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The lattice Boltzmann (LB) method has been successfully used in conjunction with a Very Large-Eddy Simulation (VLES) turbulence modeling approach for over a decade for the accurate prediction of automotive fluid dynamics. Its success lies in the unique underlying physics that is significantly different from traditional computational fluid dynamics methods. In this paper, we provide a complete description of the method followed by a set of examples which show its use in the automotive industry. We will first provide a review of the physics and numerical methods. Here the LB method and its relationship to kinetic theory and the Navier-Stokes equations will be briefly discussed. We will summarize the strengths of LB method, especially for the solution of transient flows in extremely complex geometries. The VLES turbulence modeling method will be presented next, as well as how VLES neatly fits into the LB framework.

A vehicle on the road encounters an unsteady flow due to turbulence in the natural wind, the unsteady wakes from other vehicles and as a result of traversing through the stationary wakes of road side obstacles. There is increasing concern about potential differences in aerodynamic behaviour measured in steady flow wind tunnel conditions and that which occurs for vehicles on the road. It is possible to introduce turbulence into the wind tunnel environment (e.g. by developing active turbulence generators) but on-road turbulence is wide ranging in terms of both its intensity and frequency and it would be beneficial to better understand what aspects of the turbulence are of greatest importance to the aerodynamic performance of vehicles. There has been significant recent work on the characterisation of turbulent airflow relevant to road vehicles. The simulation of this time-varying airflow is now becoming possible in wind tunnels and in CFD.

Aerodynamic efficiency plays an increasingly important role in the automotive industry, as the push for increased fuel economy becomes a larger factor in the engineering and design process. Longitudinal drag is used as the primary measure of aerodynamic performance, usually cited as the coefficient of drag (CD). This drag is created mostly by the body shape of the vehicle, but the wheel and tire system also contributes a significant portion. In addition to the longitudinal drag created by the body and wheels, rotational drag can add an appreciable amount of aerodynamic resistance to the vehicle as well. Reducing power consumption is an especially vital aspect in electric vehicle (EV) design. As the world's first luxury electric sedan, the Tesla Model S combines a premium driving experience with an electric drivetrain package that allows for unique solutions to many vehicle subsystems.

The Tesla Motors Model S has been designed from a clean sheet of paper to prove that no compromises to a desirable aesthetic style and world class driving experience are necessary in order to be energy efficient. Aerodynamic optimization is a major contributor to the overall efficiency of an electric vehicle and the close integration of the Design and Engineering groups at Tesla Motors was specifically arranged to process design iterations quickly and enable the fully informed development of the exterior surfaces at a very rapid pace. Clear communication and a working appreciation of each other's priorities were vital to this collaboration and underpinning this was extensive use of the powerful analysis and visualization capabilities of CFD. CFD was used to identify and effectively communicate the nature of beneficial and detrimental design features and to find ways to enhance or ameliorate them accordingly.

Numerical simulation of aerodynamics for vehicle development is used to meet a wide range of performance targets, including aerodynamic drag for fuel efficiency, cooling flow rates, and aerodynamic lift for vehicle handling. The aerodynamic flow field can also be used to compute the advection of small particles such as water droplets, dust, dirt, sand, etc., released into the flow domain, including the effects of mass, gravity, and the forces acting on the particles by the airflow. Previous efforts in this topic have considered the water sprays ejected by rotating wheels when driving on a wet road. The road spray carries dirt particles and can obscure the side and rear glazing. In this study, road sprays are considered in which the effects of additional water droplets resulting from splashing and dripping of particles from the wheel house and rear under body are added to help understand the patterns of dirt film accumulation on the side glass and rear glass.

Ground transportation industry contributes to about 14% of the global CO2 emissions. Therefore, any effort in reducing global CO2 needs to include the design of cleaner and more energy efficient vehicles. Their design needs to be optimized for the real-world conditions. Using wind tunnels that can only reproduce idealized conditions quite often does not translate into real-world on-road CO2 reduction and improved energy efficiency. Several recent studies found that very rarely can the real-world environment be represented by turbulence-free conditions simulated in wind tunnels. The real-world conditions consist of both transversal flow velocity component (causing an oncoming yaw flow) as well as large-scale turbulent fluctuations, with length scales of up to many times the size of a vehicle. The study presented in this paper shows how the realistic wind affects the aerodynamics of the vehicle.

