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Presented in this paper is a procedure to develop a high fidelity quasi steady state aerodynamic model for use in race car vehicle dynamic simulations and its application in a race vehicle multi-body full lap simulation. Developed to fit quasi steady state (QSS) wind tunnel data, the aerodynamic model is regressed against three independent variables: front ground clearance, rear ride height, and yaw angle. An initial dual range model is presented and then further refined to reduce the model complexity while maintaining a high level of predictive accuracy. The model complexity reduction decreases the required amount of wind tunnel data thereby reducing wind tunnel testing time and cost. The quasi steady state aerodynamic model for the pitch moment degree of freedom is systematically developed in this paper. This procedure is extended to the other five aerodynamic degrees of freedom to develop a complete, high fidelity, six degree of freedom quasi steady state aerodynamic model.

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.

Wind tunnel aerodynamic testing involving rolling road tire conditions can be expensive and complex to set up. Low cost rolling road testing can be implemented in a 0.3m2 Eiffel wind tunnel by modifying a horizontal belt sander to function as a moving road. This sander is equipped with steel supports to hold a steel plate against the bottom of the wind tunnel to stabilize the entire test section. These supports are bolted directly into the sander frame to ensure minimal vibrational losses or errors during testing. The wind tunnel design at the beginning of the project was encased in a wooden box which was removed to allow easier access to the test section for installation of the rolling road assembly. The tunnel was also modified to allow observers to view the testing process from various angles.

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.

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.

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.

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.