Jet noise analysis by Gabor spectrogram

Qiang Xu

doi:10.1117/12.655467

17 April 2006 Jet noise analysis by Gabor spectrogram

Qiang Xu

Proceedings Volume 6247, Independent Component Analyses, Wavelets, Unsupervised Smart Sensors, and Neural Networks IV; 62470J (2006) https://doi.org/10.1117/12.655467
Event: Defense and Security Symposium, 2006, Orlando (Kissimmee), Florida, United States

Abstract

A research was conducted to determine the functions of a set of nozzle pairs. The aeroacoustical performance of these pairs can be used to analyze the deformation of structure and change of jet condition. The jet noise signal was measured by a microphone placed in the radiation field of jet flow. In addition to some traditional methods used for analyzing noise both in time and frequency domain, Gabor spectrogram is adopted to obtain the joint time-frequency pattern of the jet noise under different jet conditions from nozzles with different structures. The jet noise from three nozzle pairs worked under two types of working conditions is treated by Gabor spectrogram. One condition is both nozzles in the nozzle pair keep their structure at a fixed chamber pressure, while another condition is one of these two nozzles' throat size decreases during the jet procedure under a fixed chamber pressure. Gabor spectrograms with different orders for the jet noise under the second condition are obtained and compared. Then a rational order is selected in analyzing the jet noise. Results are presented in this paper. The Gabor spectrogram patterns of these two conditions are with marked difference. The noise keeps its frequency peak during the whole jet procedure in the first condition. But there is a frequency peak shift in the second condition at a certain size of throat. The distribution of frequency peak along with the decrease of throat presents two states. This would be helpful for nozzle structure recognition.

Citation Download Citation

Qiang Xu "Jet noise analysis by Gabor spectrogram", Proc. SPIE 6247, Independent Component Analyses, Wavelets, Unsupervised Smart Sensors, and Neural Networks IV, 62470J (17 April 2006); https://doi.org/10.1117/12.655467

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KEYWORDS

Throat

Interference (communication)

Time-frequency analysis

Analytical research

Signal to noise ratio

Fourier transforms

Rockets

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