Development of New Turbulence Models and Computational Methods for Automotive Aerodynamics and Heat Transfer

Holloway, Scott; Holloway, Mary V.; Leylek, James H.

doi:10.4271/2008-01-2999

Cited by 2 publications

(5 citation statements)

References 32 publications

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“…The first two regions are relatively easy for any CFD method to obtain as long as the turbulence model is not excessively diffusive; however, the latter region of shear layer rollup and breakdown are usually outside the realm of traditional RANS models [8][9][10]11,[17][18][19]. This is due to the relatively smaller time scale of these roller structures, compared to the wake structures, and the contradiction that arises with a basic assumption in the Reynoldsaveraging procedure that there is "significant" separation between the mean flow and turbulent flow time scales.…”

Section: Discussion Of Transient Structuresmentioning

confidence: 99%

“…It has shown promise in being able to capture the unsteady rollup and breakdown of a shear layer by readjusting the calculation of the turbulent viscosity in separated flow regions based on local flow parameters. The SDSM model is discussed in detail in Holloway et al [8] and Holloway [19]. It is a threeequation eddy-viscosity model that uses the work of Walters and Leylek [20,21] as a starting point.…”

Section: Computational Methodologymentioning

confidence: 99%

“…For example, Figure 7 shows that the SDSM approach overpredicts pressure in the latter third of the truck bed. This could be due to the fact that the original focus of SDSM [8,19] was not wake regions. Results for a horizontal line (H3, z=73.3 mm) on the cab back are shown in Figure 8.…”

Section: Qualitative Comparisonsmentioning

confidence: 95%

“…Though the URANS (EVU) and SDSM approach have been shown to include more of the relevant physics in other applications [8,10,11,18,19], they perform no better aft of the cab than a steady RKE approach for this case. This inconsistency shows that there is still work to be done in order to have a true predictive capability for the flow field behind a pickup truck.…”

Section: Qualitative Comparisonsmentioning

confidence: 99%

“…However, the traditional use of EVMs eliminates the ability to deal with the anisotropy of the RST and introduces an insensitivity of the turbulence field to streamline curvature. To accurately model flows with strong streamline curvature, such as around the hood, top and bottom of the windshield, and around pillars, it is necessary to model this effect in an eddyviscosity setting [7,8].…”

Section: Introductionmentioning

confidence: 99%

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Aerodynamics of a Pickup Truck: Combined CFD and Experimental Study

Holloway¹,

Leylek²,

York³

et al. 2009

SAE Int. J. Commer. Veh.

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This paper describes a computational and experimental effort to document the detailed flow field around a pickup truck. The major objective was to benchmark several different computational approaches through a series of validation simulations performed at Clemson University (CU) and overseen by those performing the experiments at the GM R&D Center. Consequently, no experimental results were shared until after the simulations were completed. This flow represented an excellent test case for turbulence modeling capabilities developed at CU.Computationally, three different turbulence models were employed. One steady simulation used the realizable k-H model. The second approach was an unsteady RANS simulation, which included a turbulence closure model developed in-house. This simulation captured the unsteady shear layer rollup and breakdown over the front of the hood that was expected and seen in the experiments but unattainable with other off-the-shelf turbulence models. Details of the high performance computing effort required to produce these results are discussed. The third simulation employed a new closure model designed to include the effects of small-scale unsteadiness on the mean flow without actually performing time-accurate simulations. As expected, this reduces CPU time, disk storage, and data I/O times significantly. In this case, CPU time was reduced by 95%. An approach of this nature would greatly reduce design time and make CFD a more feasible option.

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Section: Discussion Of Transient Structuresmentioning

confidence: 99%

Section: Computational Methodologymentioning

confidence: 99%

Section: Qualitative Comparisonsmentioning

confidence: 95%

Section: Qualitative Comparisonsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Aerodynamics of a Pickup Truck: Combined CFD and Experimental Study

Holloway¹,

Leylek²,

York³

et al. 2009

SAE Int. J. Commer. Veh.

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Fine Tuning the SST k − ω Turbulence Model Closure Coefficients for Improved NASCAR Cup Racecar Aerodynamic Predictions

Chen¹,

Bounds²,

Uddin³

et al. 2019

SAE Int. J. Adv. & Curr. Prac. in Mobility

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<div class="section abstract"><div class="htmlview paragraph">Faster turn-around times and cost-effectiveness make the Reynolds Averaged Navier-Stokes (RANS) simulation approach still a widely utilized tool in racecar aerodynamic development, an industry where a large volume of simulations and short development cycles are constantly demanded. However, a well-known flaw of the RANS methodology is its inability to properly characterize the separated and wake flow associated with complex automotive geometries using the existing turbulence models. Experience suggests that this limitation cannot be overcome by simply refining the meshing schemes alone. Some earlier researches have shown that the closure coefficients involved in the RANS turbulence modeling transport equations most times influence the simulation prediction results. The current study explores the possibility of improving the performance of the SST k − ω turbulence model, one of the most popular turbulence models in motorsports aerodynamic applications, by re-evaluating the values of certain model closure constants. A detailed full-scale current generation NASCAR Cup racecar was used for the investigation. The simulations were run using a commercial CFD package STAR-CCM+ (version 13.04.010). Five different closure coefficients in the SST k − ω model, σk1, σk2, σω1, σω2 and β∗, were examined. The investigation suggests the influence of each closure coefficient on the simulation prediction results are significantly different. β∗ appeared to be the most sensitive closure coefficient whereas both σk1 and σk2 had almost no effect on the NASCAR Cup racecar aerodynamic predictions. This study proposes a new set of SST k − ω turbulence model closure coefficients which has the potential of providing better-correlated aerodynamic predictions of a NASCAR Cup racecar under a range of different operating conditions.</div></div>

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Development of New Turbulence Models and Computational Methods for Automotive Aerodynamics and Heat Transfer

Cited by 2 publications

References 32 publications

Aerodynamics of a Pickup Truck: Combined CFD and Experimental Study

Aerodynamics of a Pickup Truck: Combined CFD and Experimental Study

Fine Tuning the SST k − ω Turbulence Model Closure Coefficients for Improved NASCAR Cup Racecar Aerodynamic Predictions

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