Wheels and tires account for approximately 25% of the overall aerodynamic drag on a vehicle. Though researchers have investigated the accurate representation of rotating tires and wheels in aerodynamic simulations, they primarily focused on the differences in the tire or wheel geometry; few studies have investigated the effects of front-tire deflectors located at the bottom of passenger car bumpers. In other that deflectors can effectively reduce drag without significantly affecting design or packaging, deflector performance should be predicted at the early stages of product development. This study accordingly clarified the simulation conditions for full-vehicle aerodynamics necessary to accurately predict the performance of front-tire deflectors by simulating two different deflector configurations under four conditions comprising different degrees of tire geometry detail and wheel rotation methods. The simulation results were subsequently compared with wind tunnel test results, indicating that the numerical simulation using the least accurate tire geometry detail could not accurately predict the performance differences according to deflector configuration. However, the differences between the drag coefficients and airflow characteristics for each deflector were predicted more accurately by improving the tire geometry detail. The prediction accuracy was further improved by using the sliding mesh method instead of the boundary condition method to model the wheel rotation. Therefore, it was concluded that the detail tire geometry and wheel rotation method are important factors for improving the accuracy of front-tire deflector performance prediction.

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Automated driving safety evaluation predominantly relies on scenario-based approaches. In this study, the authors adopt a functional scenario catalogue initially conceived by JAMA to evaluate automated driving safety on limited access highways. The potential of this catalogue to cover real-world crashes was investigated by comparing each scenario in the catalogue with crash patterns from two international data sources: the 2007 NHTSA’s pre-crash scenario typology for crash avoidance research report, and the 2020 IGLAD’s codebook. The results indicate the potential of the scenario catalogue to comprehensively cover both the NHTSA and the IGLAD crash scenario typologies.

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