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Fine Tuning the SST kω Turbulence Model Closure Coefficients for Improved NASCAR Cup Racecar Aerodynamic Predictions

Published April 2, 2019 by SAE International in United States
Fine Tuning the SST <italic>k</italic> − <italic>ω</italic> Turbulence Model Closure Coefficients for Improved NASCAR Cup Racecar Aerodynamic Predictions
Sector:
Citation: Fu, C., Bounds, C., Uddin, M., and Selent, C., "Fine Tuning the SST kω Turbulence Model Closure Coefficients for Improved NASCAR Cup Racecar Aerodynamic Predictions," SAE Int. J. Adv. & Curr. Prac. in Mobility 1(3):1226-1232, 2019, https://doi.org/10.4271/2019-01-0641.
Language: English

Abstract:

Faster turn-around times and cost-effectiveness make the Reynolds Averaged Navier-Stokes (RANS) simulation approach still a widely utilized tool in racecar aerodynamic development, an industry where a large volume of simulations and short development cycles are constantly demanded. However, a well-known flaw of the RANS methodology is its inability to properly characterize the separated and wake flow associated with complex automotive geometries using the existing turbulence models. Experience suggests that this limitation cannot be overcome by simply refining the meshing schemes alone. Some earlier researches have shown that the closure coefficients involved in the RANS turbulence modeling transport equations most times influence the simulation prediction results. The current study explores the possibility of improving the performance of the SST kω turbulence model, one of the most popular turbulence models in motorsports aerodynamic applications, by re-evaluating the values of certain model closure constants. A detailed full-scale current generation NASCAR Cup racecar was used for the investigation. The simulations were run using a commercial CFD package STAR-CCM+ (version 13.04.010). Five different closure coefficients in the SST kω model, σk1, σk2, σω1, σω2 and β, were examined. The investigation suggests the influence of each closure coefficient on the simulation prediction results are significantly different. β appeared to be the most sensitive closure coefficient whereas both σk1 and σk2 had almost no effect on the NASCAR Cup racecar aerodynamic predictions. This study proposes a new set of SST kω turbulence model closure coefficients which has the potential of providing better-correlated aerodynamic predictions of a NASCAR Cup racecar under a range of different operating conditions.