Revision as of 10:44, 15 September 2011

High Reynolds Number Flow around Airfoil in Deep Stall

Flows Around Bodies

Underlying Flow Regime 2-11

Best Practice Advice

Key Physics

The key physics of this UFR is predominantly characterised by the unsteady, three-dimensional, massively-separated wake region. This takes the form of a nominally periodic shedding of large scale, coherent vortices in a vortex street pattern, which is overlaid with finer random turbulent fluctuations at higher frequencies and random modulation and intermittency at frequencies lower than the vortex shedding frequency. It has been found that it is necessary to capture these key physical features in a simulation in order to reliably predict the assessment parameters. Whether this is achieved or not in a simulation should be checked by:

Obtaining a visual impression of the range of spatial scales present in the wake using e.g. a snapshot of the vorticity magnitude
Confirming the mixed tonal and broadband nature of the force coefficient time histories by e.g. visual inspection or spectral analysis of time histories.

Numerical Modelling

Discretisation method
Grids and grid resolution

Physical Modelling

Turbulence modelling
Transition modelling
Near-wall modelling
Other modelling

Application Uncertainties

Summarise any aspects of the UFR model set-up which are subject to uncertainty and to which the assessment parameters are particularly sensitive (e.g location and nature of transition to turbulence; specification of turbulence quantities at inlet; flow leakage through gaps etc.)

Recommendations for Future Work

Propose further studies which will improve the quality or scope of the BPA and perhaps bring it up to date. For example, perhaps further calculations of the test-case should be performed employing more recent, highly promising models of turbulence (e.g Spalart and Allmaras, Durbin's v2f, etc.). Or perhaps new experiments should be undertaken for which the values of key parameters (e.g. pressure gradient or streamline curvature) are much closer to those encountered in real application challenges.

Contributed by: Charles Mockett; Misha Strelets — CFD Software GmbH and Technische Universitaet Berlin; New Technologies and Services LLC (NTS) and Saint-Petersburg State University