Search Results

As the automotive industry is quickly changing towards electric vehicles, we can highlight the importance of aerodynamics and its critical role in reaching extended battery ranges for electric cars. With all new smooth underbodies, a lot of attention has turned into the effects of rim designs and tires brands and the management of these tire wakes with the vehicle. Tires are one of the most challenging areas for aerodynamic drag prediction due to its unsteady behavior and rubber deformation. With the simulation technologies evolving fast regarding modeling spinning tires for aerodynamics, this paper takes the prior work and data completed by the authors and investigates the impact on the flow fields and aerodynamic forces using the most recent developments of an Immerse Boundary Method (IBM). IBM allows us to mimic realistically a rotating and deformed tire using Lattice Boltzmann methods.

Developing synergy between styling design and aerodynamic efficiency has been an ongoing challenge in the automotive industry for many years. Designers and aerodynamicists have to meet strict deadlines and have limited resources to iterate and evolve the design and performance of the vehicle exterior during early concept development. The number of possible styling variants and configurations can often reach into the hundreds. Physical wind tunnel measurements are simply not practical from a time or cost perspective to evaluate these designs. Therefore, increased performance and accuracy of Computational Fluid Dynamics (CFD) simulations have become the main objective of every vehicle OEMs. Today, the GPU hardware, particularly the memory and performance, has reached a point where there is an increased interest in their usage for aerodynamics simulations. GPUs offer potential simulation performance improvements due to lower power consumption and hardware costs.

Automotive manufacturers are under continuous pressure to satisfy changing consumer demands and regulatory requirements in an increasingly competitive landscape. This requires Aerodynamic departments to evaluate more design ideas in less development time. Aerodynamic departments are seeking to speed up their analysis in order to provide more feedback on performance to design and styling. Vehicle designers already leverage Computational Fluid Dynamics in order to quickly assess vehicle aerodynamic performance during product development. However, in order to meet modern development challenges, reducing simulation cost and turn-around-time is necessary. To that end, a novel approach to reducing simulation time of vehicle aerodynamics without sacrificing accuracy was tested in this paper. The methodology is called Transient Boundary Seeding, and enables the usage of a reduced simulation domain without the loss of information from the omitted region.

For a typical passenger ground vehicle, the rotating treaded tires and wheel housings typically account for up to 25% of the total vehicle aerodynamic drag. Due to the importance of tires and their treads to the overall vehicle aerodynamics, it’s critically important for aerodynamics departments to have accurate simulations that can predict the effects of rotating tires. Accurate prediction of flow around a tire is a complex phenomenon and is affected by details of tire features such as tread pattern, air pressure, and tire deformation. In particular, the physics of the tire treads spatial movement and deformation (i.e. expansion, contraction, sidewall bulge, and squeezing of airflow) as the tires rotate and come in contact with ground, especially around the contact patch, is vitally important in determining accurate wheel wakes, underbody flow, and the overall wake of the vehicle.

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.

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.

The Ahmed body is a simplified vehicle geometry that results in flow features representative of those found at the rear of most passenger vehicles. By adjusting the rear slant angle, separation can take place at the sharp corner, on the rear slant panel, or not at all. Accurate prediction of the separation and reattachment of the flow is essential in predicting the correct drag trends. This separation and reattachment is known to be a highly unsteady phenomenon. The objective of this study is to evaluate the ability of a lattice Boltzmann based CFD code to predict the correct drag trends and flow structures for the Ahmed body at varying rear slant angles. Component and total drag values show excellent agreement with the original experiments of Ahmed over a wide range of rear slant angles (5 to 35 degrees).