The automotive aerodynamic development relies on wind tunnel testing and Computational Fluid Dynamics (CFD), where the former provides reliable values to be used for fuel economy calculations, and the latter enables the investigation of flow features responsible for improvement/degradation of the average large-scale performances in terms of aerodynamic coefficients.
The abovementioned procedure overlooks a crucial factor however: natural wind. The speed and the direction of natural 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 and CFD simulations in an industrial context, may not yield real-world optimal shapes.
While it is possible to reproduce natural wind-like conditions in a wind tunnel using flaps, for example, the input signal to the flap system must be available beforehand, and such key element is the focus of the present research effort. In this paper, we introduce a methodology behind the selection of a statistically valid test course for wind data sampling on US public roads.
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. The visualization of the divergence on a road map allows the selection of a suitable course for data collection, providing a sound rationale for downstream fuel economy calculation tasks.