Search Results

The presentation describes the aerodynamic development and optimization process of the three different new models of the Audi A6/A7 family. The body types of these three models represent the three classic aerodynamic body types squareback, notchback and fastback. A short introduction of the flow structures of these different body types is given and their effect on the vehicle aerodynamic is described. In order to achieve good aerodynamic performance, the integration into the development process of the knowledge about these flow phenomena and the breakdown of the aerodynamic resistance into its components friction- and pressure drag as well as the induced drag is very important. The presentation illustrates how this is realized within the aerodynamic development process at Audi. It describes how the results of CFD simulations are combined with wind tunnel measurements and how the information about the different flow phenomena were used to achieve an aerodynamic improvement.

The optimization of the flow field around new vehicle concepts is driven by aerodynamic and thermal demands. Even though aerodynamics and thermodynamics interact, the corresponding design processes are still decoupled. Objective of this study is to include a thermal model into the aerodynamic design process. Thus, thermal concepts can be evaluated at a considerably earlier design stage of new vehicles, resulting in earlier market entry. In a first step, an incompressible CFD code is extended with a passive scalar transport equation for temperature. The next step also accounts for buoyancy effects. The simulated development of the thermal boundary layer is validated on a hot flat plate without pressure gradient. Subsequently, the solvers are validated for a heated block with ground clearance: The flow pattern in the wake and integral heat transfer coefficients are compared to wind tunnel simulations. The main section of this report covers the validation on a full-scale production car.

Wheels on passenger vehicles cause about 25% of the aerodynamic drag. The interference of rims and tires in combination with the rotation result in strongly turbulent wake regions with complex flow phenomena. These wake structures interact with the flow around the vehicle. To understand the wake structures of wheels and their impact on the aerodynamic drag of the vehicle, the complexity was reduced by investigating a standalone tire in the wind tunnel. The wake region behind the wheel is investigated via Particle Image Velocimetry (PIV). The average flow field behind the investigated wheels is captured with this method and offers insight into the flow field. The investigation of the wake region allows for the connection of changes in the flow field to the change of tires and rims. Due to increased calculation performance, sophisticated computational fluid dynamics (CFD) simulations can capture detailed geometries like the tire tread and the movement of the rim.

The paper describes the aerodynamic development and optimization process of the three different new models of the Audi A6/A7 family. The body types of these three models represent the three classic aerodynamic body types squareback, notchback and fastback. A short introduction of the flow structures of these different body types is given and their effect on the vehicle aerodynamic is described. In order to achieve good aerodynamic performance, the integration into the development process of the knowledge about these flow phenomena and the breakdown of the aerodynamic resistance into its components friction- and pressure drag as well as the induced drag is very important. The paper illustrates how this is realized within the aerodynamic development process at Audi. It describes how the results of CFD simulations are combined with wind tunnel measurements and how the information about the different flow phenomena were used to achieve an aerodynamic improvement.

Hybrid vehicle simulation methods combine physical test articles (vehicles, suspensions, etc.) with complementary virtual vehicle components and virtual road and driver inputs to simulate the actual vehicle operating environment. Using appropriate components, hybrid simulation offers the possibility to develop more accurate physical tests earlier, and at lower cost, than possible with conventional test methods. MTS Systems has developed Hybrid System Response Convergence (HSRC), a hybrid simulation method that can utilize existing durability test systems and detailed non-real-time virtual component models to create an accurate full-vehicle simulation test without requiring road load data acquisition. MTS Systems and Audi AG have recently completed a joint evaluation project for the HSRC hybrid simulation method using an MTS 329 road simulator at the Audi facility in Ingolstadt, Germany.

Computational Fluid Dynamics (CFD) is state of the art in the aerodynamic development process of vehicles nowadays. With increasing computer power the numerical simulations including meshing and turbulence modeling are capturing the complex geometry of vehicles and the flow field behavior around and behind a bluff body in more detail. The ultimate goal for realistic automotive simulations is to model the under-hood as well. In this study vehicle simulations using the finite volume open source CFD program OpenFOAM® are validated with own experiments on a modified generic quarter-scale SAE body with under-hood flow. A model radiator was included to take account of the pressure drop in the under-hood compartment. Force and pressure measurements around the car, total-pressure and hot-wire measurements in the car flow field and surface flow patterns were simulated and compared with the experiment.

The interaction between cooling-air and external aerodynamics is known as interference. In a conventional car this interference under the hood results in additional drag. It is estimated that about 10% of the overall aerodynamic drag originates from the cooling air [1] depending on the car shape and cooling configuration. Obviously, cooling drag should be minimized for vehicles with low-drag aerodynamics. In this study cooling-air interference-effects are investigated through experimental, numerical and analytical methods with a focus on the surface pressure of the vehicle. The surface pressure of vehicles with and without interference effects is compared. Observations show that when the cooling-air inlet is opened a pressure rise occurs around the inlet, while a pressure drop appears around the outlet. This phenomenon was investigated for several vehicle shapes including a simplified bluff-body (SAE-Body) and a close-to-real quarter-scale model (aeromodel).

