A unified hybrid Reynolds-averaged Navier–Stokes/large-eddy simulation closure is presented that is built from data-driven methods. This is a novel way to construct such models that does not impose constraints. Direct numerical simulation data is filtered, and the ratio of resolved to unresolved energy is used to fit an ideal length scale damping function for the unified framework. This study shows the viability of using high-fidelity data, not just for a priori testing but for the complete creation of lower-fidelity methods. Alongside the physical model, a convection scheme is proposed that marries well to it. This numerical scheme ensures that the damped turbulence model is provided the appropriate ratio of stability to accuracy. Furthermore, an additional function guarantees that the large-eddy simulation mode is only active in vortical flow. This hybrid closure is then applied to two industrially relevant yet very different geometries for which reliable reference data exist. The periodic hills test case is characterized by a confined separation and reattachment, whereas the tandem cylinders exhibits massive separation with wake interference patterns. For the latter, aeroacoustic predictions are of paramount importance. On both geometries, two mesh levels are tested. This a posteriori validation program provides a stern test for the new closure. For both resolutions on both geometries, the new formulation performs extremely well. Each case is also predicted using an established closure, and comparison to this further illustrates the potential of the current framework.
Hybrid Reynolds-Averaged/Large-Eddy Simulation Methodology from Symbolic Regression: Formulation and Application
AIAA Journal ; 55 , 11 ; 3734-3746
01.11.2017
Aufsatz (Konferenz) , Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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