Fine Tuning the SST k − ω Turbulence Model Closure Coefficients for Improved NASCAR Cup Racecar Aerodynamic Predictions

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 k − ω 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 k − ω model, σ_k1, σ_k2, σ_ω1, σ_ω2 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 σ_k1 and σ_k2 had almost no effect on the NASCAR Cup racecar aerodynamic predictions. This study proposes a new set of SST k − ω 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.