Effect of time-step size on flow around generic car models

Abstract

The current paper analyzes the effect of time-step size on the predictive capability and computational cost of two unsteady methods (Unsteady Reynolds-Averaged Navier–Stokes [URANS] and Delayed Detached-Eddy Simulations [DDES]). Two generic vehicle models (SAE notchback and DrivAer fastback) were tested using time-step sizes ranging over two orders-of-magnitude 1χ–100χ, with 1χ corresponding to the smallest time-step case with an average cell convective Courant number of around one. Of the two methods, URANS was less sensitive to time-step size than DDES in the time-averaged flow field predictions. For both unsteady methods, the drag buildup and mean flow-field predictions at a time-step size as high as 50χ was found to correlate closely with the temporally resolved case of 1χ. This was especially true around the rotating wheels of the DrivAer model, where even at the largest time-step size of 100χ, both unsteady methods predicted a flow much closer to the temporally well resolved case than steady RANS. Additionally, the added cost of the unsteady methods was found to be nearly inversely proportional to the time-step size when using a fixed number of inner iterations. At a time-step of 50χ, the total cost was only ∼3–7 times that of RANS.