A Numerical and Experimental Evaluation of Open Jet Wind Tunnel Interferences using the DrivAer Reference Model

Collin, Christopher; Mack, Steffen; Indinger, Thomas; Mueller, Joerg

doi:10.4271/2016-01-1597

Cited by 32 publications

(20 citation statements)

References 18 publications

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“…car base + rear end). Two published experimental databases, from the Audi and TUM wind tunnels, are compared to the numerical outcomes from the car assembly [8]. As expected, drag coefficients tend to asymptotic magnitudes as the mesh refinement level reduces.…”

Section: Validation Processmentioning

confidence: 89%

“…In addition to the body being tested, experimental studies require a model mounting system. In the majority of the DrivAer body test cases, an aerodynamically profile overhead support strut is used to connect the model [2][5] [8]. This component is not always described precisely in published works.…”

Section: Mathematical Modelsmentioning

confidence: 99%

“…Geometric dimensions have been parametrized as a function of the car scale ( ). Due to most of the available experimental data being related to a 40% scale-model, the same car scale is adopted in this study [2][5] [8]. Table 2 presents the DrivAer car dimensions as a function of the car scale, as well as the respective values assumed in this paper.…”

Section: Mathematical Modelsmentioning

confidence: 99%

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Comparison of the Far-Field Aerodynamic Wake Development for Three DrivAer Model Configurations using a Cost-Effective RANS Simulation

Soares¹,

Garry²,

Holt³

2017

SAE Technical Paper Series

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The flow field and body aerodynamic loads on the DrivAer reference model have been extensively investigated since its introduction in 2012. However, there is a relative lack of information relating to the models wake development resulting from the different rear-body configurations, particularly in the far-field. Given current interest in the aerodynamic interaction between two or more vehicles, the results from a preliminary CFD study are presented to address the development of the wake from the Fastback, Notchback, and Estateback DrivAer configurations. The primary focus is on the differences in the far-field wake and simulations are assessed in the range up to three vehicle lengths downstream, at Reynolds and Mach numbers of 5.2 × 10 6 and 0.13, respectively. Wake development is modelled using the results from a Reynolds-Averaged Navier-Stokes (RANS) simulation within a computational mesh having nominally 1.0 × 10 7 cells. This approach was chosen to reflect a simple, cost-effective solution, using an industry-standard CFD solver. Each vehicle configuration has a smooth underbody, with exterior rear-view mirrors. The computational modelling includes a ground simulation set, and all simulations are for zero freestream yaw angle. A mesh sensitivity study was undertaken and the simulation validated against published experimental data for the body pressure distribution and aerodynamic drag. Critical assessment of the results highlights the benefits of focussed mesh refinement and specific numerical strategies for optimum performance of the CFD solver. Comparison of the far-field aerodynamic wake for the three model configurations exhibits significant differences in both extent and structure within the wake region up to three vehicle lengths downstream of the base. Total pressure loss coefficient is used as the primary aerodynamic parameter for analysis. The study is an element of a larger programme related to vehicle wake simulation and strategies are identified for possible wake modelling using simplified, computationally and experimentally efficient, shapes.

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Section: Validation Processmentioning

confidence: 89%

Section: Mathematical Modelsmentioning

confidence: 99%

Section: Mathematical Modelsmentioning

confidence: 99%

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Comparison of the Far-Field Aerodynamic Wake Development for Three DrivAer Model Configurations using a Cost-Effective RANS Simulation

Soares¹,

Garry²,

Holt³

2017

SAE Technical Paper Series

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“…[37][38][39] Therefore, the DrivAer standard car model and its wind tunnel test data of the Technical University of Munich were used to determine the calculation scheme of CFD simulation analysis and verify the correctness of the results. 40,41 Considering the computational efficiency and the focus of research in this article, the combination configuration with smooth chassis, without rear-view mirrors, with wheels and moving ground were selected (FSwoMwW with GS). 42 The DrivAer configuration model selected is shown in Figure 2.…”

Section: Car Modelmentioning

confidence: 99%

Analysis and multi-objective optimization design of wheel based on aerodynamic performance

Wang

Zhang

et al. 2019

Advances in Mechanical Engineering

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In the past studies, the influence of wheel structure design and optimization on the aerodynamic drag of car and heat transfer performance of brake disc was less studied. To study the influence mechanism, the finite element model for wheel aerodynamic analysis was established using computational fluid dynamic method and the correctness of the model was verified by the wind tunnel test data. The influence mechanism of the flow field on aerodynamic drag of car and wheel and heat transfer performance of brake disc was studied. The influence of wheel disc structure on aerodynamic drag coefficient of the car and average convective heat transfer coefficient of the brake discs was studied. The parametric model of the wheel under the computational fluid dynamic analysis was established using mesh morphing technology. A total of 100 and 10 sample points were extracted using the Hammersley Design and Optimal Latin Hypercube Design, respectively, to fit the radial basis function surrogate model and test the accuracy of the surrogate model. Based on the established surrogate model, taking the minimum of the wheel mass, the minimum of the aerodynamic drag coefficient of the car and the maximum of the average surface convective heat transfer coefficient of brake discs as objectives and design variables of wheel as constraints to ensure the wheel structural strength, the non-dominated sorting genetic algorithm was adopted to carry out multi-objective optimization design for the wheel. Pareto frontier was obtained and a compromise solution was selected as the optimization design result. After the optimization design, the wheel mass is reduced by 8.28%, the aerodynamic drag coefficient of the car is reduced by 3.17% and the average surface convective heat transfer coefficient of brake discs is increased by 9.31%.

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“…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%

Complete Body Aerodynamic Study of three Vehicles

Simmonds¹,

Pitman²,

Tsoutsanis³

et al. 2017

SAE Technical Paper Series

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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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A Numerical and Experimental Evaluation of Open Jet Wind Tunnel Interferences using the DrivAer Reference Model

Cited by 32 publications

References 18 publications

Comparison of the Far-Field Aerodynamic Wake Development for Three DrivAer Model Configurations using a Cost-Effective RANS Simulation

Comparison of the Far-Field Aerodynamic Wake Development for Three DrivAer Model Configurations using a Cost-Effective RANS Simulation

Analysis and multi-objective optimization design of wheel based on aerodynamic performance

Complete Body Aerodynamic Study of three Vehicles

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