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Unsteady flow dynamics reconstruction from mean flow and point sensors: an experimental study

Published online by Cambridge University Press:  05 July 2017

Samir Beneddine Open the ORCID record for Samir Beneddine [Opens in a new window] ,
Robin Yegavian ,
Denis Sipp  and
Benjamin Leclaire
Samir Beneddine*
Affiliation:
ONERA – The French Aerospace Lab, Aerodynamics Aeroelasticity Acoustics Department, 8 rue des Vertugadins, FR-92190, Meudon, France
Robin Yegavian
Affiliation:
ONERA – The French Aerospace Lab, Aerodynamics Aeroelasticity Acoustics Department, 8 rue des Vertugadins, FR-92190, Meudon, France
Denis Sipp
Affiliation:
ONERA – The French Aerospace Lab, Aerodynamics Aeroelasticity Acoustics Department, 8 rue des Vertugadins, FR-92190, Meudon, France
Benjamin Leclaire
Affiliation:
ONERA – The French Aerospace Lab, Aerodynamics Aeroelasticity Acoustics Department, 8 rue des Vertugadins, FR-92190, Meudon, France
*
†Email address for correspondence: [email protected]
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Abstract

This article presents a reconstruction of the unsteady behaviour of a round jet at a Reynolds number equal to 3300, from the sole knowledge of the time-averaged flow field and one pointwise unsteady measurement. The reconstruction approach is an application of the work of Beneddine et al. (J. Fluid Mech., vol. 798, 2016, pp. 485–504) and relies on the computation of the dominant resolvent modes of the flow, using a parabolised stability equations analysis. To validate the procedure, the unsteady velocity field of the jet has been characterised by time-resolved particle image velocimetry (TR-PIV), yielding an experimental reference. We first show that the dominant resolvent modes are proportional to the experimental Fourier modes, as predicted by Beneddine et al. (J. Fluid Mech., vol. 798, 2016, pp. 485–504). From these results, it is then possible to fully reconstruct the unsteady velocity and pressure fluctuation fields, yielding a flow field that displays good agreement with the experimental reference. Finally, it is found that the robustness of the reconstruction mainly depends on the location of the pointwise unsteady measurement, which should be within energetic regions of the flow, and this robustness as well as the quality of the reconstruction can be greatly improved by considering a few pointwise measurements instead of a single one. The effects of other experimental parameters on the reconstruction, such as the size of the interrogation window used for the TR-PIV processing and the accuracy of the positioning of the sensors, are also investigated in this paper.

JFM classification

Mathematical Foundations: General fluid mechanics Instability: Instability Wakes/Jets: Jets
Type
Papers
Information
Journal of Fluid Mechanics , Volume 824 , 10 August 2017 , pp. 174 - 201
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Copyright
© 2017 Cambridge University Press 

