An advanced switching parameter for a hybrid LES/RANS model considering the characteristics of near-wall turbulent length scales

• ABE, Ken-ichi

  Department of Aeronautics and Astronautics, Faculty of Engineering, Kyushu University : Professor

Abstract

In this study, we proposed an idea for an advanced switching parameter used in a hybrid approach connecting large eddy simulation (LES) with Reynolds-averaged Navier-Stokes (RANS) modeling (the hybrid LES/RANS (HLR) model). Although the HLR model is promising way to predict engineering turbulent flows, an important problem is that RANS is always adopted in the near-wall region, even if the grid resolution is fine enough for LES. To overcome this difficulty, the switching parameter proposed here introduced knowledge of the Kolmogorov microscale that is thought to be reasonable for representing the near-wall turbulence. This parameter enabled the present HLR model to be smoothly replaced by a full LES if a grid resolution was fine enough in the near-wall region. To confirm model performance, the present HLR model was applied to numerical simulations of a periodic hill flow as well as fundamental plane channel flows. The model generally provided reasonable predictions for these test cases that include complex turbulence with massive flow separation.

Journal

• Theoretical and Computational Fluid Dynamics

  Theoretical and Computational Fluid Dynamics 28 (5), 499-519, 2014-08-09

  Springer nature

Keywords

• Turbulence
• Large Eddy Simulation
• Hybrid LES/RANS Model
• Near-wall Turbulent Structures
• Kolmogorov Microscale

Details

• CRID: 1050298532704079488
• NII Article ID: 120006950901
• NII Book ID: AA11631687
• DOI: 10.1007/s00162-014-0328-3
• ISSN: 14322250
• ISSN: 09354964
• HANDLE: 2324/4150511
• Web Site: http://link.springer.com/content/pdf/10.1007/s00162-014-0328-3.pdf
• Web Site: http://link.springer.com/article/10.1007/s00162-014-0328-3/fulltext.html
• Web Site: http://link.springer.com/content/pdf/10.1007/s00162-014-0328-3
• Text Lang: en
• Article Type: journal article
• Data Source:
  • IRDB
  • Crossref
  • CiNii Articles
  • KAKEN