Archives of Acoustics, 47, 2, pp. 241-257, 2022

Research on Airflow Background Noise Suppression for Aeroacoustic Wind Tunnel Testing

Yuanwen LI
University of Science and Technology Beijing

Min LI
University of Science and Technology Beijing

Daofang FENG
University of Science and Technology Beijing

Debin YANG
University of Science and Technology Beijing

Long WEI
Beijing Institute of Structure and Environment Engineering

The microphone data collected in aeroacoustic wind tunnel test contains not only desired aeroacoustic signal but also background noise generated by the jet or the valve of the wind tunnel, so the desired aeroacoustic characteristics is difficult to be highlighted due to the low Signal-to-Noise Ratio (SNR). Classical cross spectral matrix removal can only reduce the microphone self-noise, but its effect is limited for jet noise. Therefore, an Airflow Background Noise Suppression method based on the Ensemble Empirical Mode Decomposition (ABNSEEMD) is proposed to eliminate the influence of background noise on aeroacoustic field reconstruction. The new method uses EEMD to adaptively separate the background noise in microphone data, which has good practicability for increasing SNR of aeroacoustic signal. A localization experiment was conducted by using two loudspeakers in wind tunnel with 80 m/s velocity. Results show that proposed method can filter out the background noise more effectively and improve the SNR of the loudspeakers signal compared with spectral subtraction and cepstrum methods. Moreover, the aeroacoustic field produced by a NACA EPPLER 862 STRUT airfoil model was also measured and reconstructed. Delay-and-sum beamforming maps of aeroacoustic source were displayed after the background noise was suppressed, which further demonstrates the proposed method’s advantage.
Keywords: aeroacoustic measurement; acoustic source localization; EEMD; background noise suppression; wind tunnel test.
Full Text: PDF


Bahr C., Li J., Cattafesta L. (2011), Aeroacoustic measurements in open-jet wind tunnels – an evaluation of methods applied to trailing edge noise, 17th AIAA/CEAS Aeroacoustics Conference (32nd AIAA

Aeroacoustics Conference), Portland, Oregon, USA, Paper No. 2011–2771, doi: 10.2514/6.2011-2771.

Bahr C.J., Cattafesta L.N. (2016), Wavenumber–frequency deconvolution of aeroacoustic microphone phased array data of arbitrary coherence, Journal of Sound and Vibration, 382: 13–42, doi: 10.1016/j.jsv.2016.06.044.

Bahr C.J., Horne W.C. (2015), Advanced background subtraction applied to aeroacoustic wind tunnel testing, [in:] 21st AIAA/CEAS Aeroacoustics Conference, p. 3272, doi: 10.2514/ 6.2015-3272.

Bahr C.J., Horne W.C. (2017), Subspace-based background subtraction applied to aeroacoustic wind tunnel testing, International Journal of Aeroacoustics, 16(4–5): 299–325, doi: 10.1177/1475472x17718885.

Beck T.W. et al. (2005), Comparison of Fourier and wavelet transform procedures for examining the mechanomyographic and electromyographic frequency domain responses during fatiguing isokinetic muscle actions of the biceps brachii, Journal of Electromyography and Kinesiology, 15(2): 190–199, doi: 10.1016/


Bin F., Lei X. (2020), The combination of spectrum subtraction and cross-power spectrum phase method for time delay estimation, Archives of Acoustics, 45(3): 453–458, doi: 10.24425/aoa.2020.134061.

Blacodon D. (2011), Array processing for noisy data: Application for open and closed wind tunnels, AIAA Journal, 49(1): 55–66, doi: 10.2514/1.J050006.

Blacodon D., Bulté J. (2014), Reverberation cancellation in a closed test section of a wind tunnel using a multi-microphone cesptral method, Journal of Sound and Vibration, 333(9): 2669–2687, doi: 10.1016/j.jsv.2013.12.012.

Boll S. (1979), Suppression of acoustic noise in speech using spectral subtraction, IEEE Transactions on Acoustics, Speech, and Signal Processing, 27(2): 113–120, doi: 10.1109/TASSP.1979.1163209.

Boonkla S., Unoki M., Wutiwiwatchai C., Makhanov S.S. (2017), F0 estimation using empirical mode decomposition and complex cepstrum analysis in reverberant environments, 2017 Asia–Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), Kuala Lumpur, Malaysia, December 12–15, Paper No. 17562827, doi: 10.1109/apsipa.2017.8282165.

Chen W., Wang S., Zhang Z., Chuai X. (2012), Noise reduction based on wavelet threshold filtering and ensemble empirical mode decomposition, SEG Technical Program Expanded Abstracts, September 01, Paper No. 4609, doi: 10.1190/segam2012-0567.1.

Chiariotti P., Martarelli M., Castellini P. (2019), Acoustic beamforming for noise source localization–Reviews, methodology and applications, Mechanical Systems and Signal Processing, 120: 422–448, doi: 10.1016/j.ymssp.2018.09.019.

Chong T.P., Dubois E. (2016), Optimization of the poro-serrated trailing edges for airfoil broadband noise reduction, The Journal of the Acoustical Society of America, 140(2): 1361–1373, doi: 10.1121/1.4961362.

