Comparison of RANS and DES Methods for the DrivAer Automotive Body

Computational Fluid Dynamics (CFD) is now one of the most important design tools for the automotive industry. Reliable CFD simulations of the complex separated turbulent flow around vehicles is becoming an ever more crucial goal to increase fuel efficiency and reduce noise emissions. In this study Reynolds Averaged Navier-Stokes (RANS) models (both at eddy-viscosity and second-moment closure levels) are compared to hybrid RANS-LES methods (Detached-Eddy Simulation). The application is the DrivAer model; a new open-source realistic car model which aims to bridge the gap between simple Ahmed body and MIRA/SAE Reference car models and actual car geometries in use by the major car manufacturers.

To date, many hybrid RANS-LES studies on complex geometries have been under-resolved compared to more academic cases, due to a limit on computational resources available. In this work a thorough assessment of grid resolution up to 300 million cells is conducted together with a discussion of mesh metrics to assess grid resolution. It is found that no RANS model can successfully capture the correct flow field for all car configurations, even with fine meshes using advanced Reynolds Stress models. This failure is attributed to an under-prediction of the turbulence levels in the initial separated shear layer, which leads to an over-prediction of the recirculation size. The use of DES shows a clear improvement in terms of the drag coefficient and pressure distribution for each configuration. Whilst the results are still not in perfect agreement with the experimental data, the trends between the difference car models are in excellent agreement with the experimental data. Finally suggestions for further improvements are also discussed and similarities between the DrivAer models and the Ahmed car body are presented.