Complete Body Aerodynamic Study of three Vehicles

Abstract

Cooling drag, typically known as the difference in drag coefficient between open and closed cooling configurations, has traditionally proven to be a difficult flow phenomenon to predict using computational fluid dynamics. It was seen as an academic yardstick before the advent of grille shutter systems. However, their introduction has increased the need to accurately predict the drag of a vehicle in a variety of different cooling configurations during vehicle development. This currently represents one of the greatest predictive challenges to the automotive industry due to being the net effect of many flow field changes around the vehicle. A comprehensive study is presented in the paper to discuss the notion of defining cooling drag as a number and to explore its effect on three automotive models with different cooling drag deltas using the commercial CFD solvers; STARCCM+ and Exa PowerFLOW. The notchback DrivAer model with under-hood cooling provides a popular academic benchmark alongside two fully-engineered production cars; a large saloon (Jaguar XJ) and an SUV (Land Rover Range Rover). Initially three levels of spatial discretization were used with three steady-state RANS solvers (k-ɛ realizable, k-ω SST and Spalart-Allmaras) to ascertain whether previous work using RANS on the large saloon studying cooling flows could be replicated on other vehicle shapes. For both the full-production vehicles, all three turbulence models were capable of predicting the cooling drag delta within 5 counts (0.005 C d). However, the DrivAer model was much more sensitive to both changes in turbulence models and mesh sizes. For the SA turbulence model only the drag coefficient was well predicted, for the other two RANS models no amount of grid refinement allowed the models to correctly predict the flow field. It was seen when comparing the k-ɛ realizable and SA turbulence models the difference in cooling drag was attributed to the rear of the vehicle. This highlighted that despite similar drag values from the cooling package, the cooling deltas were very different, suggesting that cooling drag cannot be thought of as open-closed drag with the addition of drag due to the cooling package. Further work on the DrivAer model expanded on the RANS simulations utilizing the eddy-resolving methods, IDDES and LBM, as validation cases. Oscillations which were seen in the SA and k-ω SST RANS turbulence models were shown to be of similar levels to those in the transient methods indicating a pseudo-unsteadiness present in the steady-state solvers and the importance of resolving it. Drag and lift coefficient absolute values were compared showing that only the IDDES method with sliding wheels and LBM method could obtain physical results for the majority of the tested criteria.

Turbulence modeling effects on the aerodynamic characterizations of a NASCAR Generation 6 racecar subject to yaw and pitch changes

“…Literature revealed that the selection of turbulence model alone can result in significantly different predictions in many engineering applications. 19–22 As far as the road vehicle external aerodynamic studies are concerned, the turbulence modeling effects on RANS predictions are considerably explored with simplified vehicle models, most commonly, the Ahmed body and the DrivAer model, 23–25 as well as various types of passenger vehicles; 26,27 recent studies by Fu et al 28,29 are probably the only one related to stock racecar applications. However, since the racecar’s aerodynamic features can be very different under yaw and/or pitch and these racecars are intentionally made asymmetric, observations based on straightway attitudes may not be valid.…”

Development of an Algebraic Model of Empirical Parameterization of Near Wakes around a Vehicle

“…In the CFD calculation, iterations of the pressure and velocity equations have not converged in all cases (described later in Computational costs). Simmonds et al (2017) [23] pointed out that the RANS analysis for the DrivAer model using k-omega SST turbulence model experienced fluctuations due to a pseudo-unsteadiness in the flow. We consider that the pseudo-unsteadiness appears in our simulations.…”

Numerical modeling of airflow over column of vehicles using Ansys® package

“…Elements around vehicle's geometric has been applied using inflation layer. Inflation layer is 20 cell height with corresponding y+ value near 35 [9]. As turbulence model, in simulation, was used k-ω Shear Stress Transport (SST).…”