Insight into the Aerodynamics of Race and Idealized Road Vehicles Using Scale-Resolved and Scale-Averaged CFD Simulations
Aerodynamics has long been perceived as the single most important aspect amongst all factors that contribute to the on-track performance of a racecar. As such, in all forms of motorsports, race teams dedicate a significant portion of their budget and efforts to aerodynamic development. As track testing of racecars is cost prohibitive and is mostly controlled by the sports sanctioning bodies, wind-tunnel testing and Computational Fluid Dynamics (CFD) are the commonly used tools in racecar aero development. However, in an effort to ensure level playing fields, race sanctioning bodies introduced limits on how much wind tunnel time or CFD resources each team can utilize in its aerodynamic development. For CFD, the implication of these caps means that the solution turn around time, accuracy and reliability must be improved to overcome the challenges caused by the imposed resource-utilization-restrictions. In order to achieve the goal of finding a fast, yet reliable CFD methodology, this project presents the development of a Reynolds-Averaged Navier Stokes (RANS) CFD framework for NASCAR Cup stock-racecars using a Scale Averaged (SAS) approach based on the SST k-ω turbulence model. The methodology development process involves a thorough understanding of the effects of solver parameters, closure coefficients, and boundary conditions on the prediction veracity. The prediction accuracy is validated against test data obtained from a closed-return, open-jet rolling-road wind tunnel for a range of racecar on-track operating conditions, such as the changes in ride-heights and yaw. Results using the CFD framework presented in this dissertation achieved a correlation of ~98% with the wind-tunnel lift and drag measurements data over a range of operating conditions. However, existing literature suggests that the scale and time-resolved (or SRS) Detached Eddy Simulation (DES) approach produces a better overall flow field predictions for simplified road vehicles, such as the Ahmed body. The aerodynamic characteristics of racecars are starkly different even from the passenger vehicles, let alone the simplified vehicles. As a study comparing the effectiveness of SAS and SRS approaches in flow-filed predictions is not available in existing literature, this work also investigates the aerodynamics of a stock-racecar using Improved Delayed DES (IDDES). This study finds that the IDDES resolved a range of finer vortical structures that are almost entirely missed by the RANS approach. To better understand the roles of these vortices on the aero characteristics of the race car, spectral analyses of the aerodynamic forces and moments are carried out. The distribution of Power Spectral Density (PSD) is found to be largely independent of the operating conditions. This implies that the dominant modes stemmed from the racecar geometry with a ramification that an understanding on the contribution to the dominant energy modes by different race-car geometry components would be very beneficial for performance improvement. To identify the dominant modes, a more advanced and informative modal decomposition tool is required. The Dynamic Mode Decomposition (DMD), which was seen in existing literature to be a very effective tool for low Reynolds number flows around canonical geometries, is considered to be an ideal candidate. However, due to the nonavailability of the volume of flow-field data required to train a DMD algorithm for the flows past NASCAR race-cars, the process is sought to develop using a simplified road vehicle, the Ahmed body. When the DMD algorithm from the existing literature was applied to the high Reynolds number, separation-dominated flow past an Ahmed body, the DMD reconstruction of the flow field suffered nonphysical dampening of the medium-to-high frequency modes. To circumvent this, a modified DMD algorithm is proposed in this work which involves introduction of a mode filtration process. The proposed DMD algorithm is found to be very effective in both flow-field reconstruction and predictions of the future state.