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Research Article

Periodic Filtering as a subgrid-scale model for LES of laminar separation bubble flows

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Pages 954-965 | Received 09 Dec 2015, Accepted 25 Jun 2016, Published online: 11 Aug 2016
 

ABSTRACT

Laminar separation bubbles develop over many blades and airfoils at moderate angles of attack and Reynolds numbers ranging from 104 to 105. More accurate simulation tools are necessary to enable higher fidelity design optimisation for these airfoils and blades as well as to test flow control schemes. Following previous investigators, an equivalent problem is formulated by imposing suitable boundary conditions for flow over a flat plate which allows to use a high accuracy spectral solver. Large eddy simulation (LES) of such a flow were performed at drastically reduced resolution to assess the accuracy of several LES modelling approaches: the explicit dynamic Smagorinsky model, implicit LES, and the truncated Navier–Stokes approach (TNS). To mimic dissipation that occurs in implicit LES, the solution on a coarse mesh is filtered at every time step and two different filter strengths are used. In the TNS approach, the solution is filtered periodically, every few hundred time steps. The performance of each approach is evaluated against benchmark direct numerical simulation (DNS) data focusing on pressure and skin friction distributions, which are critical to airfoil designers. TNS results confirm that periodic filtering can act as an apt substitute for explicit subgrid-scale models, whereas filtering at every time step demonstrates the dependence of implicit LES on details of numerics.

Acknowledgments

Thank you to Dr P. Spalart for sharing his DNS data and helpful insight along the way. We are grateful to Dr G. Castiglioni for stimulating discussions on numerical dissipation and implicit LES. This work was performed with computational time from the University of Southern California High Performance Computing Center.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

National Science Foundation [grant number CBET-1233160].

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