Hostname: page-component-586b7cd67f-l7hp2 Total loading time: 0 Render date: 2024-11-23T13:31:26.989Z Has data issue: false hasContentIssue false
  • English
  • Français

Reduction of noise in cold and hot supersonic jets using active flow control guided by a genetic algorithm

Published online by Cambridge University Press:  02 December 2022

Fernando Zigunov Open the ORCID record for Fernando Zigunov [Opens in a new window] ,
Prabu Sellappan Open the ORCID record for Prabu Sellappan [Opens in a new window]  and
Farrukh. S. Alvi
Fernando Zigunov*
Affiliation:
Department of Mechanical Engineering, FAMU-FSU College of Engineering, FL 32310, USA
Prabu Sellappan
Affiliation:
Department of Mechanical Engineering, FAMU-FSU College of Engineering, FL 32310, USA
Farrukh. S. Alvi
Affiliation:
Department of Mechanical Engineering, FAMU-FSU College of Engineering, FL 32310, USA
*
†Email address for correspondence: fzigunov@gmail.com
Get access Rights & Permissions [Opens in a new window]

Abstract

This study demonstrates an experimental platform for active jet noise reduction comprising of an automated system that performs a search for the optimal actuator locations and parameters, powered by a genetic algorithm (GA). Sideline noise reduction levels of 7.3 dB were achieved for a cold overexpanded (nozzle pressure ratio, $NPR=2.8$) jet, beyond the state-of-the-art for jet noise reduction with air injection. The reduction in noise was achieved at a mass flow rate of 1.4 % of the main jet, requiring no prior knowledge of the flow physics to inform the placement of the actuators. The same actuator pattern was tested in hot conditions (nozzle temperature ratio, $NTR=1.88$), achieving 4.7 dB sideline noise reduction. Detailed examination of the solutions obtained unveils some of the mechanisms leveraged by the GA to accomplish these high levels of noise reduction through microphone measurements and schlieren flow visualization. The GA found that actuating at the diverging wall of the convergent–divergent nozzle, where flow is supersonic, is very effective when combined with air injection near the nozzle lip, outside of the nozzle. Although the external actuation is effective at eliminating the screech tone, it is by actuating inside the nozzle at the diverging wall that sufficient disruption of the shock cell train can be achieved in order to reduce the broadband shock-associated noise.

