Tone and Broadband Noise Separation from Acoustic Data of a Scale-Model Counter-Rotating Open Rotor

Sree, David; Stephens, David B.

doi:10.2514/6.2014-2744

Cited by 5 publications

(3 citation statements)

References 11 publications

(18 reference statements)

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“…There are also many ways to denoise the test signal itself; FFT (Fast Fourier Transform) [10], segment-averaged method [11][12], sub-window time domain method [13], acoustic modal analysis method [14], and the Bayesian maximum likelihood method [15][16] all belongs to this class. FFT is the most basic denoising method.…”

Section: Introductionmentioning

confidence: 99%

Phase-shifted beamforming for denoising acoustic reflective signals

Chen

2024

J. vibroeng.

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When the beamforming algorithm is used to locate the fault of the internal structure, the effective fault acoustic signal will be reflected by the structure shell during its transmission process. This results in the microphone signals being polluted, and then the positioning accuracy being low. In this paper, a phase-shifted beamforming algorithm is proposed to segment the microphone signal, extract the time delay of the reflected noise by using the cross correlation property, and then subtract the reflected noise from the raw data to obtain the microphone signal with weakened reflected signal. The newly generated signals are utilized to calculate the cross-spectral matrix of beamforming, which is then used to locate vibration faults. Simulation results show that the phase-shifted beamforming algorithm can more accurately locate the vibration faults of reflective structures.

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Section: Introductionmentioning

confidence: 99%

Phase-shifted beamforming for denoising acoustic reflective signals

Chen

2024

J. vibroeng.

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“…Therefore, a solid understanding of each noise component is essential for modelling and predicting rotor noise, as well as for evaluating the efficacy of noise-mitigation strategies which can be later investigated by computational models. Only a few research works presented in the literature are conceived as tonal isolation techniques, and they include peak-finding algorithm, moving average curves, phase averaging, and broadband extrapolation procedures such as the Sree's Method [9][10][11]. An interesting example is the one of [12], in their investigation on fan noise, the authors employed a Vold-Kalman filter to extract the harmonic noise sources [13].…”

Section: Introductionmentioning

confidence: 99%

A wavelet-based separation method for tonal and broadband components of low Reynolds-number propeller noise

Meloni¹,

Paola²,

Grande³

et al. 2023

Meas. Sci. Technol.

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Propeller noise generally exhibits a rich mixture of tonal and broadband components related to different physical mechanisms. Specifically, the tones are characterized by having deterministic and persistent characteristics, while the broadband counterpart has random behaviour. The separation is essential for the experimenters as they provide information on the different noise sources. In this framework, the study presents a novel wavelet-based method able to separate the noise emitted by a low Reynolds number propeller into its tonal and broadband components. The technique is applied to an isolated rotor operating under different loading configurations, including hover and cruise conditions. The acoustic pressure data are obtained in the anechoic tunnel (A-Tunnel) of the TU Delft low-speed laboratory with a near-field polar and azimuthal distribution of microphones. The method is based upon a threshold varying procedure that separates the tonal and broadband components through the computation of two-point statistics. Advantages and drawbacks with respect to other methodologies already known from the literature are discussed. The application of the method provides the spectral content of the tonal and broadband components as well as the different polar and azimuthal directivity. Specifically, the observed dipole-like shape directivity for the tonal part and flatter broadband OASPL, confirm that the method can provide quite a good separation. Furthermore, the overall flow behaviour is inferred from the decomposition and validated through benchmarked flow visualizations.

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“…In the study, therefore, the actual acoustic power pertaining to the solely tonal and broadband components could not be properly quantified. A deeper description of the limitations of this technique is discussed by Sree [12], and Sree and Stephens [13]. A new method for processing open rotor data capable to minimize the effects of phase shifts, and thereby eliminating the spike levels in the broadband spectrum, was later presented by Sree and Stephens [14], as an improved algorithm of the original technique.…”

Section: Introductionmentioning

confidence: 99%

Wavelet-based decomposition of the tonal-broadband components of propeller noise

Meloni

Paola

Grande

et al. 2022

28th AIAA/CEAS Aeroacoustics 2022 Conference

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The present study reports a novel wavelet-based method aimed at separating the noise emitted by a single propeller into two contributions, tonal and broadband. An assessment using two different experimental investigations of propellers operating in diverse configurations is presented. The first experiment focuses upon near-field polar microphone array data of a benchmarked low-Reynolds number propeller, in hover and cruise conditions. Measurements were performed in the anechoic tunnel (A-Tunnel) at the low-speed laboratory of TU Delft. The second set of data consists of a test campaign carried out at the Pininfarina Aerodynamic and Aeroacoustic Research Center in Turin (Italy) under the EU funded project ERaCLE. The model comprises a five-bladed propeller installed close to a wing. Pressure signals were acquired using a top-mounted linear microphone array that spans different polar locations. The wavelet-based algorithm able to separate the tonal and broadband contributions through the computation of two-point statistics. The assessment of the decomposition procedure on two very different databases is presented to validate the technique with the aim to extend its range of applications.

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Tone and Broadband Noise Separation from Acoustic Data of a Scale-Model Counter-Rotating Open Rotor

Cited by 5 publications

References 11 publications

Phase-shifted beamforming for denoising acoustic reflective signals

Phase-shifted beamforming for denoising acoustic reflective signals

A wavelet-based separation method for tonal and broadband components of low Reynolds-number propeller noise

Wavelet-based decomposition of the tonal-broadband components of propeller noise

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