Evaluation of CFD methodologies for prediction of flows around simplified and complex automotive models

Abstract

A comprehensive evaluation of the predictive capability and computational costs of Reynolds Averaged Navier–Stokes (RANS), Unsteady RANS (URANS), Delayed Detached-Eddy Simulation (DDES), and Lattice-Boltzmann Method (LBM) methodologies was carried out for flow around the SAE notchback and the DrivAer fastback models. From the comparison, all methods predicted drag within roughly 10% of experimental values. However, this accuracy was misleading for RANS as the predicted flowfield deviated from both the unsteady methods and experiments in regions where flow separation was developing. This was especially true around the rotating wheel flows of the DrivAer model, where RANS predicted extensive separation around the rear wheels to the point that it changed the structure of the vehicle wake. In these regions of highly separated flow, the unsteady methods proved to be much more accurate and robust, although comparisons of the flow around the rotating wheels proved inconclusive as to which methods provided suitable predictive capabilities. Despite improved flow predictions, URANS and DDES proved to be significantly more expensive than RANS, while LBM provided comparable accuracy to URANS/DDES with substantially reduced costs.