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Koopman analysis of the long-term evolution in a turbulent convection cell

Published online by Cambridge University Press:  29 May 2018

Dimitrios Giannakis
Affiliation:
Center for Atmosphere Ocean Science, Courant Institute of Mathematical Sciences, New York University, New York, NY 10012, USA
Anastasiya Kolchinskaya
Affiliation:
Institut für Thermo- und Fluiddynamik, Postfach 100565, Technische Universität Ilmenau, D-98684 Ilmenau, Germany
Dmitry Krasnov
Affiliation:
Institut für Thermo- und Fluiddynamik, Postfach 100565, Technische Universität Ilmenau, D-98684 Ilmenau, Germany
Jörg Schumacher*
Affiliation:
Institut für Thermo- und Fluiddynamik, Postfach 100565, Technische Universität Ilmenau, D-98684 Ilmenau, Germany
*
Email address for correspondence: joerg.schumacher@tu-ilmenau.de

Abstract

We analyse the long-time evolution of the three-dimensional flow in a closed cubic turbulent Rayleigh–Bénard convection cell via a Koopman eigenfunction analysis. A data-driven basis derived from diffusion kernels known in machine learning is employed here to represent a regularized generator of the unitary Koopman group in the sense of a Galerkin approximation. The resulting Koopman eigenfunctions can be grouped into subsets in accordance with the discrete symmetries in a cubic box. In particular, a projection of the velocity field onto the first group of eigenfunctions reveals the four stable large-scale circulation (LSC) states in the convection cell. We recapture the preferential circulation rolls in diagonal corners and the short-term switching through roll states parallel to the side faces which have also been seen in other simulations and experiments. The diagonal macroscopic flow states can last as long as 1000 convective free-fall time units. In addition, we find that specific pairs of Koopman eigenfunctions in the secondary subset obey enhanced oscillatory fluctuations for particular stable diagonal states of the LSC. The corresponding velocity-field structures, such as corner vortices and swirls in the midplane, are also discussed via spatiotemporal reconstructions.

