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Beamforming techniques are widely used today in aeroacoustic wind tunnels to identify wind noise sources generated by interaction between incoming flow and the test object. In this study, a planar spiral microphone array with 120 channels was set out-of-flow at 1:1 aeroacoustic wind tunnel of Shanghai Automotive Wind Tunnel Center (SAWTC) to test exterior wind noise sources of a production car. Simultaneously, 2 reference microphones were set in vehicle interior to record potential sound source signal near the left side view mirror triangle and the signal of driver’s ear position synchronously. In addition, a spherical array with 48 channels was set inside the vehicle to identify interior noise sources synchronously as well. With different correlation methods and an advanced algorithm CLEAN-SC, the ranking of contributions of vehicle exterior wind noise sources to interested interior noise locations was accomplished.

By means of particle image velocimetry(PIV) measurements, this paper uses vortex identification algorithms to find and analyze the coherent structures in the shear layer region of a 1:15 scaled 3/4 open jet automotive wind tunnel with a high Reynolds number(about 106), referring to SAWTC’s AAWT. The proper orthogonal decomposition(POD) is used to process the PIV experimental data to reconstruct the velocity fields. Based on the vortex identification functions, the locations of the center, the rotation direction and the radius of vortex can be computed. Furthermore, this paper uses the statistical method to study the regularities of distribution of these vortexes in a two-dimensional plane, and identify the vortex pairing process in the shear layer region. This paper also chooses different vortex identification algorithms to find the most accurate and suitable algorithms.