Abstract
Sensor measurements often contain contamination resulting from acoustics, vibrations, electromagnetic interference, etc., making it challenging to isolate the physics of interest. The current work provides a general noise-removal technique via a multiple-input, multiple-output (MIMO) framework capable of removing an arbitrary number of possibly coherent contaminating noise measurements, regardless of their order, from multiple sensor measurements. An application example to unsteady surface pressure measurements in an air wind tunnel is provided to demonstrate the technique. Nineteen pressure sensors measuring the pressure fluctuations within a turbulent separation bubble (TSB) at \(Re_{\theta } = U_{\infty }\theta /\nu \approx 800\) are denoised using five measurements of external sources of contamination, simultaneously acquired via two microphones, two accelerometers, and the most upstream pressure sensor. The effectiveness of the ‘denoising’ approach is demonstrated in both the frequency and time-domains for signal-to-noise ratio (SNR) \(\approx\) 0 dB, uncovering the fundamental flow physics related to the TSB.
Graphical abstract
This is a preview of subscription content, log in via an institution to check access.
Data availability
The datasets used and/or analyzed in the current study are available from the authors.
Notes
\(X=\mathfrak {I}(x)\) denotes a Fourier transform of signal x where lowercase denotes the time domain, and uppercase X denotes the frequency domain.
References
Bendat JS, Piersol AG (2010) Random Data: Analysis and Measurements Procedures, 4th edn. Wiley, New York
Bonness WK, Jenkins Jr DM (2012) Coherent noise removal from dynamic wall pressure and vibration signals. In: 41st International Congress and Exposition on Noise Control Engineering 2012, INTER-NOISE 2012, pp 118–127
Horne M, Hansen R (1983) Minimization of farfield acoustic effects in turbulent boundary layer wall pressure fluctuation experiments. In: 7th Symposium on Turbulence, pp 139–147
Lauchle GC, Daniels MA (1987) Wall-pressure fluctuations in turbulent pipe flow. Phys Fluids 30(10):3019–3024. https://doi.org/10.1063/1.866080
Mohammed-Taifour A, Weiss J (2021) Periodic forcing of a large turbulent separation bubble. J Fluid Mech 915:A24. https://doi.org/10.1017/jfm.2021.77
Na Y, Moin P (1998) The structure of wall-pressure fluctuations in turbulent boundary layers with adverse pressure gradient and separation. J Fluid Mech 377:347–373. https://doi.org/10.1017/S0022112098003218
Naguib A, Gravante S, Wark C (1996) Extraction of turbulent wall-pressure time-series using an optimal filtering scheme. Exp Fluids 22(1):14–22. https://doi.org/10.1007/BF01893301
Richardson R, Zhang Y, Cattafesta LN (2023) Low frequency characteristics of a pressure-gradient induced turbulent separation bubble. In: AIAA Scitech 2023 Forum, p 0079, https://doi.org/10.2514/6.2023-0079
Weiss J, Mohammed-Taifour A, Schwaab Q (2015) Unsteady behavior of a pressure-induced turbulent separation bubble. AIAA J 53(9):2634–2645. https://doi.org/10.2514/1.J053778
Zawodny NS, Liu F, Cattafesta L (2017) Transfer matrix modeling of a recessed microphone for unsteady surface pressure measurements. Appl Acoust 117:185–190. https://doi.org/10.1016/j.apacoust.2016.10.013
Acknowledgements
We gratefully acknowledge support by the U.S. Air Force Office of Scientific Research Grant FA9550-17-1-0380, monitored by Dr. Gregg Abate.
Funding
Funding details are provided in the Acknowledgements.
Author information
Authors and Affiliations
Contributions
R.R. and Y.Z. performed the experiments and the analysis. L.C. devised the method. R.R. wrote the manuscript and prepared figures. Y.Z. and L.C. edited the text. All authors reviewed the manuscript.
Corresponding author
Ethics declarations
Competing interest
The authors have no competing interests to disclose.
Ethical approval
Not applicable.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Richardson, R., Zhang, Y. & Cattafesta, L.N. Sensor decontamination via conditional spectral analysis. Exp Fluids 64, 163 (2023). https://doi.org/10.1007/s00348-023-03705-9
Received:
Revised:
Accepted:
Published:
DOI: https://doi.org/10.1007/s00348-023-03705-9