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Dynamic mode decomposition of numerical and experimental data

Published online by Cambridge University Press:  01 July 2010

PETER J. SCHMID*
Affiliation:
Laboratoire d'Hydrodynamique (LadHyX), CNRS-École Polytechnique, 91128 Palaiseau, France
*
Email address for correspondence: [email protected]

Abstract

The description of coherent features of fluid flow is essential to our understanding of fluid-dynamical and transport processes. A method is introduced that is able to extract dynamic information from flow fields that are either generated by a (direct) numerical simulation or visualized/measured in a physical experiment. The extracted dynamic modes, which can be interpreted as a generalization of global stability modes, can be used to describe the underlying physical mechanisms captured in the data sequence or to project large-scale problems onto a dynamical system of significantly fewer degrees of freedom. The concentration on subdomains of the flow field where relevant dynamics is expected allows the dissection of a complex flow into regions of localized instability phenomena and further illustrates the flexibility of the method, as does the description of the dynamics within a spatial framework. Demonstrations of the method are presented consisting of a plane channel flow, flow over a two-dimensional cavity, wake flow behind a flexible membrane and a jet passing between two cylinders.

Type
Papers
Copyright
Copyright © Cambridge University Press 2010

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References

REFERENCES

Åkervik, E., Hœpffner, J., Ehrenstein, U. & Henningson, D. S. 2007 Optimal growth, model reduction and control in a separated boundary-layer flow using global modes. J. Fluid Mech. 579, 305314.CrossRefGoogle Scholar
Aubry, N. 1991 On the hidden beauty of the proper orthogonal decomposition. Theor. Comp. Fluid Dyn. 2, 339352.CrossRefGoogle Scholar
Barbagallo, A., Sipp, D. & Schmid, P. J. 2009 Closed-loop control of an open cavity flow using reduced-order models. J. Fluid Mech. 641, 150.CrossRefGoogle Scholar
Berkooz, G., Holmes, P. & Lumley, J. L. 1993 The proper orthogonal decomposition in the analysis of turbulent flows. Annu. Rev. Fluid Mech. 25, 539575.CrossRefGoogle Scholar
Bonnet, J.-P., Cole, D. R., Delville, J., Glauser, M. N. & Ukeiley, L. S. 1994 Stochastic estimation and proper orthogonal decomposition: complementary techniques for identifying structure. Exp. Fluids 17, 307314.CrossRefGoogle Scholar
DelSole, T. & Hou, A. Y. 1999 Empirical stochastic models for the dominant climate statistics of a general circulation model. J. Atmos. Sci. 56, 34363456.2.0.CO;2>CrossRefGoogle Scholar
Edwards, W. S., Tuckerman, L. S., Friesner, R. A. & Sorensen, D. C. 1994 Krylov methods for the incompressible Navier–Stokes equations. J. Comput. Phys. 110, 82102.CrossRefGoogle Scholar
Greenbaum, A. 1997 Iterative Methods for Solving Linear Systems. SIAM.CrossRefGoogle Scholar
Hasselmann, K. 1988 POPs and PIPs: the reduction of complex dynamical systems using principal oscillations and interaction patterns. J. Geophys. Res. 93, 1097510988.Google Scholar
Hemon, P. & Santi, F. 2007 Simulation of a spatially correlated turbulent velocity field using biorthogonal decomposition. J. Wind Engng Ind. Aerodyn. 95, 2129.CrossRefGoogle Scholar
Herzog, S. 1986 The large scale structure in the near-wall region of turbulent pipe flow. PhD dissertation, Department Mechanical Engineering, Cornell University.Google Scholar
Hussain, A. K. M. F. 1986 Coherent structures and turbulence. J. Fluid Mech. 173, 303356.CrossRefGoogle Scholar
Lasota, A. & Mackey, M. C. 1994 Chaos, Fractals and Noise: Stochastic Aspects of Dynamics. Springer.CrossRefGoogle Scholar
Lehoucq, R. B. & Scott, J. A. 1997 Implicitly restarted Arnoldi methods and subspace iteration. SIAM J. Matrix Anal. Appl. 23, 551562.CrossRefGoogle Scholar
Lumley, J. L. 1970 Stochastic Tools in Turbulence. Academic Press.Google Scholar
Mezić, I. 2005 Spectral properties of dynamical systems, model reduction and decompositions. Nonlinear Dyn. 41, 309325.CrossRefGoogle Scholar
Noack, B. R., Afanasiev, K., Morzynski, M., Tadmor, G. & Thiele, F. 2003 A hierarchy of low-dimensional models for the transient and post-transient cylinder wake. J. Fluid Mech. 497, 335363.CrossRefGoogle Scholar
Noack, B. R., Schlegel, M., Ahlborn, B., Mutschke, G., Morzynski, M., Comte, P. & Tadmor, G. 2008 A finite-time thermodynamics formalism for unsteady flows. J. Non-Equilib. Thermodyn. 33, 103148.Google Scholar
Orszag, S. A. 1971 Accurate solution of the Orr–Sommerfeld stability equation. J. Fluid Mech. 50, 689703.CrossRefGoogle Scholar
Penland, C. & Magoriam, T. 1993 Prediction of Niño 3 sea-surface temperatures using linear inverse modelling. J. Climate 6, 10671076.2.0.CO;2>CrossRefGoogle Scholar
Rowley, C. W., Mezić, I., Bagheri, S., Schlatter, P. & Henningson, D. S. 2009 Spectral analysis of nonlinear flows. J. Fluid Mech. 641, 115127.CrossRefGoogle Scholar
Ruhe, A. 1984 Rational Krylov sequence methods for eigenvalue computation. Linear Algebr Appl. 58, 279316.CrossRefGoogle Scholar
Schmid, P. J. 2007 Transition and transition control in a square cavity. In Advances in Turbulence XI (ed. Palma, J. M. L. M. & Silva Lopez, A.), pp. 562569. Springer Verlag.CrossRefGoogle Scholar
Schmid, P. J. & Henningson, D. S. 2001 Stability and Transition in Shear Flows. Springer.CrossRefGoogle Scholar
Schmid, P. J., Li, L., Juniper, M. P. & Pust, O. 2010 Applications of the dynamic mode decomposition. Theor. Comput. Fluid Dyn. (in press).CrossRefGoogle Scholar
Schmid, P. J. & Sesterhenn, J. L. 2008 Dynamic mode decomposition of numerical and experimental data. In Bull. Amer. Phys. Soc., 61st APS meeting, p. 208. San Antonio.Google Scholar
Schmit, R. F. & Glauser, M. N. 2009 Use of low-dimensional methods for wake flowfield estimation from dynamic strain. AIAA J. 43 (5), 11331136.CrossRefGoogle Scholar
Sipp, D. & Lebedev, A. 2007 Global stability of base and mean flows: a general approach and its applications to cylinder and open cavity flows. J. Fluid Mech. 593, 333358.CrossRefGoogle Scholar
Sirovich, L. 1987 Turbulence and the dynamics of coherent structures. Q. Appl. Math. 45, 561590.CrossRefGoogle Scholar
von Storch, H., Bürger, G., Schnur, R. & von Storch, J. 1995 Principal oscillation pattern: a review. J. Climate 8, 377400.2.0.CO;2>CrossRefGoogle Scholar
Trefethen, L. N. & Bau, D. 1997 Numerical Linear Algebra. SIAM.CrossRefGoogle Scholar