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Network broadcast analysis and control of turbulent flows

Published online by Cambridge University Press:  08 January 2021

Chi-An Yeh*
Affiliation:
Mechanical and Aerospace Engineering, University of California, Los Angeles, CA90095, USA
Muralikrishnan Gopalakrishnan Meena
Affiliation:
Mechanical and Aerospace Engineering, University of California, Los Angeles, CA90095, USA
Kunihiko Taira
Affiliation:
Mechanical and Aerospace Engineering, University of California, Los Angeles, CA90095, USA
*
Email address for correspondence: cayeh@seas.ucla.edu

Abstract

We present a network-based modal analysis technique that identifies key dynamical paths along which perturbations amplify over a time-varying base flow. This analysis is built upon the Katz centrality, which reveals the flow structures that can effectively spread perturbations over a time-evolving network of vortical interactions on the base flow. Motivated by the resolvent form of the Katz function, we take the singular value decomposition of the resulting communicability matrix, complementing the resolvent analysis for fluid flows. The right-singular vectors, referred to as the broadcast modes, give insights into the sensitive regions where introduced perturbations can be effectively spread and amplified over the entire fluid-flow network that evolves in time. We apply this analysis to a two-dimensional decaying isotropic turbulence. The broadcast mode reveals that vortex dipoles are important structures in spreading perturbations. By perturbing the flow with the principal broadcast mode, we demonstrate the utility of the insights gained from the present analysis for effectively modifying the evolution of turbulent flows. The current network-inspired work presents a novel use of network analysis to guide flow control efforts, in particular for time-varying base flows.

Type
JFM Papers
Copyright
© The Author(s), 2021. Published by Cambridge University Press

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Footnotes

Present address: National Center for Computational Sciences, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA.