Water accumulating on a vehicle's wind screen, driven over the A-pillar by a combination of aerodynamic forces and the action of the windscreen wipers, can be a significant impediment to driver vision. Surface water film, or streams, persisting in key vision areas of the side glass can impair the drivers' ability to see clearly through to the door mirror, and laterally onto junctions. Common countermeasures include: water management channels and hydrophobic glass coatings. Water management channels have both design and wind noise implications. Hydrophobic coatings entail significant cost. In order to manage this design optimisation issue a water film and wiper effect model has been developed in collaboration with Jaguar Land Rover, extending the capabilities of the PowerFLOW CFD software. This is complimented by a wind-tunnel based test method for development and validation. The paper presents the progress made to date.

Contamination of vehicle rear surfaces is a significant issue for customers. Along with being unsightly, it can degrade the performance of rear camera systems and lighting, prematurely wear rear screens and wipers, and transfer soil to customers moving goods through the rear tailgate. Countermeasures, such as rear camera wash or automated deployment add expense and complexity for OEMs. This paper presents a rear surface contamination model for a fully detailed SUV based on the use of a highly-resolved time-accurate aerodynamic simulation realised through the use of a commercial Lattice-Boltzmann solver, combined with Lagrangian Particle Tracking to simulate droplet advection and surface water dynamics via a thin film model. Droplet break-up due to aerodynamic shear is included, along with splash and stripping from the surface film. The effect of two-way momentum coupling is included in a sub-set of simulations.

Recent regulations on greenhouse gas (GHG) emission standards for heavy duty vehicles have prompted government agencies to standardize procedures to assess aerodynamic performance of Class 8 tractor-trailers. The coastdown test procedure is the primary reference method to assess vehicle drag and other valid alternatives include wind tunnel testing and computational fluid dynamics (CFD) simulations. While there have been many published studies comparing results between simulations and wind tunnel testing, it is less well understood how to compare results with coastdown testing. Both the wind tunnel and simulation directly measure aerodynamic drag forces in controlled conditions, while coastdown testing is conducted in an open road environment, aerodynamic forces are calculated from a road load equation, and variable wind and vehicle speed introduce additional complexity.

Historically vehicle aerodynamic development has focused on testing under idealised conditions; maintaining measurement repeatability and precision in the assessment of design changes. However, the on-road environment is far from ideal: natural wind is unsteady, roadside obstacles provide additional flow disturbance, as does the presence of other vehicles. On-road measurements indicate that turbulence with amplitudes up to 10% of vehicle speed and dominant length scales spanning typical vehicle sizes (1-10 m) occurs frequently. These non-uniform flow conditions may change vehicle aerodynamic behaviour by interfering with separated turbulent flow structures and increasing local turbulence levels. Incremental improvements made to drag and lift during vehicle development may also be affected by this non-ideal flow environment. On-road measurements show that the shape of the observed turbulence spectrum can be generalised, enabling the definition of representative wind conditions.

One of the remaining challenges in the simulation of the aerodynamics of ground vehicles is the modeling of the airflows around the spinning tires and wheels of the vehicle. As in most advances in the development of simulation capabilities, it is the lack of appropriately detailed and accurate experimental data with which to correlate that holds back the advance of the technology. The flow around the wheels and tires and their interfaces with the vehicle body and the ground is a critical area for the development of automobiles and trucks, not just for aerodynamic forces and moments, and their result on fuel economy and vehicle handling and performance, but also for the airflows and pressures that affect brake cooling, engine cooling airflows, water spray management etc.

This paper presents a methodology for simulating Fluid Structure Interaction (FSI) for a typical vehicle bonnet (hood) under a range of onset flow conditions. The hood was chosen for this study, as it is one of the panels most prone to vibration; particularly given the trend to make vehicle panels lighter. Among the worst-case scenarios for inducing vibration is a panel being subjected to turbulent flow from vehicle wakes, and the sudden peak loads caused by emerging from a vehicle wake. This last case is typical of a passing manoeuvre, with the vehicle suddenly transitioning from being immersed in the wake of the leading vehicle, to being fully exposed to the free-stream flow. The transient flowfield was simulated for a range of onset flow conditions that could potentially be experienced on the open road, which may cause substantial vibration of susceptible vehicle panels.