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References

Adrian, R. J. 1979 Conditional eddies in isotropic turbulence. Phys. Fluids 22 (11), 2065–2070.Google Scholar
Andersson, P., Henningson, D. S. & Hanifi, A. 1998 On a stabilization procedure for the parabolic stability equations. J. Engng Maths 33 (3), 311–332.CrossRefGoogle Scholar
Barkley, D. 2006 Linear analysis of the cylinder wake mean flow. Europhys. Lett. 75 (5), 750–756.Google Scholar
Beneddine, S., Sipp, D., Arnault, A., Dandois, J. & Lesshafft, L. 2016 Conditions for validity of mean flow stability analysis. J. Fluid Mech. 798, 485–504.CrossRefGoogle Scholar
Cavalieri, A., Rodríguez, D., Jordan, P., Colonius, T. & Gervais, Y. 2013 Wavepackets in the velocity field of turbulent jets. J. Fluid. Mech. 730, 559–592.Google Scholar
Champagnat, F., Plyer, A., Le Besnerais, G., Leclaire, B., Davoust, S. & Le Sant, Y. 2011 Fast and accurate PIV computation using highly parallel iterative correlation maximization. Exp. Fluids 50 (4), 1169–1182.Google Scholar
Cole, D. R. & Glauser, M. N. 1998 Applications of stochastic estimation in the axisymmetric sudden expansion. Phys. Fluids 10 (11), 2941–2949.Google Scholar
Davoust, S., Jacquin, L. & Leclaire, B. 2012 Dynamics of m = 0 and m = 1 modes and of streamwise vortices in a turbulent axisymmetric mixing layer. J. Fluid Mech. 709, 408–444.CrossRefGoogle Scholar
Ehrenstein, U. & Gallaire, F. 2005 On two-dimensional temporal modes in spatially evolving open flows: the flat-plate boundary layer. J. Fluid Mech. 536, 209–218.Google Scholar
Gòmez, F., Blackburn, H. M., Rudman, M., Sharma, A. S. & McKeon, B. J. 2016a A reduced-order model of three-dimensional unsteady flow in a cavity based on the resolvent operator. J. Fluid Mech. 798, R2.Google Scholar
Gòmez, F., Sharma, A. S. & Blackburn 2016b Estimation of unsteady aerodynamic forces using pointwise velocity data. J. Fluid Mech. 804, R4.CrossRefGoogle Scholar
Gudmundsson, K. & Colonius, T. 2011 Instability wave models for the near-field fluctuations of turbulent jets. J. Fluid Mech. 689, 97–128.Google Scholar
Guezennec, Y. G. 1989 Stochastic estimation of coherent structures in turbulent boundary layers. Phys. Fluids A 1 (6), 1054–1060.CrossRefGoogle Scholar
Gutmark, E. & Ho, C. 1983 Preferred modes and the spreading rates of jets. Phys. Fluids 26 (10), 2932–2938.Google Scholar
Guzmán Iñigo, J., Sipp, D. & Schmid, P. J. 2014 A dynamic observer to capture and control perturbation energy in noise amplifiers. J. Fluid Mech. 758, 728–753.Google Scholar
Herbert, T. 1997 Parabolized stability equations. Annu. Rev. Fluid Mech. 29 (1), 245–283.Google Scholar
Hudy, L. M., Naguib, A. & Humphreys, W. M. 2007 Stochastic estimation of a separated-flow field using wall-pressure-array measurements. Phys. Fluids 19 (2), 024103.Google Scholar
Jeon, Y. J., Chatellier, L. & David, L. 2014 Fluid trajectory evaluation based on an ensemble-averaged cross-correlation in time-resolved PIV. Exp. Fluids 55 (7), 1–16.CrossRefGoogle Scholar
Jordan, P. & Colonius, T. 2013 Wave packets and turbulent jet noise. Annu. Rev. Fluid Mech. 45, 173–195.Google Scholar
Lynch, K. & Scarano, F. 2013 A high-order time-accurate interrogation method for time-resolved PIV. Meas. Sci. Technol. 24 (3), 035305.Google Scholar
McKeon, B. J. & Sharma, A. S. 2010 A critical-layer framework for turbulent pipe flow. J. Fluid Mech. 658, 336–382.Google Scholar
Murray, N. E. & Ukeiley, L. S. 2003 Estimation of the flowfield from surface pressure measurements in an open cavity. AIAA J. 41 (5), 969–972.Google Scholar
Naguib, A. M., Wark, C. E. & Juckenhöfel, O. 2001 Stochastic estimation and flow sources associated with surface pressure events in a turbulent boundary layer. Phys. Fluids 13 (9), 2611–2626.Google Scholar
Pier, B. 2002 On the frequency selection of finite-amplitude vortex shedding in the cylinder wake. J. Fluid Mech. 458, 407–417.Google Scholar
Rodríguez, D., Cavalieri, A. V. G., Colonius, T. & Jordan, P. 2015 A study of linear wavepacket models for subsonic turbulent jets using local eigenmode decomposition of PIV data. Eur. J. Mech. (B/Fluids) 49, 308–321.Google Scholar
Sasaki, K., Piantanida, S., Cavalieri, A. V. G. & Jordan, P. 2017 Real-time modelling of wavepackets in turbulent jets. J. Fluid Mech. 821, 458–481.Google Scholar
Stokes, S. & Glauser, M. 1999 Multi-point measurement techniques used in the study of separated flows. In 30th AIAA Fluid Dynamics Conference, AIAA Paper, vol. 28, p. 3518.Google Scholar
Theofilis, V. 2011 Global linear instability. Annu. Rev. Fluid Mech. 43, 319–352.Google Scholar
Tissot, G., Zhang, M., Lajús, F. C., Cavalieri, A. V. G. & Jordan, P. 2017 Sensitivity of wavepackets in jets to nonlinear effects: the role of the critical layer. J. Fluid Mech. 811, 95–137.Google Scholar
Towne, A. & Colonius, T. 2015 One-way spatial integration of hyperbolic equations. J. Comput. Phys. 300, 844–861.Google Scholar
Tu, J. H., Griffin, J., Hart, A., Rowley, C. W., Cattafesta, L. N. III & Ukeiley, L. S. 2013 Integration of non-time-resolved PIV and time-resolved velocity point sensors for dynamic estimation of velocity fields. Exp. Fluids 54 (2), 1–20.Google Scholar
Tung, T. C. & Adrian, R. J. 1980 Higher-order estimates of conditional eddies in isotropic turbulence. Phys. Fluids 23 (7), 1469–1470.Google Scholar
Yegavian, R., Leclaire, B., Champagnat, F., Illoul, C. & Losfeld, G. 2016 Lucas-Kanade fluid trajectories for time-resolved PIV. Meas. Sci. Technol. 27 (8), 084004.Google Scholar