Analysis of jet–airfoil interaction noise sources by using a microphone array technique

Davy, Renaud

doi:10.1016/j.jsv.2015.11.027

Cited by 26 publications

(14 citation statements)

References 32 publications

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“…The array processing methods used by ONERA in JERONIMO are derived from the well-known DAMAS and DAMAS-C microphone array techniques see Ref. [1,2], and has been more detailed in previous papers 4,5 . Whatever the methodology is, the task consists in finding the optimal representation of noise sources reproducing the measurements, in particular the microphone cross-spectral matrix, as closely as possible.…”

Section: A Deconvolution Algorithmsmentioning

confidence: 99%

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Installed jet noise source analysis by microphone array processing

Davy¹,

Mortain²,

Huet³

et al. 2019

25th AIAA/CEAS Aeroacoustics Conference

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This paper presents the identification of jet noise sources carried out at ONERA in the framework of the European Project JERONIMO. A dedicated test campaign was conducted in the anechoic wind tunnel CEPRA19 for a UHBR dual-stream nozzle with and without wing model installed. Arrays of microphone were implemented in order to apply advanced processing for noise source identification. The analysis focused on the wing installation effects. Correlated monopoles model was used in order to identify the different noise sources radiation occurring in installed jet configurations. The results projected in far field have been assessed with the farfield measurement. The acoustic localization processing applied to the numerical simulation demonstrates a good agreement with the experiments. Tonal jet-flap interaction noise is also highlighted, showing strong correlation between source at bypass exit and flap trailing edge.

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Section: A Deconvolution Algorithmsmentioning

confidence: 99%

“…known to be a good operator for the localization of noise sources 6 . An extension to correlated noise sources was proposed by Fleury 5 . This operator is used to define the weighting function W. The regularization parameter  is optimized from the L-curve method 7 .…”

Section: A Deconvolution Algorithmsmentioning

confidence: 99%

Installed jet noise source analysis by microphone array processing

Davy¹,

Mortain²,

Huet³

et al. 2019

25th AIAA/CEAS Aeroacoustics Conference

Self Cite

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“…Moreover, a target sound source can be located by receiving the sound source signal and applying an algorithm. Microphone arrays [8][9][10][11] perform functions such as noise elimination and target tracking. They can also be used to passively receive sound source signals, making it practical for researchers to collect signals.…”

Section: Introductionmentioning

confidence: 99%

Sound Source Localization Fusion Algorithm and Performance Analysis of a Three-Plane Five-Element Microphone Array

Xing

Yang

2019

Applied Sciences

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To reduce the negative effect on sound source localization when the source is at an extreme angle and improve localization precision and stability, a theoretical model of a three-plane five-element microphone array is established, using time-delay values to judge the sound source’s quadrant position. Corresponding judgment criteria were proposed, solving the problem in which a single-plane array easily blurs the measured position. Based on sound source geometric localization, a formula for the sound source azimuth calculation of a single-plane five-element microphone array was derived. The sinusoids and cosines of two elevation angles based on two single-plane arrays were introduced into the sound source spherical coordinates as composite weighted coefficients, and a sound source localization fusion algorithm based on a three-plane five-element microphone array was proposed. The relationship between the time-delay estimation error, elevation angle, horizontal angle, and microphone array localization performance was discussed, and the precision and stability of ranging and direction finding were analyzed. The results show that the measurement precision of the distance from the sound source to the array center and the horizontal angle are improved one to threefold, and the measurement precision of the elevation angle is improved one to twofold. Although there is a small error, the overall performance of the sound source localization is stable, reflecting the advantages of the fusion algorithm.

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“…Array-based beamforming has become an indispensable acoustic source identification technology in some industries such as aviation aircraft, [1][2][3][4] rail train, 5 wind turbine, [6][7][8][9] mining industry, 10 and automobile [11][12][13] over decades. Conventional beamforming (CB) has the advantages of fast measurement speed, high computational efficiency, and suitable for medium-to-long distance measurement.…”

Section: Introductionmentioning

confidence: 99%

A novel Fourier-based deconvolution algorithm with improved efficiency and convergence

Shen

Chu

Zhang

et al. 2019

Journal of Low Frequency Noise, Vibration and Active Control

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Various deconvolution algorithms for acoustic source are developed to improve spatial resolution and suppress sidelobe of the conventional beamforming. To improve the computational efficiency and solution convergence of deconvolution, this paper proposes a Fourier-based improved fast iterative shrinkage thresholding algorithm. Simulations and experiments show that Fourier-based improved fast iterative shrinkage thresholding algorithm can achieve excellent acoustic identification performance, with high computational efficiency and good convergence. For Fourier-based improved fast iterative shrinkage thresholding algorithm, the larger the weight coefficient, the narrower the mainlobe width, and the better the convergence, but the spurious source also increases. The recommended weight coefficient for the array described herein is 3. In addition, like other Fourier-based deconvolution algorithms, Fourier-based improved fast iterative shrinkage thresholding algorithm using irregular focus grid can obtain better acoustic source identification performance than using the conventional regular focus grid.

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Analysis of jet–airfoil interaction noise sources by using a microphone array technique

Cited by 26 publications

References 32 publications

Installed jet noise source analysis by microphone array processing

Installed jet noise source analysis by microphone array processing

Sound Source Localization Fusion Algorithm and Performance Analysis of a Three-Plane Five-Element Microphone Array

A novel Fourier-based deconvolution algorithm with improved efficiency and convergence

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