Effects of Angle of Attack and Velocity on Trailing Edge Noise

Hutcheson, Florence V.; Brooks, Thomas F.

doi:10.2514/6.2004-1031

Cited by 38 publications

(23 citation statements)

References 22 publications

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“…Here again, a quantitative characterization of the pressure waves has not been performed. Hutcheson and Brooks (2004) predicted and measured the noise of a NACA 63-215 airfoil with different trailing edge thicknesses at very low Mach numbers (M ∞ ≤ 0.17, Re c ≤ 1.6 × 10 6 , −6.2 • ≤ α ≤ 8.8 • ). In this Mach number range, they found the scaled amplitude of the turbulent boundary layer (TBL) noise to decrease with increasing Mach number, whereas the scaled amplitude for the blunt trailing edge (BTE) noise could be scaled with the Strouhal number.…”

Section: List Of Symbolsmentioning

confidence: 99%

“…This broadening was also observed by Herr and Dobrzynski (2005) during their experimental investigation on a flat plate with a blunt trailing edge in an acoustic wind tunnel. The Strouhal number of the experiments of Hutcheson and Brooks (2004) was determined as St d = 0.1 based on the trailing edge thickness. They further mentioned an influence of the shape and the taper of the BTE on the noise.…”

Section: List Of Symbolsmentioning

confidence: 99%

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Investigation of wave phenomena on a blunt airfoil with straight and serrated trailing edges

et al. 2015

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Section: List Of Symbolsmentioning

confidence: 99%

Section: List Of Symbolsmentioning

confidence: 99%

Investigation of wave phenomena on a blunt airfoil with straight and serrated trailing edges

et al. 2015

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“…For this no-flow case, the convective and shear layer refraction terms are absent in the steering vector definition, Eqs. (4) and (5). The right frame of Fig.…”

Section: Experimental Applicationsmentioning

confidence: 99%

“…Quantitative measurements for distributed sources of slat noise 3 were achieved using an array and specially tailored weighting functions that matched array beampatterns with knowledge of the line source type distribution for slat noise. Similar measurements for distributed trailing edge noise 4,5 and leading edge noise (due in this case to grit boundary layer tripping) were made along with special COP methodologies involving microphone groups.…”

Section: Introductionmentioning

confidence: 99%

A Deconvolution Approach for the Mapping of Acoustic Sources (DAMAS) Determined from Phased Microphone Arrays

Brooks

Humphreys

2004

10th AIAA/CEAS Aeroacoustics Conference

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130

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Current processing of acoustic array data is burdened with considerable uncertainty. This study reports an original methodology that serves to demystify array results, reduce misinterpretation, and accurately quantify position and strength of acoustic sources. Traditional array results represent noise sources that are convolved with array beamform response functions, which depend on array geometry, size (with respect to source position and distributions), and frequency. The Deconvolution Approach for the Mapping of Acoustic Sources (DAMAS) method removes beamforming characteristics from output presentations. A unique linear system of equations accounts for reciprocal influence at different locations over the array survey region. It makes no assumption beyond the traditional processing assumption of statistically independent noise sources. The full rank equations are solved with a new robust iterative method. DAMAS is quantitatively validated using archival data from a variety of prior high-lift airframe component noise studies, including flap edge/cove, trailing edge, leading edge, slat, and calibration sources. Presentations are explicit and straightforward, as the noise radiated from a region of interest is determined by simply summing the mean-squared values over that region. DAMAS can fully replace existing array processing and presentations methodology in most applications. It appears to dramatically increase the value of arrays to the field of experimental acoustics.

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“…Upon converting signals to the frequency domain, this "background only" acquisition is subtracted (on a pressure-squared basis) from that of the primary acquisition (signal plus noise). With the introduction of beamforming using microphone arrays, this same principle has been applied to Cross-Spectral Matrices (CSMs) since the late 1990s [16][17][18][19][20]. Both techniques rely on the cancellation of the noise in its statistical sense, since the background noise present will not be identical during two acquisitions separated in time.…”

Section: Introductionmentioning

confidence: 99%

A Background Noise Reduction Technique using Adaptive Noise Cancellation for Microphone Arrays

Spalt

Fuller

Brooks

et al. 2011

17th AIAA/CEAS Aeroacoustics Conference (32nd AIAA Aeroacoustics Conference)

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Background noise in wind tunnel environments poses a challenge to acoustic measurements due to possible low or negative Signal to Noise Ratios (SNRs) present in the testing environment. This paper overviews the application of time domain Adaptive Noise Cancellation (ANC) to microphone array signals with an intended application of background noise reduction in wind tunnels. An experiment was conducted to simulate background noise from a wind tunnel circuit measured by an out-of-flow microphone array in the tunnel test section. A reference microphone was used to acquire a background noise signal which interfered with the desired primary noise source signal at the array. The technique's efficacy was investigated using frequency spectra from the array microphones, array beamforming of the point source region, and subsequent deconvolution using the Deconvolution Approach for the Mapping of Acoustic Sources (DAMAS) algorithm. Comparisons were made with the conventional techniques for improving SNR of spectral and Cross-Spectral Matrix subtraction. The method was seen to recover the primary signal level in SNRs as low as -29 dB and outperform the conventional methods. A second processing approach using the center array microphone as the noise reference was investigated for more general applicability of the ANC technique. It outperformed the conventional methods at the -29 dB SNR but yielded less accurate results when coherence over the array dropped. This approach could possibly improve conventional testing methodology but must be investigated further under more realistic testing conditions. = data window weighting constant x n = reference input at step n y n = FIR filter output at step n z n = ANC output at step n = coherence ε n = ANC error at step n μ = LMS algorithm step size τ = time delay τ m = propagation time from grid point to microphone m

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Effects of Angle of Attack and Velocity on Trailing Edge Noise

Cited by 38 publications

References 22 publications

Investigation of wave phenomena on a blunt airfoil with straight and serrated trailing edges

Investigation of wave phenomena on a blunt airfoil with straight and serrated trailing edges

A Deconvolution Approach for the Mapping of Acoustic Sources (DAMAS) Determined from Phased Microphone Arrays

A Background Noise Reduction Technique using Adaptive Noise Cancellation for Microphone Arrays

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