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Tire aerodynamics has long been viewed as a critical area in the ongoing research on vehicle drag reduction as it is a significant contributor to the overall automotive parasitic drag. Previous wind-tunnel experiments have revealed that the flow over a rotating wheel is a very complex phenomenon. This complexity arises from the tire-ground contact patch, various points of flow separation due to the wheel geometry, and the effects of wheel rotation. These aspects make the numerical simulation of this type of flow rather challenging. Existing literature shows a number of ways, like sliding mesh, by which to simulate the flow over an isolated wheel, but the problem of finding an accurate yet cost-effective solution still remains elusive. The current paper attempts to investigate the different methodologies to emulate the wheel motion. In addition, the paper will address the influence of mesh parameters and solver setting dependency of the solution.

Cost benefit and teraflop restrictions imposed by racing sanctioning bodies make steady-state RANS CFD simulation a widely accepted first approximation tool for aerodynamics evaluations in motorsports, in spite of its limitations. Research involving generic and simplified vehicle bodies has shown that the veracity of aerodynamic CFD predictions strongly depends on the turbulence model being used. Also, the ability of a turbulence model to accurately predict aerodynamic characteristics can be vehicle shape dependent as well. Modifications to the turbulence model coefficients in some of the models have the potential to improve the predictive capability for a particular vehicle shape. This paper presents a systematic study of turbulence modeling effects on the prediction of aerodynamic characteristics of a NASCAR Gen-6 Cup racecar. Steady-state RANS simulations are completed using a commercial CFD package, STAR-CCM+, from CD-Adapco.

The demand for better fuel economy pushed by both consumer and Environmental Protection Agency (EPA), made OEMs to put more effort on other areas beside vehicle external aerodynamics. As one of these areas, under-hood aero-thermal management has taken an important role in the new road vehicle design process, due to the combination of growing engine power demands, utilization of sophisticated under-hood and underbody devices, and emission regulations. The challenge of the under-hood aerothermal management is not only due to the complexity of under-hood compartment, but also as a result of the complex heat transfer phenomena involving conduction, convention and thermal radiation. In this study, 3D CFD simulations were used to investigate the under-hood aerothermal flow features. The full vehicle model with detailed under-hood components used in this study is a Hyundai Veloster. A commercial CDF code Star-CCM+ version 11.04 from CD-adapco was used to run all the simulations.

For flows around a tire rotating over a ground plane, the Reynolds number is probably the most important parameter influencing the transition mechanism leading to flow separation from the tire surface, as it determines the viscous response of the boundary layer in the vortex-wall interaction. The present work investigates the effects of Reynolds number on an isolated tire model using a commercial Computational Fluid Dynamics (CFD) code. It validates the baseline simulation for this purpose against the Particle Image Velocimetry (PIV) data from Stanford University got using a Toyota Formula 1 race car tire model. Time-resolved velocity fields and vortex structures from the PIV data are used to correlate local and global flow phenomena to identify unsteady boundary-layer separation and the subsequent flow structures. The study will highlight the pre to post critical flow regimes where the aero coefficients and vortex structure will be studied.

When Computational Fluid Dynamics (CFD) is used in the development of road vehicles for passenger and performance use, the fidelity and numerical accuracy of the simulation are paramount as manufacturers strive to optimize the vehicle down to the single aerodynamic count. While much research has been performed on how the choices of simulation model or grid size affects the simulation results, very little has been done to investigate how the spatial decomposition of the domain amongst different nodes of a high-performance computing unit (HPC) influences the results of the simulation. As simulations grow larger, more nodes are required to reduce the simulation time, however in most commercial software this introduces a new form of error due to the accumulation of round-off errors created in the intra-node communication schemes used during iterations.

Tire modeling has been an area of major research in automotive industries as the tires cause approximately 25% of vehicle drag. With the fast-paced growth of computational resources, Computational Fluid Dynamics (CFD) has evolved as an effective tool for aerodynamic design and development in the automotive industry. One of the main challenges in the simulation of the aerodynamics of tires is the lack of a detailed and accurate experimental setup with which to correlate. In this study, the focus is on the prediction of the aerodynamics associated with an isolated rotating Formula 1 tire and brake assembly. Literature has indicated differing mechanisms explaining the dominant features such as the wake structures and unsteadiness. Limited work has been published on the aerodynamics of a realistic tire geometry with specific emphasis on advanced turbulence closures such as the Detached Eddy Simulation (DES).

Turbulence modeling and the specific boundary conditions are among the two major simulation physics setup variables that affect the prediction veracity of a CFD simulation. The existing literature is rich with studies investigating the effect of the inlet boundary conditions on the predictability of a CFD simulation, and it is recognized that the turbulence characteristics of the freestream flow, viz. the turbulence intensity and length scale, can impact the simulation results significantly, even with the same turbulence model. However, none of these studies encompasses the significance of these two boundary specifications on the CFD analysis of a realistic racecar model. Against this backdrop, the purpose of this study is to systematically investigate the effect of different freestream turbulence specifications on the CFD predictions of the aerodynamic characteristics of a latest generation NASCAR Cup racecar model.

This paper discusses a realistic approach of simulating a generic idealized car model (Ahmed body) moving in close proximity to a Side-wall using transient CFD. This phenomenon is very important in motorsports where racing very close to the safety barrier is very common. Driving in close proximity to a Side-wall alters the aerodynamic characteristics of the vehicle significantly, however, only a handful of published work exists in this area. Additionally, the experimental studies conducted in the past suffer from certain inadequacies especially in properly emulating the Side-wall, which cast some uncertainty as to their applicability to the real world. As such, the present study attempted to imitate the real world flow phenomenon by taking a non-traditional CFD approach in which the body is translated relative to the stationary surrounding fluid and Side-wall instead of the classical method of flowing air over a stationary object.

