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Predictive Capability Assessment of the PANS-\(\zeta \)-f Model of Turbulence. Part I: Physical Rationale by Reference to Wall-Bounded Flows Including Separation

  • C.-Y. Chang
  • S. JakirlicEmail author
  • B. Basara
  • C. Tropea
Conference paper
Part of the Notes on Numerical Fluid Mechanics and Multidisciplinary Design book series (NNFM, volume 130)

Abstract

The present work deals with validation of the PANS (Partially-Averaged Navier-Stokes) computational methodology coupled with the \(\zeta -f\) RANS model of turbulence [10], formulated and implemented into the CFD software package AVL-FIRE by Basara et al. [2], in a broad range of wall-bounded flows featured by separation, swirling and tumbling motion. The configurations considered in the part I include a fully-developed flow in a plane channel (DNS by Moser et al. [14]) and flow separating from a continuous curved surface (reference database is provided by LES, [7] and [3]). The PANS approach, whose validation represents the prime objective of the present work, provides a seamless transition from the fully-modelled Unsteady RANS to the fully-resolved direct numerical solution (DNS) as the unresolved-to-total ratios of kinetic energy and its dissipation are appropriately varied. In addition, the complementary RANS (by using the \(\zeta -f\) model) computations of the flow configurations considered are also performed. The results obtained illustrate the PANS model capability to capture the turbulence unsteadiness leading consequently to a correct prediction of time-averaged flow quantities, unlike its RANS counterpart. The companion part II [4] is concerned with the PANS model application to more complex flow configurations including a tumbling vortex generation and compression and a swirling flow in a tube, the outlet of which is designed as an orifice with eccentric opening.

Keywords

Flow Configuration Separate Shear Layer RANS Model Direct Numerical Solution Reynolds Stress Component 
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

The financial support of the AVL List GmbH Company, Graz, Austria for C.-Y. Chang is gratefully acknowledged.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • C.-Y. Chang
    • 1
  • S. Jakirlic
    • 1
    Email author
  • B. Basara
    • 2
  • C. Tropea
    • 1
  1. 1.Institute of Fluid Mechanics and Aerodynamics/Center of Smart Interfaces Technische Universität DarmstadtDarmstadtGermany
  2. 2.Advanced Simulation Technologies—ASTAVL List GmbHGrazAustria

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