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PANS Method as a Computational Framework from an Industrial Perspective

  • B. BasaraEmail author
Conference paper
Part of the Notes on Numerical Fluid Mechanics and Multidisciplinary Design book series (NNFM, volume 130)

Abstract

Although Computational Fluid Dynamics (CFD) is routinely used in a wide variety of industries, there are many remaining challenges in physical modelling as well as in numerical methods, which have to be tackled and eventually solved in the near future. Turbulence modelling, especially for industrial CFD, is still one of those open issues. For the purpose of a better and more practical or affordable representation of turbulence in complex flows, the variable resolution methods have emerged as an alternative to a computationally more costly Large Eddy Simulation (LES) method. At present, and among many approaches, the Partial-Averaged Navier Stokes (PANS) approach is one of the most attractive methods for industrial CFD. Therefore, the capabilities of the PANS on a wide range of CFD applications are shown in this paper. The results are presented for simple and well established benchmarks but also for industrial flows in complex geometries. The basic theory and arguments for the usage of this method are given. Besides the present status, the paper also provides some hints for possible improvements and explains some of the on-going activities in this field.

Keywords

Computational Fluid Dynamics Large Eddy Simulation Direct Numerical Simulation Resolution Parameter Integral Length Scale 
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.

Notes

Acknowledgments

It is the author’s pleasure to thank Dr. M. Bogensperger for setting up the engine test case.

References

  1. 1.
    AVL FIRE Manual, AVL List GmbH, Graz, Austria. CFD Solver Version 2013Google Scholar
  2. 2.
    Basara, B.: An eddy viscosity transport model based on elliptic relaxation approach. AIAA J. 44(7), 1686–1690 (2006)CrossRefGoogle Scholar
  3. 3.
    Basara, B., Krajnovic, S., Girimaji, S., Pavlovic, Z.: Near-wall formulation of the partially averaged Navier-Stokes turbulence model. AIAA J. 49(12), 2627–2636 (2011)CrossRefGoogle Scholar
  4. 4.
    Basara, B., Girimaji, S.: Modelling of the cut-off scale supplying variable in bridging methods for turbulence flow simulation. In: Proceedings of International Conference on Jets, Wakes, and Separated Flows, Nagoya, Japan, 17–21 Sept 2013Google Scholar
  5. 5.
    Foroutan, H., Yavuzkurt, S.: Partially-averaged Navier-Stokes modeling of turbulent swirling flow. In: American Physical Society, 66th Annual Meeting of the Division of Fluid Dynamics, Pittsburgh, Pennsylvania, 24–26 Nov 2013Google Scholar
  6. 6.
    Girimaji, S., Srinivasan, R., Jeong, E.: PANS turbulence models for seamless transition between RANS and LES; fixed point analysis and preliminary results. ASME Paper FEDSM45336, (2003)Google Scholar
  7. 7.
    Girimaji, S., Abdul-Hamid, K.S.: Partially-averaged Navier-Stokes model for turbulence: implementation and validation, AIAA Paper 2005–0502. Reno, NV (2005)Google Scholar
  8. 8.
    Girimaji, S.S.: Partially-averaged Navier-Stokes model for turbulence: a reynolds-averaged Navier-Stokes to direct numerical simulation bridging method. J. Appl. Mech. 73, 413–421 (2006)CrossRefzbMATHGoogle Scholar
  9. 9.
    Girimaji, S.S., Wallin, S.: Closure modeling in bridging regions of variable-resolution (VR) turbulence computations. J. Turbul. 14(1), 72–98 (2013)Google Scholar
  10. 10.
    Hussaini, M.Y., Thangam, S., Woodruff, S.L., Zhou, Y.: Development of a continuous model for simulation of turbulent flows. J. Appl. Mech. 73, 441–448 (2006)CrossRefzbMATHGoogle Scholar
  11. 11.
    Hanjalic, K., Popovac, M., Hadziabdic, M.: A robust near-wall elliptic-relaxation eddy-viscosity turbulence model for CFD. Int. J. Heat Fluid Flow 25(6), 1047–1051 (2004)CrossRefGoogle Scholar
  12. 12.
    Iwamoto, K., Suzuki, Y., Kasagi, N.: Reynolds number effect on wall turbulence: toward effective feedback control. Int. J. Heat Fluid Flow 23(5), 678–689 (2002)CrossRefGoogle Scholar
  13. 13.
    Jakirlic, S., Kutej, L., Basara, B., Tropea, C.: Computational study of the aerodynamics of a realistic car model by means of RANS and hybrid RANS/LES approaches. SAE Int. J. Passeng. Cars Mech. Syst. 7(2), (2014). doi: 10.4271/2014-01-0594
  14. 14.
    Popovac, M., Hanjalic, K.: Compound wall treatment for RANS computation of complex turbulent flows and heat transfer. Flow Turbul. Combust. 78, 177–202 (2007)Google Scholar
  15. 15.
    Speziale, C.G.: A combined large-eddy simulation and time-dependent RANS capability for high-speed compressible flows. J. Sci. Comput. 713(3), 441–448 (1998)MathSciNetGoogle Scholar
  16. 16.
    Wallin, S., Girimaji, S.S.: Commutation error mitigation in variable-resolution PANS closure: proof of concept in decaying isotropic turbulence. In: 6th AIAA Theoretical Fluid Mechanics Conference, AIAA Paper 2011–3105. Honolulu, Hawaii, 27–30 June 2011Google Scholar
  17. 17.
    Wallin, S., Reyes, D.A., Girimaji, S.S.: Bridging between coarse and fine resolution in variable resolution turbulence computations. In: Proceeding of Turbulence, Heat and Mass Transfer, vol. 7, Palermo, Italy (2012)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Advanced Simulation TechnologyAVL List GmbHGrazAustria

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