References

REFERENCES

Albert, R. & Barabási, A.-L. 2002 Statistical mechanics of complex networks. Rev. Mod. Phys. 74 (1), 4797.CrossRefGoogle Scholar
Aprahamian, M., Higham, D. J. & Higham, N. J. 2016 Matching exponential-based and resolvent-based centrality measures. J. Complex Netw. 4 (2), 157176.CrossRefGoogle Scholar
Barabási, A.-L. 2016 Network Science. Cambridge University Press.Google Scholar
Barabási, A.-L. & Bonabeau, E. 2003 Scale-free networks. Sci. Am. 288 (5), 6069.CrossRefGoogle ScholarPubMed
Batchelor, G. K. & Proudman, I. 1954 The effect of rapid distortion of a fluid in turbulent motion. Q. J. Mech. Appl. Maths 7 (1), 83103.CrossRefGoogle Scholar
Benzi, R., Paladin, G. & Vulpiani, A. 1990 Power spectra in two-dimensional turbulence. Phys. Rev. A 42, 36543656.CrossRefGoogle ScholarPubMed
Boffetta, G. & Ecke, R. E. 2012 Two-dimensional turbulence. Annu. Rev. Fluid Mech. 44 (1), 427451.CrossRefGoogle Scholar
Bracco, A., McWilliams, J. C., Murante, G., Provenzale, A. & Weiss, J. B. 2000 Revisiting freely decaying two-dimensional turbulence at millennial resolution. Phys. Fluids 12 (11), 29312941.CrossRefGoogle Scholar
Brachet, M. E., Meneguzzi, M., Politano, H. & Sulem, P. L. 1988 The dynamics of freely decaying two-dimensional turbulence. J. Fluid Mech. 194, 333349.CrossRefGoogle Scholar
Brockmann, D. & Helbing, D. 2013 The hidden geometry of complex, network-driven contagion phenomena. Science 342 (6164), 13371342.CrossRefGoogle ScholarPubMed
Broder, A., Kumar, R., Maghoul, F., Raghavan, P., Rajagopalan, S., Stata, R., Tomkins, A. & Wiener, J. 2000 Graph structure in the web. Comput. Netw. 33 (1–6), 309320.CrossRefGoogle Scholar
Dorogovtsev, S. 2010 Complex Networks. Oxford University Press.Google Scholar
Firth, J. A., Hellewell, J., Klepac, P., Kissler, S., Kucharski, A. J. & Spurgin, L. G. 2020 Using a real-world network to model localized COVID-19 control strategies. Nat. Med. 26, 16161622.CrossRefGoogle ScholarPubMed
Godavarthi, V., Unni, V. R., Gopalakrishnan, E. A. & Sujith, R. I. 2017 Recurrence networks to study dynamical transitions in a turbulent combustor. Chaos 27 (6), 063113.CrossRefGoogle Scholar
Gopalakrishnan Meena, M., Nair, A. G. & Taira, K. 2018 Network community-based model reduction for vortical flows. Phys. Rev. E 97, 063103.CrossRefGoogle ScholarPubMed
Gopalakrishnan Meena, M. & Taira, K. 2020 Identifying turbulence network connectors for flow modification. J. Fluid Mech. arXiv:2005.02514Google Scholar
Grindrod, P. & Higham, D. J. 2013 A dynamical systems view of network centrality. Proc. R. Soc. Lond. A 470, 20130835.Google Scholar
Grindrod, P., Parsons, M. C., Higham, D. J. & Estrada, E. 2011 Communicability across evolving networks. Phys. Rev. E 83, 046120.CrossRefGoogle ScholarPubMed
Gürcan, Ö. D. 2017 Nested polyhedra model of turbulence. Phys. Rev. E 95 (6), 063102.CrossRefGoogle ScholarPubMed
Hadjighasem, A., Karrasch, D., Teramoto, H. & Haller, G. 2016 Spectral-clustering approach to lagrangian vortex detection. Phys. Rev. E 93 (6), 063107.CrossRefGoogle ScholarPubMed
Haller, G. 2015 Lagrangian coherent structures. Annu. Rev. Fluid Mech. 47 (1), 137162.CrossRefGoogle Scholar
Holmes, P., Lumley, J. L., Berkooz, G. & Rowley, C. W. 2012 Turbulence, Coherent Structures, Dynamical Systems and Symmetry, 2nd edn. Cambridge University Press.CrossRefGoogle Scholar
Hunt, J. C. R. & Carruthers, D. J. 1990 Rapid distortion theory and the ‘problems’ of turbulence. J. Fluid Mech. 212, 497532.CrossRefGoogle Scholar
Hunt, J. C. R., Wray, A. A. & Moin, P. 1988 Eddies, stream, and convergence zones in turbulent flows. Tech. Rep. CTR-S88. Center for Turbulence Research.Google Scholar
Iacobello, G., Scarsoglio, S., Kuerten, J. G. M. & Ridolfi, L. 2019 Lagrangian network analysis of turbulent mixing. J. Fluid Mech. 865, 546562.CrossRefGoogle Scholar
Jeong, J. & Hussain, F. 1995 On the identification of a vortex. J. Fluid Mech. 285, 6994.CrossRefGoogle Scholar
Jiménez, J. 2018 Machine-aided turbulence theory. J. Fluid Mech. 854, R1.CrossRefGoogle Scholar
Jiménez, J. 2020 Monte Carlo science. J. Turbul. 21 (9–10), 544566.CrossRefGoogle Scholar
Jovanović, M. R. 2004 Modeling, analysis, and control of spatially distributed systems. PhD thesis, University of California at Santa Barbara, CA.Google Scholar