Type
JFM Papers
Copyright
© 2018 Cambridge University Press 

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References

Arbabi, H. & Mezić, I. 2017 Ergodic theory, dynamic mode decomposition and computation of spectral properties of the Koopman operator. SIAM J. Appl. Dyn. Sys. 16, 20962126.Google Scholar
Aubry, N., Guyonnet, R. & Lima, R. 1991 Spatiotemporal analysis of complex signals: theory and applications. J. Stat. Phys. 64, 683739.CrossRefGoogle Scholar
Babuška, I. & Osborn, J. 1991 Eigenvalue problems. In Finite Element Methods (Part 1), Handbook of Numerical Analysis (ed. Ciarlet, P. G. & Lions, J. L.), vol. II, pp. 641787. North-Holland.Google Scholar
Bai, K., Ji, D. & Brown, E. 2016 Ability of a low-dimensional model to predict geometry-dependent dynamics of large-scale coherent structures in turbulence. Phys. Rev. E 93, 023117.Google Scholar
Bailon-Cuba, J., Emran, M. S. & Schumacher, J. 2010 Aspect ratio dependence of heat transfer and large-scale flow in turbulent convection. J. Fluid Mech. 655, 152173.CrossRefGoogle Scholar
Berry, T., Cressman, R., Gregurić-Ferenček, Z. & Sauer, T. 2013 Time-scale separation from diffusion-mapped delay coordinates. SIAM J. Appl. Dyn. Syst. 12, 618649.CrossRefGoogle Scholar
Berry, T., Giannakis, D. & Harlim, J. 2015 Nonparametric forecasting of low-dimensional dynamical systems. Phys. Rev. E 91, 032915.Google Scholar
Berry, T. & Sauer, T. 2016 Local kernels and the geometric structure of data. Appl. Comput. Harmon. Anal. 40 (3), 439469.CrossRefGoogle Scholar
Brenowitz, N. D., Giannakis, D. & Majda, A. J. 2016 Nonlinear Laplacian spectral analysis of Rayleigh–Bénard convection. J. Comput. Phys. 315, 536553.Google Scholar
Broomhead, D. S. & King, G. P. 1986 Extracting qualitative dynamics from experimental data. Physica D 20 (2–3), 217236.Google Scholar
Brown, E. & Ahlers, G. 2006 Rotations and cessations of the large-scale circulation in turbulent Rayleigh–Bénard convection. J. Fluid Mech. 568, 351386.Google Scholar
Brunton, S. L., Brunton, B. W., Proctor, J. L., Kaiser, E. & Kutz, J. N. 2017 Chaos as an intermittently forced linear system. Nat. Commun. 8, 9.CrossRefGoogle ScholarPubMed
Budisić, M., Mohr, R. & Mezić, I. 2012 Applied Koopmanism. Chaos 22, 047510.Google Scholar
Chillà, F. & Schumacher, J. 2012 New perspectives in turbulent Rayleigh–Bénard convection. Eur. Phys. J. E 35, 58.Google Scholar
Chong, K.-L. & Xia, K.-Q. 2016 Exploring the severely confined regime in Rayleigh–Bénard convection. J. Fluid Mech. 805, R4.Google Scholar
Coifman, R. R. & Lafon, S. 2006a Diffusion maps. Appl. Comput. Harmon. Anal. 21, 530.Google Scholar
Coifman, R. R. & Lafon, S. 2006b Geometric harmonics: a novel tool for multiscale out-of-sample extension of empirical functions. Appl. Comput. Harmon. Anal. 21, 3152.Google Scholar
Das, S. & Giannakis, D.2017 Delay-coordinate maps and the spectra of Koopman operators. arXiv:1706.08544.Google Scholar
Daya, Z. A. & Ecke, R. E. 2001 Does turbulent convection feel the shape of the container? Phys. Rev. Lett. 87, 184501.CrossRefGoogle Scholar
Dellnitz, M. & Junge, O. 1999 On the approximation of complicated dynamical behavior. SIAM J. Numer. Anal. 36, 491515.Google Scholar
Eisner, T., Farkas, B., Haase, M. & Nagel, R. 2015 Operator Theoretic Aspects of Ergodic Theory, Graduate Texts in Mathematics, vol. 272. Springer.CrossRefGoogle Scholar
Emran, M. S. & Schumacher, J. 2010 Lagrangian tracer dynamics in a closed cylindrical turbulent convection cell. Phys. Rev. E 82, 016303.Google Scholar
Emran, M. S. & Schumacher, J. 2015 Large-scale mean patterns in turbulent convection. J. Fluid Mech. 776, 96108.Google Scholar
Foroozani, N., Niemela, J. J., Armenio, V. & Sreenivasan, K. R. 2014 Influence of container shape on scaling of turbulent fluctuations in convection. Phys. Rev. E 90, 063003.Google ScholarPubMed
Foroozani, N., Niemela, J. J., Armenio, V. & Sreenivasan, K. R. 2017 Reorientations of the large-scale flow in turbulent convection in a cube. Phys. Rev. E 95, 033107.Google Scholar
Franke, B., Hwang, C.-R., Pai, H.-M. & Sheu, S. J. 2010 The behavior of the spectral gap under growing drift. Trans. Am. Math. Soc. 362 (3), 13251350.Google Scholar
Ghil, M., Allen, M. R., Dettinger, M. D., Ide, K., Kondrashov, D., Mann, M. E., Robertson, A. W., Saunders, A., Tian, Y., Varadi, F. & Yiou, P. 2002 Advanced spectral methods for climatic time series. Rev. Geophys. 40 (1), 3.CrossRefGoogle Scholar
Giannakis, D. 2017 Data-driven spectral decomposition and forecasting of ergodic dynamical systems. Appl. Comput. Harmon. Anal. in press, doi:10.1016/j.acha.2017.09.001.Google Scholar
Giannakis, D. & Majda, A. J. 2011 Time series reconstruction via machine learning: revealing decadal variability and intermittency in the North Pacific sector of a coupled climate model. In Conference on Intelligent Data Understanding 2011. Mountain View, California. NASA.Google Scholar
