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Stochastic Lagrangian dynamics of vorticity. Part 1. General theory for viscous, incompressible fluids

Published online by Cambridge University Press:  19 August 2020

Gregory L. Eyink*
Affiliation:
Department of Applied Mathematics and Statistics, The Johns Hopkins University, Baltimore, MD21218, USA Department of Physics and Astronomy, The Johns Hopkins University, Baltimore, MD21218, USA Department of Mechanical Engineering, The Johns Hopkins University, Baltimore, MD21218, USA
Akshat Gupta
Affiliation:
Department of Applied Mathematics and Statistics, The Johns Hopkins University, Baltimore, MD21218, USA
Tamer A. Zaki
Affiliation:
Department of Mechanical Engineering, The Johns Hopkins University, Baltimore, MD21218, USA
*
Email address for correspondence: [email protected]

Abstract

Prior mathematical work of Constantin & Iyer (Commun. Pure Appl. Maths, vol. 61, 2008, pp. 330–345; Ann. Appl. Probab., vol. 21, 2011, pp. 1466–1492) has shown that incompressible Navier–Stokes solutions possess infinitely many stochastic Lagrangian conservation laws for vorticity, backward in time, which generalize the invariants of Cauchy (Sciences mathématiques et physique, vol. I, 1815, pp. 33–73) for smooth Euler solutions. We reformulate this theory for the case of wall-bounded flows by appealing to the Kuz'min (Phys. Lett. A, vol. 96, 1983, pp. 88–90)–Oseledets (Russ. Math. Surv., vol. 44, 1989, p. 210) representation of Navier–Stokes dynamics, in terms of the vortex-momentum density associated to a continuous distribution of infinitesimal vortex rings. The Constantin–Iyer theory provides an exact representation for vorticity at any interior point as an average over stochastic vorticity contributions transported from the wall. We point out relations of this Lagrangian formulation with the Eulerian theory of Lighthill (Boundary layer theory. In Laminar Boundary Layers (ed. L. Rosenhead), 1963, pp. 46–113)–Morton (Geophys. Astrophys. Fluid Dyn., vol. 28, 1984, pp. 277–308) for vorticity generation at solid walls, and also with a statistical result of Taylor (Proc. R. Soc. Lond. A, vol. 135, 1932, pp. 685–702)–Huggins (J. Low Temp. Phys., vol. 96, 1994, pp. 317–346), which connects dissipative drag with organized cross-stream motion of vorticity and which is closely analogous to the ‘Josephson–Anderson relation’ for quantum superfluids. We elaborate a Monte Carlo numerical Lagrangian scheme to calculate the stochastic Cauchy invariants and their statistics, given the Eulerian space–time velocity field. The method is validated using an online database of a turbulent channel-flow simulation (Graham et al., J. Turbul., vol. 17, 2016, pp. 181–215), where conservation of the mean Cauchy invariant is verified for two selected buffer-layer events corresponding to an ‘ejection’ and a ‘sweep’. The variances of the stochastic Cauchy invariants grow exponentially backward in time, however, revealing Lagrangian chaos of the stochastic trajectories undergoing both fluid advection and viscous diffusion.

Type
JFM Papers
Copyright
© The Author(s), 2020. Published by Cambridge University Press