The question of the proper simulation of wheel rotation has not so far been a major concern. Within the scope of an examination of the influence of wheels and tyres on aerodynamic drag it will be shown that their contribution to the overall drag value - whether they are rotating or not - is of about the same magnitude as the proportion of the rough underbody. Therefore the question of the importance of the simulation of wheel rotation is posed. This paper discusses how a measurement with a better simulation can look like and what the major changes in the flow field are. In particular a new physical quantity, which has to be determined, the so-called “fan moment” is introduced. . The problems that arise in the determination of the fan moment of the wheels and hence in the required isolation of the rolling resistance, are described in detail. This is done for a test set up with full width moving belt and measurement via internal balance and sting support.

A wide-ranging investigation into the sensitivity of the hybrid RANS-LES based OpenFOAM CFD process at Audi was undertaken. For a range of cars (A1, TT, Q3 & A4) the influence of the computational grid resolution, turbulence model formulation and spatial & temporal discretization is assessed. It is shown that SnappyHexMesh, the Cartesian-prismatic built-in OpenFOAM mesher is unable to generate low y+ grids of sufficient quality for the production Audi car geometries. For high y+ grids there was not a consistent trend of additional refinement leading to improved correlation between CFD and experimental data. Similar conclusions were found for the turbulence models and numerical schemes, where consistent improvements over the baseline setup for all aerodynamic force coefficients were in general not possible. The A1 vehicle exhibited the greatest sensitivity to methodology changes, with the TT showing the least sensitivity.

The improved performance of heavy-duty vehicles as transport carriers is essential for economic reasons and to fulfil new emission standards in Europe. A key parameter is the aerodynamic vehicle drag. An enormous potential still exists for fuel saving and reducing exhaust emission by aerodynamic optimisation. Engineering methods are required for developments in vehicle aerodynamics. To assess the reliability of the most common experimental testing and numerical simulation methods in the industrial design process is the objective of this article. Road tests have been performed to provide realistic results, which are compared to the results obtained by scale-model wind tunnel experiments and time-averaged computational fluid dynamics (CFD). These engineering methods are evaluated regarding their deployment in the industrial development process. The investigations focus on the separated flow region behind the vehicle rear end.

In recent years, driving simulators have become a valuable tool in the automotive design and testing process. Yet, in the field of vehicle dynamics, most decisions are still based on test drives in real cars. One reason for this situation can be found in the fact that many driving simulators do not allow the driver to evaluate the handling qualities of a simulated vehicle. In a driving simulator, the motion cueing algorithm tries to represent the vehicle motion within the constrained motion envelope of the motion platform. By nature, this process leads to so called false cues where the motion of the platform is not in phase or moving in a different direction with respect to the vehicle motion. In a driving simulator with classical filter-based motion cueing, false cues make it considerably more difficult for the driver to rate vehicle dynamics.

Some widely-used scale-resolving turbulence models are comparatively assessed in simulating the aerodynamic behavior of a full-scale AUDI-A1 car configuration. The presently considered hybrid RANS/LES (RANS – Reynolds-Averaged Navier-Stokes; LES – Large-Eddy Simulation) models include the well-known DDES (Delayed Detached-Eddy Simulation) scheme and two further variable-resolution formulations denoted by PANS (Partially-Averaged Navier-Stokes; Basara, 2011) and VLES (Very LES; Chang et al., 2014). Whereas the DDES method represents the originally proposed formulation based on the one-equation Spalart-Almaras model (Spalart et al. 2006), whose RANS/LES interface position is directly correlated to the underlying grid resolution, the other two models represent ‘true’ seamless formulations, providing a smooth transition from Unsteady RANS to LES in terms of a dynamic “resolution parameter” variation.

The aerodynamic development of the new Audi Q5 (released in 2017) is described. In the course of the optimization process a number of different tools has been applied depending on the chronological progress in the project. During the early design phase, wind tunnel experiments at 1:4 scale were performed accompanied by transient DES and stationary adjoint simulations. At this stage the model contained a detailed underbody but no detailed engine bay for underhood flow. Later, a full scale Q5 model was built up for the aerodynamic optimization in the 1:1 wind tunnel at Audi AG. The model featured a detailed underbody and engine bay including original parts for radiators, engine, axles and brakes from similar vehicles. Also the 1:1 experiments were accompanied by transient DES and stationary adjoint simulations in order to predict optimization potential and to better understand the governing flow.

The aerodynamic optimization of an AUDI Q5 vehicle is presented using the continuous adjoint approach within the OpenFOAM framework. All calculations are performed on an unstructured automatically generated mesh. The primal flow, which serves as input for the adjoint method, is calculated using the standard CFD process at AUDI. It is based on DES calculations using a Spalart-Allmaras turbulence model. The transient results of the primal solution are time averaged and fed to a stationary adjoint solver using a frozen turbulence assumption. From the adjoint model, drag sensitivity maps are computed and measures for drag reduction are derived. The predicted measures are compared to CFD simulations and to wind tunnel experiments at 1:4 model scale. In this context, general challenges, such as convergence and accuracy of the adjoint method are discussed and best practice guidelines are demonstrated.

The detailed presentation of fundamental aerodynamics principles that influence and improve vehicle design have made Aerodynamics of Road Vehicles the engineer’s “source” for information. This fifth edition features updated and expanded information beyond that which was presented in previous releases. Completely new content covers lateral stability, safety and comfort, wind noise, high performance vehicles, helmets, engine cooling, and computational fluid dynamics.