Hiroshi Takata, Takuo Nishi, Hyungseok Kook, Gregory Moebs, Patricia Davies and J. Stuart Bolton, “Visualization techniques to identify and quantify sources and paths of exterior noise radiated from stationary and nonstationary vehicles,” Society of Automotive Engineers of Korea, Technical Paper 2000-05-0326, Seoul, Korea, June 2000. Also: Seoul 2000 FISITA World Automotive Congress, Paper F2000H227,June 12-15, 2000, Seoul, Korea.


In recent years, Nearfield Acoustical Holography (NAH) has been used to identify stationary vehicle exterior noise sources. However that application has usually been limited to individual components. Since powertrain noise sources are hidden within the engine compartment, it is difficult to use NAH to identify those sources and the associated partial fields that combine to create the complete exterior noise field of a motor vehicle. Integrated Nearfield Acoustical Holography (INAH) has been developed to address these concerns: it is described here. The procedure entails sensing the sources inside the engine compartment by using an array of reference microphones, and then calculating the associated partial radiation fields by using NAH. In the second part of this paper, the use of farfield arrays is considered. Several array techniques have previously been applied to identify noise sources on moving vehicles. However use of those procedures has been restricted to the constant velocity case. Here, spherical beamforming was used to visualize sound radiation during a conventional vehicle passby test: i.e., during full throttle acceleration. First, forward and backward propagation procedures are compared. A spherical spreading correction factor is described, along with a maximum likelihood procedure for obtaining an optimal array weighting dependent on the relative distance between the microphones and the focus point. The de-Dopplerized microphone outputs are multiplied by weighting factors and summed to yield the source strengths over a reconstruction plane attached to the vehicle.


NAH, SVD, spherical beamforming, maximum likelihood estimation

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