Development of a Test Course Selection Method for Statistically Correct Wind Modeling on US Public Roads

Traditional automotive aerodynamic development relies on wind tunnel testing and Computation Fluid Dynamics (CFD), where the former provides reliable values to be used for fuel economy calculations, and the latter enables the investigation of the flow features responsible for improvement/degradation of the averaged large-scale performances in terms of aerodynamic coefficients. The abovementioned procedure overlooks a crucial factor: natural wind. The speed of the wind encountered while driving alters the vehicle’s effective yaw angle. Such condition implies that the minimization of the drag coefficient at zero-yaw, commonly performed through wind tunnel testing and CFD simulations, may not yield real-world optimal shapes. While it is possible to employ a wind signal as an input in a wind tunnel, the signal itself must be available beforehand, and such key element is the focus of the present research effort. In this paper, we explain a methodology behind the selection of a statistically valid test course for wind data sampling on public road. Big Data from the US road traffic network and the wind distributions measured at weather stations across the country are used to build a representative country-wide distribution, which is then compared with the local distribution on the road according to the weather station data using the Kullback-Leibler divergence. The visualization of the divergence on a road map allows the selection of a suitable path for data collection, providing a sound rationale for further data processing.