Giannakis, D. & Majda, A. J. 2012 Nonlinear Laplacian spectral analysis for time series with intermittency and low-frequency variability. Proc. Natl. Acad. Sci. 109 (7), 22222227.Google Scholar
Giannakis, D. & Majda, A. J. 2013 Nonlinear Laplacian spectral analysis: capturing intermittent and low-frequency spatiotemporal patterns in high-dimensional data. Stat. Anal. Data Min. 6 (3), 180194.Google Scholar
Giannakis, D., Ourmazd, A., Slawinska, J. & Zhao, Z.2017 Spatiotemporal pattern extraction by spectral analysis of vector-valued observables. arXiv:1711.02798.Google Scholar
Giannakis, D., Slawinska, J. & Zhao, Z. 2015 Spatiotemporal feature extraction with data-driven Koopman operators. J. Mach. Learn. Res. 44, 103115.Google Scholar
Horn, S. & Schmid, P. J. 2017 Prograde, retrograde, and oscillatory modes in rotating Rayleigh–Bénard convection. J. Fluid Mech. 831, 182211.CrossRefGoogle Scholar
Kaczorowski, M. & Xia, K.-Q. 2013 Turbulent flow in the bulk of Rayleigh–Bénard convection: small-scale properties in a cubic cell. J. Fluid Mech. 722, 596617.CrossRefGoogle Scholar
Krasnov, D., Zikanov, O. & Boeck, T. 2011 Comparative study of finite difference approaches in simulation of magnetohydrodynamic turbulence at low magnetic Reynolds number. Comput. Fluids 50, 4659.Google Scholar
von Luxburg, U., Belkin, M. & Bousquet, O. 2008 Consistency of spectral clustering. Ann. Stat. 26 (2), 555586.Google Scholar
Mezić, I. 2005 Spectral properties of dynamical systems, model reduction and decompositions. Nonlinear Dyn. 41, 309325.CrossRefGoogle Scholar
Mezić, I. 2013 Analysis of fluid flows via spectral properties of the Koopman operator. Annu. Rev. Fluid Mech. 45, 357378.Google Scholar
Mezić, I. & Banaszuk, A. 2004 Comparison of systems with complex behavior. Phys. D 197, 101133.Google Scholar
Palmer, R. G. 1982 Broken ergodicity. Adv. Phys. 31, 669735.CrossRefGoogle Scholar
Pauluis, O. & Schumacher, J. 2010 Idealized moist Rayleigh–Bénard convection with piecewise linear equation of state. Commun. Math. Sci. 8, 295319.Google Scholar
Podvin, B. & Sergent, A. 2012 Proper orthogonal decomposition investigation of turbulent Rayleigh–Bénard convection in a rectangular cavity. Phys. Fluids 24, 105106.Google Scholar
Podvin, B. & Sergent, A. 2015 A large-scale investigation of wind reversal in a square Rayleigh–Bénard cell. J. Fluid Mech. 766, 172201.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.Google Scholar
Scheel, J. D. & Schumacher, J. 2014 Local boundary layer scales in turbulent Rayleigh–Bénard convection. J. Fluid Mech. 758, 344373.Google Scholar
Schmid, P. J. 2010 Dynamic mode decomposition of numerical and experimental data. J. Fluid Mech. 656, 528.CrossRefGoogle Scholar
Schmid, P. J. & Sesterhenn, J. L. 2008 Dynamic mode decomposition of numerical and experimental data. In Bulletin of the American Physical Society, 61st APS-DFD Meeting, San Antonio, p. 208.Google Scholar
Shi, N., Emran, M. S. & Schumacher, J. 2012 Boundary layer structure in turbulent Rayleigh–Bénard convection. J. Fluid Mech. 706, 533.Google Scholar
Slawinska, J. & Giannakis, D. 2016 Spatiotemporal pattern extraction with data-driven Koopman operators for convectively coupled equatorial waves. In Proceedings of the 6th International Workshop on Climate Informatics, Boulder, Colorado (ed. Banerjee, A., Ding, W., Dy, J., Lyubchich, V. & Rhines, A.), pp. 4952. National Center for Atmospheric Research.Google Scholar
Slawinska, J., Pauluis, O., Majda, A. J. & Grabowski, W. W. 2014 Multiscale interactions in an idealized Walker circulation: mean circulation and intraseasonal variability. J. Atmos. Sci. 71 (3), 953971.Google Scholar
Song, H., Brown, E., Hawkins, R. & Tong, P. 2014 Dynamics of the large-scale circulation of turbulent thermal convection in a horizontal cylinder. J. Fluid Mech. 740, 136167.CrossRefGoogle Scholar
Tu, J. H., Rowley, C. W., Lucthenburg, C. M., Brunton, S. L. & Kutz, J. N. 2014 On dynamic mode decomposition: theory and applications. J. Comput. Dyn. 1 (2), 391421.Google Scholar
Vautard, R. & Ghil, M. 1989 Singular spectrum analysis in nonlinear dynamics, with applications to paleoclimatic time series. Phys. D 35, 395424.Google Scholar
Williams, M. O., Kevrekidis, I. G. & Rowley, C. W. 2015a A data-driven approximation of the Koopman operator: extending dynamic mode decomposition. J. Nonlinear Sci. 25, 13071346.Google Scholar
Williams, M. O., Rowley, C. M. & Kevrekidis, I. G. 2015b A kernel-based method for data-driven Koopman spectral analysis. J. Comput. Dyn. 2 (2), 247265.CrossRefGoogle Scholar
Young, L.-S. 2002 What are SRB measures, and which dynamical systems have them? J. Stat. Phys. 108, 733754.Google Scholar
Zhao, Z. & Giannakis, D. 2016 Analog forecasting with dynamics-adapted kernels. Nonlinearity 29, 28882939.Google Scholar
Zhou, Q., Xi, H.-D., Zhou, S.-Q., Sun, C. & Xia, K.-Q. 2009 Oscillations of the large-scale circulation in turbulent Rayleigh–Bénard convection: the sloshing mode and its relationship with the torsional mode. J. Fluid Mech. 630, 367390.Google Scholar