Chong T.P., Joseph P.F., Kingan M.J. (2013), An investigation of airfoil tonal noise at different Reynolds numbers and angles of attack, Applied Acoustics, 74(1): 38–48, doi: 10.1016/j.apacoust.2012.05.016.

Di Marco A. et al. (2019), Airframe noise measurements in a large hard-walled closed-section wind tunnel, Applied Acoustics, 146: 96–107, doi: 10.1016/j.apacoust.2018.11.006.

Fischer J., Doolan C. (2020), An improved eigenvalue background noise reduction method for acoustic beamforming, Mechanical Systems and Signal Processing, 140: 106702, doi: 10.1016/j.ymssp.2020.106702.

Fu Q., Wei L., Yang D., Li M. (2014), Reflected acoustic wave suppression method based on the cepstrum clip and its application in noise field reconstruction, [in Chinese] Chinese Journal of Engineering, 36(6): 845–854, doi: 10.13374/j.issn1001-053x.2014.06.020.

Hald J. (2017), Removal of incoherent noise from an averaged cross-spectral matrix, The Journal of the Acoustical Society of America, 142(2): 846–854, doi: 10.1121/1.4997923.

Hald J., Ginn K.B. (2019), Cross-spectral matrix denoising for beamforming in wind tunnels, [in:] INTERNOISE and NOISE-CON Congress and Conference Proceedings, Vol. 259, No. 6, pp. 3516-3527, Madrid, Spain, June 16–19.

Huang N.E. et al. (1998), The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis, Proceedings of the Royal Society of London. Series A, 454(1971): 903–995, doi: 10.1098/rspa.1998.0193.

Huang X. (2011), Real-time location of coherent sound sources by the observer-based array algorithm, Measurement Science and Technology, 22(6): 065501, doi: 10.1088/0957-0233/22/6/065501.

Humphrey N.J., Edgington-Mitchell D. (2016), The effect of low lobe count chevron nozzles on supersonic jet screech, International Journal of Aeroacoustics, 15(3): 294–311, doi: 10.1177/1475472x16630872.

Kingan M.J., Pearse J.R. (2009), Laminar boundary layer instability noise produced by an aerofoil, Journal of Sound and Vibration, 322(4–5): 808–828, doi: 10.1016/j.jsv.2008.11.043.

Koop L., Ehrenfried K. (2008), Microphone-array processing for wind-tunnel measurements with strong background noise, 14th AIAA/CEAS Aeroacoustics Conference (29th AIAA Aeroacoustics Conference), Vancouver, British Columbia, Canada, May 5–7, Paper

No. 2008-2907, doi: 10.2514/6.2008-2907.

Lee I., Zhang Y., Lin D. (2018), A model-scale test on noise from single-stream nozzle exhaust geometries in static conditions, Chinese Journal of Aeronautics, 31(12): 2206–2220, doi: 10.1016/j.cja.2018.08.001.

Li Y., Wang X., Zhang D. (2013), Control strategies for aircraft airframe noise reduction, Chinese Journal of Aeronautics, 26(2): 249–260, doi: 10.1016/j.cja.2013.02.001.

Liu P., Xing Y., Guo H., Li L. (2017), Design and performance of a small-scale aeroacoustic wind tunnel, Applied Acoustics, 116: 65–69, doi: 10.1016/j.apacoust.2016.09.014.

Liu W.Y., Tang B.P., Han J.G., Lu X.N., Hu N.N., He Z.Z. (2015), The structure healthy condition monitoring and fault diagnosis methods in wind turbines: A review, Renewable and Sustainable Energy Reviews, 44: 466–472, doi: 10.1016/j.rser.2014.12.005.

Luesutthiviboon S., Malgoezar A.M.N., Merino-Martinez R., Snellen M., Sijtsma P., Simons D.G. (2019), Enhanced HR-CLEAN-SC for resolving multiple closely spaced sound sources, International Journal of Aeroacoustics, 18(4–5): 392–413, doi: 10.1177/1475472X19852938.

Mariyappa N. et al. (2014), Baseline drift removal and denoising of MCG data using EEMD: role of noise amplitude and the thresholding effect, Medical Engineering & Physics, 36(10): 1266–1276, doi: 10.1016/j.medengphy.2014.06.023.

Merino-Martínez R. et al. (2018), Comparison between analog and digital phased microphone arrays for aeroacoustic measurements, AIAA/CEAS Aeroacoustics Conference, Atlanta, Georgia, USA, June. 25–29, Paper No. 2018–2809, doi: 10.2514/6.2018-2809.

Merino-Martínez R. et al. (2019), A review of acoustic imaging methods using phased microphone arrays, CEAS Aeronautical Journal, 10(1): 197–230, doi: 10.1007/s13272-019-00383-4.

Mimani A., Fischer J., Moreau D.J., Doolan C.J. (2018), A comparison of time-reversal and crossspectral beamforming for localizing experimental rodairfoil interaction noise sources, Mechanical Systems and Signal Processing, 111: 456–491, doi: 10.1016/j.ymssp.2018.03.029.