Previous work by the authors showed the development of an aerodynamic CFD model using the Lattice Boltzmann Method for simulating vehicles inside the IVK Model-Scale Wind-Tunnel test-section. In both experiment and simulation, alternate configurations of the wind-tunnel geometry were studied to change the pressure distribution in the wind-tunnel test section, inducing a reduction in aerodynamic drag due to interference between the wind-tunnel geometry and the pressure on the surface of the vehicle. The wind-tunnel pressure distribution was modified by adding so-called “stagnation bodies” inside the collector to create blockage and to increase the pressure in the rear portion of the test section. The primary purpose of previous work was to provide a validated CFD approach for modeling wind-tunnel interference effects, so that these effects can be understood and accounted for when designing vehicles.

Aerodynamic evaluation of vehicles using static yaw angle changes in wind tunnel testing and numerical simulation has been used as standard practice for evaluating vehicle performance under a range of wind conditions. However, this approach does not consider dynamic wind effects coming from changing wind conditions, passing other vehicles and roadside obstacles, and transient non-uniform wind conditions coming from environmental turbulence. In previous work by the authors, computational fluid dynamics (CFD) simulation methodology for considering dynamic wind conditions and on-road turbulence was demonstrated, showing the important effects of the wind conditions on the vehicle aerodynamics. The technique allows the vehicle to be tested under a range of transient gust conditions, also accounting for wind turbulence coming from upstream vehicles and natural environmental wind fluctuations.

The focus of evaluating yaw characteristics in automotive aerodynamics has been primarily with regards to the effects of crosswind on vehicle handling. However, changes to drag that the vehicle experiences due to prevalent on-road crosswind can also be significant, even at low yaw angles. Using wind tunnel testing, it is possible to quickly determine the static yaw performance of the vehicle by rotating the vehicle on a turntable to different yaw angles during a single wind tunnel run. However, this kind of testing does not account for dynamic crosswind effects or non-uniform crosswind such as with natural on-road turbulence. Alternatively, numerical simulations using computational fluid dynamics (CFD) can be used to evaluate yaw performance. In this paper, Exa's PowerFLOW is used to examine two alternative methods of simulating aerodynamic performance in the presence of realistic on-road crosswind for the Tesla Model S sedan.

The aerodynamics of a rotating tire can contribute up to a third of the overall aerodynamic force on the vehicle. The flow around a rotating tire is very complex and is often affected by smallest tire features. Accurate prediction of vehicle aerodynamics therefore requires modeling of tire rotation including all geometry details. Increased simulation accuracy is motivated by the needs emanating from stricter new regulations. For example, the upcoming Worldwide harmonized Light vehicles Test Procedures (WLTP) will place more emphasis on vehicle performance at higher speeds. The reason for this is to bring the certified vehicle characteristics closer to the real-world performance. In addition, WLTP will require reporting of CO2 emissions for all vehicle derivatives, including all possible wheel and tire variants. Since the number of possible derivatives can run into the hundreds for most models, their evaluation in wind tunnels might not be practically possible.

Aerodynamic performance assessment of automotive shapes is typically performed in wind tunnels. However, with the rapid progress in computer hardware technology and the maturity and accuracy of Computational Fluid Dynamics (CFD) software packages, evaluation of the production-level automotive shapes using a digital process has become a reality. As the time to market shrinks, automakers are adopting a digital design process for vehicle development. This has elevated the accuracy requirements on the flow simulation software, so that it can be used effectively in the production environment. Evaluation of aerodynamic performance covers prediction of the aerodynamic coefficients such as drag, lift, side force and also lift balance between the front and rear axle. Drag prediction accuracy is important for meeting fuel efficiency targets, prediction of front and rear lifts as well as side force and yawing moment are crucial for high speed handling.

The ultimate goal for vehicle aerodynamicists is to develop vehicles that perform well on the road under real-world conditions. One of the most important metrics to evaluate vehicle performance is the drag coefficient. However, vehicle development today is performed mostly under controlled settings using wind tunnels and computational fluid dynamics (CFD) with artificially uniform upstream conditions, neglecting real-world effects due to road turbulence from wind and other vehicles. Thus, the drag coefficients computed with these methods might not be representative of the real performance of the car on the road. This might ultimately lead engineers to develop design solutions and aerodynamic devices which, while performing well in idealized conditions, do not perform well on the road. For this reason, it is important to assess the vehicle’s drag as seen in real-world environments. An effort in this direction is represented by using the wind-averaged drag.