Abstract
In this study, the propagation time and attenuation rate distributions of each sound source grid point (200 × 200) to a microphone of an arbitrary position across the shear layer, which are required for beamforming, were reconstructed by the reduced-order model with sparse sampling for acceleration of the computation. First, the propagation time and attenuation rate distributions, including the refraction of sound by the shear layer were calculated over 100 patterns of combinations of the wind speed and the microphone position, as training data. The dominant modes and optimum sampling points were discovered from the training data. Subsequently, data-driven sparse sampling for reconstruction was applied and the propagation time and the attenuation rate from each grid point (200 × 200) to a microphone were quickly calculated for the given microphone position and wind speed. The error of the obtained calculation result is 1% or less, and the approximation by data-driven sparse sampling is concluded to be effective.
Highlights
Aerodynamic noise that is generated from aircrafts and automobiles may lead to environmental problems
This suggests that the relationship between the sound sources and microphone array can be expressed by the low-dimensional model, and it may be possible to estimate all of the steering vectors between microphones and sound source locations by just calculating them at appropriate sparse sampling points
This study proposed a high-speed algorithm for the calculation of the propagation time and attenuation rate distributions of the acoustic waves, which is applicable to onsite quick check of the aeroacoustic measurement in wind tunnel testing
Summary
Aerodynamic noise that is generated from aircrafts and automobiles may lead to environmental problems. In the case of beamforming, the parameters of the positions, the freestream velocity, and the temperature change the relationship between the microphones and sound sources, but the changes in the relationship seem to have some patterns This suggests that the relationship between the sound sources and microphone array can be expressed by the low-dimensional model, and it may be possible to estimate all of the steering vectors between microphones and sound source locations by just calculating them at appropriate sparse sampling points. This method requires training data calculation, but it can be conducted before the experiments. The acceleration of the computational time and the estimation accuracy of the proposed method are evaluated
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