JFM classification

Acoustics: Noise control Compressible Flows: Supersonic flow
Type
JFM Papers
Information
Journal of Fluid Mechanics , Volume 952 , 10 December 2022 , A40
Check for updates
Copyright
© The Author(s), 2022. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Alkislar, M.B., Krothapalli, A. & Butler, G.W. 2007 The effect of streamwise vortices on the aeroacoustics of a Mach 0.9 jet. J. Fluid Mech. 578, 139–169.CrossRefGoogle Scholar
Alvi, F.S., Lou, H., Shih, C. & Kumar, R. 2008 Experimental study of physical mechanisms in the control of supersonic impinging jets using microjets. J. Fluid Mech. 613, 55–83.CrossRefGoogle Scholar
Blanchard, A.B., Cornejo Maceda, G.Y., Fan, D., Li, Y., Zhou, Yu., Noack, B.R. & Sapsis, T.P. 2021 Bayesian optimization for active flow control. Acta Mechanica Sin. 37 (12), 1786–1798.CrossRefGoogle Scholar
Carroll, B.F., Dutton, J.C. & Addy, A.L. 1986 NOZCS2: a computer program for the design of continuous slope supersonic nozzles. Tech. Rep. UILU ENG 86-4007. University of Illinois at Urbana-Champaign.Google Scholar
Castelain, T., Sunyach, M., Juvé, D. & Béra, J.-C. 2008 Jet-noise reduction by impinging microjets: an acoustic investigation testing microjet parameters. AIAA J. 46 (5), 1081–1087.CrossRefGoogle Scholar
Cornejo Maceda, G.Y., Li, Y., Lusseyran, F., Morzyński, M. & Noack, B.R. 2021 Stabilization of the fluidic pinball with gradient-enriched machine learning control. J. Fluid Mech. 917, A42.CrossRefGoogle Scholar
Craft, J. 2016 Characterization and validation of an anechoic facility for high-temperature jet noise studies. Masters thesis, Ann Arbor. Florida State University.CrossRefGoogle Scholar
Debien, A., Krbek, K.A.F.F., Mazellier, N., Duriez, T., Cordier, L., Noack, B.R., Abel, M.W. & Kourta, A. 2016 Closed-loop separation control over a sharp edge ramp using genetic programming. Exp. Fluids 57 (3), 40.CrossRefGoogle Scholar
Déda, T.C. & Wolf, W.R. 2022 Extremum seeking control applied to airfoil trailing-edge noise suppression. AIAA J. 60 (2), 823–843.Google Scholar
Diaz-Gomez, P. & Hougen, D. 2007 Initial population for genetic algorithms: a metric approach. In Proceedings of the 2007 International Conference on Genetic and Evolutionary Methods, GEM 2007, June 25–28, 2007, Las Vegas, Nevada, USA, pp. 43–49.Google Scholar
Duriez, T., Brunton, S. & Noack, B. 2017 Machine Learning Control – Taming Nonlinear Dynamics and Turbulence. Springer.CrossRefGoogle Scholar
Edgington-Mitchell, D. 2019 Aeroacoustic resonance and self-excitation in screeching and impinging supersonic jets – a review. Intl J. Aeroacoust. 18 (2–3), 118–188.CrossRefGoogle Scholar
Gautier, N., Aider, J.-L., Duriez, T., Noack, B.R., Segond, M. & Abel, M. 2015 Closed-loop separation control using machine learning. J. Fluid Mech. 770, 442–457.CrossRefGoogle Scholar
Greska, B. & Krothapalli, A. 2005 The near-field effects of microjet injection. In 11th AIAA/CEAS Aeroacoustics Conference, May 2005. AIAA Paper 2005-3046.Google Scholar
Greska, B., Krothapalli, A. & Arakeri, V. 2003 A further investigation into the effects of microjets on high speed jet noise. In 9th AIAA/CEAS Aeroacoustics Conference and Exhibit, May 2003. AIAA Paper 2003-3128.Google Scholar
Harper-Bourne, M. & Fisher, M.J. 1977 The noise from shock waves in supersonic jets. In AGARD Conference on Noise Mechanisms, vol. 131, p. 11. North Atlantic Treaty Organization (NATO).Google Scholar
Henderson, B. 2010 Fifty years of fluidic injection for jet noise reduction. Intl J. Aeroacoust. 9 (1–2), 91–122.CrossRefGoogle Scholar
Henderson, B. & Norum, T. 2008 Impact of azimuthally controlled fluidic chevrons on jet noise. In 14th AIAA/CEAS Aeroacoustics Conference (29th AIAA Aeroacoustics Conference), May 2008. AIAA Paper 2008-3062.Google Scholar
