar
  11. Kravchenko, A., Moin, P.: On the effect of numerical errors in large eddy simulations of turbulent flows. J. Comp. Phys. 131, 310–322 (1997)zbMATHCrossRefGoogle Scholar
  12. Hellsten, A.: New advanced k- ω turbulence modek for high-lift aerodynamics. AIAA J. 43(9), 1857–1869 (2005)CrossRefGoogle Scholar
  13. Wallin, S., Johansson, A.: An explicit algebraic Reynolds stress model for incompressible and compressible turbulent flows. J. Fluid Mech. 403, 89–132 (2000)MathSciNetzbMATHCrossRefGoogle Scholar
  14. Comte-Bellot, G., Corrsin, S.: Simple Eulerian time correlation of full- and narrow-band velocity signals in grid-generated, isotropic turbulence. J. Fl. Mech. 48, 273–337 (1971)CrossRefGoogle Scholar
  15. Menter, F.R., Egorov, Y.: The Scale-Adaptive Simulation method for unsteady turbulent flow predictions. Part 1: theory and model description. Flow Turbulence and Combustion 85, 113–138 (2010)zbMATHCrossRefGoogle Scholar
  16. Peng, S.-H.: M219 Cavity. In: Haase, W., Braza, M., Revell, A. (eds.) DESider – A European Effort on Hybrid RANS-LES Modelling. NNFM, vol. 103, pp. 270–285. Springer, Heidelberg (2009)Google Scholar
  17. Delanaye, M., Patel, A., Kovalev, K., Léonard, B., Hirsch, C.: From CAD to Flow Solution with Adaptive Unstructured Hexahedral Meshing. In: Mang, H.A., Rammerstorfer, F.G., Eberhardsteiner, J. (eds.) Fifth World Congress on Computational Mechanics, Vienna, Austria (2002)Google Scholar
  18. Mockett, C., Fuchs, M., Thiele, F.: Progress in DES for wall-modelled LES of complex internal flows. Submitted to Computer and Fluids (2011)Google Scholar

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© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Chaussée de La HulpeNUMECA Int. S.A.BrusselsBelgium

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