Use Of Digital Wind Tunnel For Improved Aerodynamic Prediction

In the automotive industry, external aerodynamic evaluations in digital environments are commonly conducted using simplified, large box tunnels with vehicle being static. These approaches are computationally efficient and ensure faster turnaround times. To closely replicate physical wind tunnel testing or real-world conditions, these simulations are often augmented with moving ground and rolling tire configurations. While such setups provide valuable directional feedback for aerodynamic drag improvements, they frequently exhibit significant discrepancies when compared to physical wind tunnel test data. It is observed that key factors such as wind tunnel blockage effects, boundary layer suctions, when not properly accounted for, distort the local flow field dynamics and introduce errors in the simulations. With OEMs aiming to accelerate time-to-market for new vehicle launches, many aspire to minimize reliance on physical testing and maximize use of digital methods for design sign-off. Achieving this requires the development of robust digital processes that deliver accurate result predictions rather than merely directional design insights. This paper investigates various factors, including modeling strategies and boundary conditions to enhance the reliability of 3D CFD simulation models for aerodynamic predication. It explores the potential of creating a 'digital twin'—a virtual replica of actual wind tunnel—to replicate test environments around the vehicle. The study also emphasizes the significance of accurately modeling an empty wind tunnel as a foundational step in digital twin development. Finally, the paper validates the methodology by comparing results for multiple vehicles against physical test data, focusing on aerodynamic drag coefficients, pressure maps, and tomography data across different configurations enhancing confidence in the digital aerodynamic evaluation process.