Computational Fluid Dynamics (CFD) tools play an important role in the early stages of vehicle aerothermal development. Arguably, the RANS (Reynolds Averaged Navier-Stokes) approaches are most widely used in industry due to their acceptable accuracy with affordable computational cost and faster turnaround time. In many automotive flows, RANS models cannot very accurately capture the absolute flow features or even the integral force coefficients. In spite of this, the RANS based CFD prediction results can conveniently be used to assess the magnitude and direction of a trend. However, even for such purposes, notable disagreements often exist between the flow features predicted by different RANS turbulence models. Whilst comparisons of different RANS models for various applications are abundant in literature, such evaluations on full-car models are limited, especially the evaluations of the cooling airflow inside the underhood compartment.

The radiator is the key component of a vehicle’s cooling system. The cooling effectiveness of a radiator largely depends on the flow of fresh air through it. Thus, at high vehicle speeds, the mass flow rate and flow-distribution or flow-uniformity over the radiator surface are the major operating parameters influencing the performance of a radiator. Additionally, the mass of air coming from the front grille plays an important role on the total drag of the vehicle. This paper presents computational studies aiming at improving simultaneously the efficiency of a radiator and reducing the total drag of the vehicle; this is achieved using passive aerodynamic devices that alter the flow pattern approaching the radiator. The vehicle model considered is a Hyundai Veloster and all analyses were carried out using a commercial CFD code Star-CCM+ version 10.04 by CD-adapco.

Currently, most of the Navier-Stokes equation based Computational Fluid Dynamic solvers rely heavily on the robustness of unstructured finite volume discretization to solve complex flows. Widely used finite volume solvers are restricted to second order spatial accuracy while structured finite difference codes can easily resolve up to five orders of spatial discretization and beyond. In order to solve flow around complicated geometries, unstructured finite volume codes are employed to avoid tedious and time consuming handmade structured meshes. By using overset grids and NASA's overset grid solver, Overflow, structured finite difference solutions are achievable for complex geometries such as the DrivAer [1] model. This allows for higher order flow structures to be captured as compared to traditional finite volume schemes. The current paper compares flow field solutions computed with finite volume and finite difference methods to experimental results of the DrivAer model [1].

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.

Racecar aerodynamic development demands rapid and incremental development cycles using extensive and well-correlated simulation data. The successful implementation of such a process is a major performance differentiator between race teams. Reynolds Averaged Navier-Stokes (RANS) simulations are an industry-wide tool of choice for their relatively quick turn-around times and cost-effectiveness. A limitation of RANS simulation is an inability to fully resolve flow separation and wake structures of the racecar geometry thereby reducing the accuracy of simulation and the confidence in incremental development work. However, race organizers of both Formula1 and NASCAR are placing increasing limits on aerodynamic development such as number of runs in a wind tunnel and CPU hours for CFD simulation. This prevents widespread use of LES or DES methodologies that require 5-10 times more computational resources.

In spite of growing popularity of scale resolved transient simulations, like the Detached Eddy Simulation (DES), among the mainstream automotive OEMs for the aerodynamic optimization of the production vehicles, Reynolds Averaged Navier-Stokes (RANS) simulations is still the most widely used Computational Fluid Dynamics (CFD) approach in motorsports. This is partially due to the usage-limitations imposed by the sanctioning bodies like, the FIA and NASCAR, restricting not only the hours of wind tunnel operation but also limiting the amount of CFD compute resource. This, coupled with speed requirements for aerodynamic development prevent the widespread use of scale-resolved modeling, such as Large Eddy Simulation (LES) or Detached Eddy Simulation (DES) methodologies that require an order of magnitude more computational resources.

Autonomous takeoff and landing maneuvers of an unmanned aerial vehicle (UAV) from/on a moving ground vehicle (GV) have been an area of active research for the past several years. For military missions requiring repeated flight operations of the UAV, precise landing ability is important for autonomous docking into a recharging station, since such stations are often mounted on a ground vehicle. The development of precise and efficient control algorithms for this autonomous maneuvering has two key challenges; one is related to flight aerodynamics and the other is related to a precise detection of the landing zone. The aerodynamic challenges include understanding the complex interaction of the flows over the UAV and GV, potential ground effects at the proximity of the landing surface, and the impact of the variations in the surrounding wind flow and ambient conditions.

Approximately ninety percent of automotive wind tunnels in the world have incorporated or been built with a Moving Ground Plane (MGP). However, very little research has been published in the literature on the interaction of the MGP and the vehicle. The goal of this paper is to characterize the flow structures and unsteady motion of the isolated wheel wake and its interaction with a 5-belt MGP using numerical simulations. This paper is divided into three parts. In the first part, a Computational Fluid Dynamics (CFD) study is carried out on the Mears (2004) wheel using IDDES model where the CFD process to be used later is validated against the experimental data. In the second part, a simulation is carried out for a 5-belt MGP system and the verification is carried out using the Von Karman integral formula for the boundary layer development over the belts.

This article discusses an application of Machine Learning (ML) tools to improve the prediction accuracy of Computational Fluid Dynamics (CFD) for external aerodynamic workflows. The Reynolds Averaged Navier-Stokes (RANS) approach to CFD has proved to be one of the most popular simulation methodologies due to its quick turnaround times and acceptable level of accuracy for most applications. However, in many cases the accuracy for the RANS models can prove to be suboptimal that can be significantly improved with model closure coefficient tuning. During the original turbulence model creation, these closure coefficients were chosen by somewhat ad hoc methods using simple canonical flows that do not transfer well to flows involving more complex objects, like the automotive bodies used in this work.