Extreme Scale Automotive Aerodynamic Simulations

Achieving an optimal balance between simulation accuracy and computational efficiency remains a central challenge in automotive aerodynamics. While the adoption of AI and machine learning (ML) methods in vehicle development is expected to grow significantly, the demand for highly scalable, computationally efficient, and accurate computational fluid dynamics (CFD) methods persists. The emergence of GPU technology presents new opportunities to deliver cost-effective, high-fidelity, scale-resolving simulations to industrial users. A comprehensive evaluation of Simcenter STAR-CCM+’s parallel scalability and accuracy across extensive CPU and GPU resources was executed on the Frontier supercomputer at Oak Ridge National Laboratory. Steady-state and transient aerodynamic scalability simulations were executed using the DrivAer notchback vehicle configuration. Simulation accuracy was evaluated through transient simulations employing the SST-DDES turbulence model on an initial mesh of 200 million cells. Subsequent analyses examined the influence of spatial discretization by increasing the mesh size to 1 billion cells and the impact of temporal discretization by reducing the time step by an order of magnitude. Finally, the Wall-Modelled LES (WMLES) approach was applied to assess turbulence modeling effects. Validation was performed using detailed experimental data from the 3rd Automotive CFD Prediction Workshop, comparing numerical results for both the DrivAer baseline and a variant equipped with front wheel deflectors. In sum, these studies offer critical insights for software vendors developing next-generation CFD tools.