Huff, D.L. 2001 High-speed jet noise reduction NASA perspective. NASA Tech. Rep. 20020024448.Google Scholar
Ibrahim, M.K., Kunimura, R. & Nakamura, Y. 2002 Mixing enhancement of compressible jets by using unsteady microjets as actuators. AIAA J. 40 (4), 681–688.Google Scholar
Joslin, R.D. & Miller, D.N. 2009 Fundamentals and Applications of Modern Flow Control. AIAA.CrossRefGoogle Scholar
Kandula, M. 2008 Prediction of turbulent jet mixing noise reduction by water injection. AIAA J. 46 (11), 2714–2722.Google Scholar
Kibens, V., John, D.III, Smith, D. & Mossman, M. 1999 Active flow control technology transition – the Boeing ACE program. In 30th Fluid Dynamics Conference, June 1999. AIAA Paper 1999-3507.Google Scholar
Knast, T. 2020 The effect of jet exit pressure on jets in supersonic crossflow. Masters thesis, Florida State University.Google Scholar
Krothapalli, A., Venkatakrishnan, L. & Lourenco, L. 2000 Crackle – a dominant component of supersonic jet mixing noise. In 6th Aeroacoustics Conference and Exhibit, June 2000. AIAA Paper 2000-2024.Google Scholar
Krothapalli, A., Venkatakrishnan, L., Lourenco, L., Greska, B. & Elavarasan, R. 2003 Turbulence and noise suppression of a high-speed jet by water injection. J. Fluid Mech. 491, 131–159.CrossRefGoogle Scholar
Lighthill, M.J. & Newman, M.H.A. 1952 On sound generated aerodynamically I. General theory. Proc. R. Soc. Lond. A 211 (1107), 564–587.Google Scholar
Liu, J., Khine, Yu.Yu., Saleem, M., Rodriguez, O.L. & Gutmark, E.J. 2022 Effect of axial location of micro vortex generators on supersonic jet noise reduction. In AIAA SCITECH 2022 Forum, January 2022. AIAA Paper 2022-1791.Google Scholar
Morris, P.J., McLaughlin, D.K. & Kuo, C.-W. 2013 Noise reduction in supersonic jets by nozzle fluidic inserts. J. Sound Vib. 332 (17), 3992–4003.Google Scholar
Norum, T. 2004 Reductions in multi-component jet noise by water injection. In 10th AIAA/CEAS Aeroacoustics Conference, May 2004. AIAA Paper 2004-2976.Google Scholar
Powell, A. 1954 The influence of the exit velocity profile on the noise of a jet. Aeronaut. Q. 4 (4), 341–360.Google Scholar
Prasad, C. & Morris, P.J. 2020 A study of noise reduction mechanisms of jets with fluid inserts. J. Sound Vib. 476, 115331.CrossRefGoogle Scholar
Raman, G. & Cornelius, D. 1995 Jet mixing control using excitation from miniature oscillating jets. AIAA J. 33 (2), 365–368.CrossRefGoogle Scholar
Semlitsch, B., Cuppoletti, D.R., Gutmark, E.J. & Mihăescu, M. 2019 Transforming the shock pattern of supersonic jets using fluidic injection. AIAA J. 57 (5), 1851–1861.CrossRefGoogle Scholar
Song, M.J., Bhargav, V.N., Seckin, S., Sellappan, P., Kumar, R. & Alvi, F.S. 2022 Dynamics and response to control of single and dual supersonic impinging jets. AIAA J. 60 (5), 1–17.CrossRefGoogle Scholar
Tam, C.K.W. 1995 Supersonic jet noise. Annu. Rev. Fluid Mech. 27 (1), 17–43.Google Scholar
Welch, P. 1967 The use of fast Fourier transform for the estimation of power spectra: a method based on time averaging over short, modified periodograms. IEEE Trans. Audio Electroacoust. 15 (2), 70–73.CrossRefGoogle Scholar
Zaman, K.B.M.Q. 2010 Subsonic jet noise reduction by microjets – a parametric study. Intl J. Aeroacoust. 9 (6), 705–732.CrossRefGoogle Scholar
Zigunov, F. 2020 Jexel Driver – 108 count solenoid driver (Version V0). Zenodo. https://doi.org/10.5281/zenodo.3991589.CrossRefGoogle Scholar
Zigunov, F., Sellappan, P. & Alvi, F. 2021 Beyond actuator line arrays in active flow control studies: lessons from a genetic algorithm approach. Phys. Rev. Fluids 6, 083903.CrossRefGoogle Scholar
Zigunov, F., Sellappan, P. & Alvi, F. 2022 A bluff body flow control experiment with distributed actuation and genetic algorithm-based optimization. Exp. Fluids 63 (1), 23.Google Scholar