3D Beamforming Algorithm for Vehicle Aeroacoustic Investigation

The significant reduction in automotive powertrain noise, due to electric vehicles and stringent emissions regulations, has highlighted wind noise and its mitigation as a key area of automotive development. Consequently, the precise identification of aeroacoustic noise sources is vital for enhancing vehicle acoustic comfort in a cost-effective and efficient manner, particularly as many of the noise sources now observed were once masked by powertrain sounds. Currently, beamforming techniques, which utilize one or more microphone arrays to detect noise sources on a virtual plane near the vehicle, are the industry standard for external aeroacoustic assessment. However, assuming that all noise sources reside on this defined plane can lead to inaccuracies in estimating the intensity and location of off-plane sources. To address this limitation, Pininfarina has implemented a multi-plane approach for its overhead microphone array. This strategy defines several planes that more accurately capture the vehicle’s vertical dimensions, with the natural evolution of this method being the direct mapping of noise sources onto a three-dimensional scan of the vehicle. This thesis aims to develop a new algorithm that merges data from three arrays into a single acoustic map of the vehicle's surface using a Multiplicative Beamforming technique. This approach minimizes localization errors and inaccuracies in source strength caused by focus deviations.