The Effects of Cooling Air on the Flow Field around a Vehicle

Kuthada, Timo; Wittmeier, Felix; Bock, Benjamin; Schoenleber, Christoph; Link, Alexander

doi:10.4271/2016-01-1603

Cited by 17 publications

(14 citation statements)

References 6 publications

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“…this is to match the conditions of previous cooling work [13]. While not expanded upon during this paper it has been found that the leakage cooler configuration can increase the cooling drag coefficient by at least 10 counts (0.01 Cd).…”

Section: Automotive Modelsmentioning

confidence: 60%

“…For additional comparison, Exa PowerFLOW was also utilized. The Lattice Boltzmann Solver (LBM) was used successfully to perform the computational component of previous work [13]. In the present work it will be used to validate differences in the results that may arise from the differences in the simulation set-up (wheel arches, wind tunnel effects and scale).…”

Section: Methodsmentioning

confidence: 99%

“…These interferences have been an important field of study in cooling airflows and have been well documented. [10,11,12,13]. Other relevant work in this subject has focused on studying the effect of various components on the system [5,9,14].…”

Section: Cooling Air Flowsmentioning

confidence: 99%

“…Most recently the DrivAer model was seen as an ideal candidate to modify by including underhood geometry, in order to provide a benchmark case which could be introduced for validation [12]. This has allowed for geometrical changes and for external factors such as wind tunnel affects to be modeled [13,15].…”

Section: Cooling Air Flowsmentioning

confidence: 99%

“…The model is designed for further study by providing two variants of the cooler. One without any leakage, which has been previously published [12,13] and one which contains leakage around the cooling package.…”

Section: Automotive Modelsmentioning

confidence: 99%

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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.

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Section: Automotive Modelsmentioning

confidence: 60%

Section: Methodsmentioning

confidence: 99%