Jovanović, M. R. & Bamieh, B. 2005 Componentwise energy amplification in channel flows. J. Fluid Mech. 534, 145183.CrossRefGoogle Scholar
Katz, L. 1953 A new status index derived from sociometric analysis. Psychometrika 18, 3943.CrossRefGoogle Scholar
Krueger, P. S., Hahsler, M., Olinick, E. V., Williams, S. H. & Zharfa, M. 2019 Quantitative classification of vortical flows based on topological features using graph matching. Proc. R. Soc. Lond. A 475 (2228), 20180897.Google ScholarPubMed
Liljeros, F., Edling, C. R., Amaral, L. A. N., Stanley, H. E. & Åberg, Y. 2001 The web of human sexual contacts. Nature 411 (6840), 907908.CrossRefGoogle ScholarPubMed
Lilly, D. K. 1971 Numerical simulation of developing and decaying two-dimensional turbulence. J. Fluid Mech. 45 (2), 395415.CrossRefGoogle Scholar
McKeon, B. J. & Sharma, A. S. 2010 A critical-layer framework for turbulent pipe flow. J. Fluid Mech. 658, 336382.CrossRefGoogle Scholar
McWilliams, J. C. 1990 The vortices of two-dimensional turbulence. J. Fluid Mech. 219, 361385.CrossRefGoogle Scholar
Murayama, S., Kinugawa, H., Tokuda, I. T. & Gotoda, H. 2018 Characterization and detection of thermoacoustic combustion oscillations based on statistical complexity and complex-network theory. Phys. Rev. E 97, 022223.CrossRefGoogle ScholarPubMed
Nair, A. G. & Taira, K. 2015 Network-theoretic approach to sparsified discrete vortex dynamics. J. Fluid Mech. 768, 549571.CrossRefGoogle Scholar
Newman, M. E. J. 2003 The structural and function of complex networks. SIAM Rev. 45 (2), 167256.CrossRefGoogle Scholar
Newman, M. E. J. 2018 Networks. Oxford University Press.CrossRefGoogle Scholar
Pullin, D. I. & Saffman, P. G. 1998 Vortex dynamics in turbulence. Annu. Rev. Fluid Mech. 30 (1), 3151.CrossRefGoogle Scholar
Rowley, C. W. 2005 Model reduction for fluids, using balanced proper orthogonal decomposition. Intl J. Bifurcation Chaos 15 (3), 9971013.CrossRefGoogle Scholar
Scarsoglio, S., Cazzato, F. & Ridolfi, L. 2017 From time-series to complex networks: application to the cerebrovascular flow patterns in atrial fibrillation. Chaos 27 (9), 093107.CrossRefGoogle ScholarPubMed
Schlueter-Kuck, K. & Dabiri, J. O. 2017 Coherent structure colouring: identification of coherent structures from sparse data using graph theory. J. Fluid Mech. 811, 468486.CrossRefGoogle Scholar
Schmid, P. J. 2010 Dynamic mode decomposition of numerical and experimental data. J. Fluid Mech. 656, 528.CrossRefGoogle Scholar
Schmid, P. J. & Henningson, D. S. 2001 Stability and Transition in Shear Flows. Springer.CrossRefGoogle Scholar
Ser-Giacomi, E., Rossi, V., López, C. & Hernández-García, E. 2015 Flow networks: a characterization of geophysical fluid transport. Chaos 25 (3), 036404.CrossRefGoogle ScholarPubMed
Taira, K., Brunton, S. L., Dawson, S. T. M., Rowley, C. W., Colonius, T., McKeon, B. J., Schmidt, O. T., Gordeyev, S., Theofilis, V. & Ukeiley, L. S. 2017 Modal analysis of fluid flows: an overview. AIAA J. 55 (12), 40134041.CrossRefGoogle Scholar
Taira, K., Hemati, M. S., Brunton, S. L., Sun, Y., Duraisamy, K., Bagheri, S., Dawson, S. T. M. & Yeh, C.-A. 2020 Modal analysis of fluid flows: applications and outlook. AIAA J. 58 (3), 991993.CrossRefGoogle Scholar
Taira, K., Nair, A. G. & Brunton, S. L. 2016 Network structure of two-dimensional decaying isotropic turbulence. J. Fluid Mech. 795, R2.CrossRefGoogle Scholar
Theofilis, V. 2011 Global linear instability. Annu. Rev. Fluid Mech. 43, 319352.CrossRefGoogle Scholar
Trefethen, L. N., Trefethen, A. E., Reddy, S. C. & Driscoll, T. A. 1993 Hydrodynamic stability without eigenvalues. Science 261 (5121), 578584.CrossRefGoogle ScholarPubMed
Yeh, C.-A., Benton, S. I., Taira, K. & Garmann, D. J. 2020 Resolvent analysis of an airfoil laminar separation bubble at $Re = 500,000$. Phys. Rev. Fluids 5, 083906.CrossRefGoogle Scholar
Yeh, C.-A. & Taira, K. 2019 Resolvent-analysis-based design of airfoil separation control. J. Fluid Mech. 867, 572610.CrossRefGoogle Scholar
Zhang, H., Rowley, C. W., Deem, E. A. & Cattafesta, L. N. 2019 Online dynamic mode decomposition for time-varying systems. SIAM J. Appl. Dyn. Syst. 18 (3), 15861609.CrossRefGoogle Scholar