Murayama M., Nakakita K., Yamamoto K., Ura H., Ito Y., Choudhari M.M. (2014), Experimental study on slat noise from 30p30n three-element high-lift airfoil at JAXA hard-wall lowspeed wind tunnel, 20th AIAA/CEAS Aeroacoustics Conference, Atlanta, GA, USA, June. 16–20, Paper No. 2014–2080, doi: 10.2514/6.2014-2080.

Pagani Jr C.C., Souza D.S., Medeiros M.A. (2016), Slat noise: aeroacoustic beamforming in closed-section wind tunnel with numerical comparison, AIAA Journal, 54(7): 2100–2115, doi: 10.2514/1.J054042.

Pan X., Wu H., Jiang W. (2019), Multipole orthogonal beamforming combined with an inverse method for coexisting multipoles with various radiation patterns, Journal of Sound and Vibration, 463: 114979, doi: 10.1016/j.jsv.2019.114979.

Paruchuri C. et al. (2017), Performance and mechanism of sinusoidal leading edge serrations for the reduction of turbulence-aerofoil interaction noise, Journal of Fluid Mechanics, 818: 435–464, doi: 10.1017/jfm.2017.141.

Porteous R., Prime Z., Doolan C.J., Moreau D.J., Valeau V. (2015), Three-dimensional beamforming of dipolar aeroacoustic sources, Journal of Sound and Vibration, 355: 117–134, doi: 10.1016/j.jsv.2015.06.030.

Qiao W.Y., Ji L., Tong F., Wang L.F., Chen W.J. (2018), Separation and quantification of airfoil LEand TE-noise source with microphone array, 7th Berlin Beamforming Conference, Berlin, Germany, March 5–8, Paper No. BeBeC2018 D-14.

Snakowska A., Idczak H. (2008), Prediction of turbofan engine noise considering diffraction at the duct outlet, Archives of Acoustics, 33(4): 129–134.

Spalt T., Fuller C., Brooks T., Humphreys W. (2011), A background noise reduction technique using adaptive noise cancellation for microphone arrays, 17th AIAA/CEAS Aeroacoustics Conference (32nd AIAA Aeroacoustics Conference), Portland, Oregon, USA, June 05–08, Paper No. 2011-2715, doi: 10.2514/6.2011-2715.

Spalt T.B., Fuller C.R., Brooks T.F. (2012), Background noise reduction using adaptive noise cancellation determined by the cross-correlation, Inter-Noise Conference, New York, USA, August 19–22.

Suryadi A., Martens S., Herr M. (2017), Trailing edge noise reduction technologies for applications in wind energy, 23rd AIAA/CEAS Aeroacoustics Conference, Denver, Colorado, USA, June 5–9, Paper No. 2017-3534, doi: 10.2514/6.2017-3534.

Taebi A., Mansy H.A. (2017), Noise cancellation from vibrocardiographic signals based on the ensemble empirical mode decomposition, Journal of Applied Biotechnology & Bioengineering, 2(2): 00024, doi: 10.15406/jabb.2017.02.00024.

Tao J., Sun G. (2016), An artificial neural network approach for aerodynamic performance retention in airframe noise reduction design of a 3D swept wing model, Chinese Journal of Aeronautics, 29(5): 1213–1225, doi: 10.1016/j.cja.2016.08.008.

Vathylakis A., Chong T.P., Joseph P.F. (2015), Poro-serrated trailing-edge devices for airfoil self-noise reduction, AIAA Journal, 53(11): 3379–3394, doi: 10.2514/1.j053983.

Wei L., Li M., Qin S., Fu Q., Yang D. (2017a), Sound source localization method in an environment with flow based on Amiet–IMACS, Mechanical Systems and Signal Processing, 88: 240–252, doi: 10.1016/j.ymssp.2016.11.011.

Wei L., Li M., Yang D., Niu F., Zeng W. (2017b), Reconstruction of sound source signal by analytical passive TR in the environment with airflow, Journal of Sound and Vibration, 392: 77–90, doi: 10.1016/j.jsv.2016.12.040.

Wu Z., Huang N.E. (2009), Ensemble empirical mode decomposition: a noise-assisted data analysis method, Advances in Adaptive Data Analysis, 1(01): 1–41, doi: 10.1142/s1793536909000047.

Yang D., Li H., Hu Y., Zhao J., Xiao H., Lan Y. (2016), Vibration condition monitoring system for wind turbine bearings based on noise suppression with multi-point data fusion, Renewable Energy, 92, 104–

, doi: 10.1016/j.renene.2016.01.099.

Zhang C.Q. et al. (2019), Locating and tracking sound sources on a horizontal axis wind turbine using a compact microphone array based on beamforming, Applied Acoustics, 146: 295–309, doi: 10.1016/j.apacoust.2018.10.006.

Žvokelj M., Zupan S., Prebil I. (2016), EEMDbased multiscale ICA method for slewing bearing fault detection and diagnosis, Journal of Sound and Vibration, 370: 394–423, doi: 10.1016/j.jsv.2016.01.046.

DOI: 10.24425/aoa.2022.141653

Copyright © Polish Academy of Sciences & Institute of Fundamental Technological Research (IPPT PAN)