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References

REFERENCES

Anderson, P. W. 1966 Considerations on the flow of superfluid helium. Rev. Mod. Phys. 38 (2), 298.CrossRefGoogle Scholar
Arnold, V. 1966 Sur la géométrie différentielle des groupes de Lie de dimension infinie et ses applications à l'hydrodynamique des fluides parfaits. Annales de l'institut Fourier 16 (1), 319361.CrossRefGoogle Scholar
Arouna, B. 2004 Adaptative Monte Carlo method, a variance reduction technique. Monte Carlo Meth. Applic. 10 (1), 124.CrossRefGoogle Scholar
Batchelor, G. K. 2000 An Introduction to Fluid Dynamics. Cambridge University Press.CrossRefGoogle Scholar
Berrut, J.-P. & Trefethen, L. N. 2004 Barycentric Lagrange interpolation. SIAM Rev. 46 (3), 501517.CrossRefGoogle Scholar
Besse, N. & Frisch, U. 2017 Geometric formulation of the Cauchy invariants for incompressible Euler flow in flat and curved spaces. J. Fluid Mech. 825, 412478.CrossRefGoogle Scholar
Borodin, A. N. & Salminen, P. 2015 Handbook of Brownian Motion – Facts and Formulae. Birkhäuser.Google Scholar
Box, G. E. P. & Muller, M. E. 1958 A note on the generation of random normal deviates. Ann. Math. Statist. 29 (2), 610611.CrossRefGoogle Scholar
Boyer, F. & Fabrie, P. 2012 Mathematical Tools for the Study of the Incompressible Navier–Stokes Equations and Related Models. Springer.Google Scholar
Brenier, Y. 2003 Topics on hydrodynamics and volume preserving maps. In Handbook of Mathematical Fluid Dynamics (ed. Friedlander, S., Serre, D.), vol. 2, pp. 5586. Elsevier.CrossRefGoogle Scholar
Brown, G. L. & Roshko, A. 2012 Turbulent shear layers and wakes. J. Turbul. 13, N51.CrossRefGoogle Scholar
Cauchy, A. L. 1815 Sur l’état du fluide. à une époque quelconque du mouvement. Mémoires extraits des recueils de l'Académie des sciences de l'Institut de France, Théorie de la propagation des ondes à la surface d'un fluide pesant d'une profondeur indéfinie. (Extraits des Mémoires présentés par divers savants à l'Académie royale des Sciences de l'Institut de France et imprimés par son ordre). Sciences mathématiques et physiques. Tome I, 1827 Seconde Partie, pp. 33–73.Google Scholar
Chhikara, R. & Folks, J. L. 1988 The Inverse Gaussian Distribution: Theory: Methodology, and Applications. Taylor & Francis.Google Scholar
Constantin, P. & Iyer, G. 2008 A stochastic Lagrangian representation of the 3-dimensional incompressible Navier–Stokes equations. Commun. Pure Appl. Maths 61, 330345.CrossRefGoogle Scholar
Constantin, P. & Iyer, G. 2011 A stochastic-Lagrangian approach to the Navier–Stokes equations in domains with boundary. Ann. Appl. Probab. 21 (4), 14661492.CrossRefGoogle Scholar
Drivas, T. D. & Eyink, G. L. 2017 A Lagrangian fluctuation–dissipation relation for scalar turbulence. Part II. Wall-bounded flows. J. Fluid Mech. 829, 236279.CrossRefGoogle Scholar
Eyink, G. L. 2008 Turbulent flow in pipes and channels as cross-stream ‘inverse cascades’ of vorticity. Phys. Fluids 20 (12), 125101.CrossRefGoogle Scholar
Eyink, G. L. 2010 Stochastic least-action principle for the incompressible Navier–Stokes equation. Physica D 239 (14), 12361240.CrossRefGoogle Scholar
Eyink, G. L. & Spohn, H. 1993 Negative-temperature states and large-scale, long-lived vortices in two-dimensional turbulence. J. Stat. Phys. 70 (3–4), 833886.CrossRefGoogle Scholar
Eyink, G. L., Gupta, A., Wang, M. & Zaki, T. A. 2019 SCauchy – a Fortran90 code to calculate stochastic Cauchy invariants. Available at: https://github.com/mzwang2012/SCauchy.git.Google Scholar
Eyink, G. L., Gupta, A. & Zaki, T. A. 2020 Stochastic Lagrangian dynamics of vorticity. Part 2. Application to near-wall channel-flow turbulence. J. Fluid Mech. (submitted).Google Scholar
Falkovich, G., Gawedzki, K. & Vergassola, M. 2001 Particles and fields in fluid turbulence. Rev. Mod. Phys. 73 (4), 913.CrossRefGoogle Scholar
Freidlin, M. I. 1985 Functional Integration and Partial Differential Equations. Princeton University Press.CrossRefGoogle Scholar
Friedman, A. 2006 Stochastic Differential Equations and Applications. Dover.Google Scholar
Frisch, U. & Villone, B. 2014 Cauchy's almost forgotten Lagrangian formulation of the Euler equation for 3D incompressible flow. Eur. Phys. J. H 39 (3), 325351.CrossRefGoogle Scholar
Graham, J. et al. 2016 A web services accessible database of turbulent channel flow and its use for testing a new integral wall model for LES. J. Turbul. 17 (2), 181215.CrossRefGoogle Scholar
Guala, M., Liberzon, A., Lüthi, B., Kinzelbach, W. & Tsinober, A. 2006 Stretching and tilting of material lines in turbulence: the effect of strain and vorticity. Phys. Rev. E 73 (3), 036303.CrossRefGoogle ScholarPubMed
Guala, M., Lüthi, B., Liberzon, A., Tsinober, A. & Kinzelbach, W. 2005 On the evolution of material lines and vorticity in homogeneous turbulence. J. Fluid Mech. 533, 339359.CrossRefGoogle Scholar
von Helmholtz, H. 1858 Über Integrale der hydrodynamischen Gleichungen welche den Wirbelbewegungen entsprechen. J. Reine Angew. Math. 55, 2555.Google Scholar
Higham, D. J., Mao, X., Roj, M., Song, Q. & Yin, G. 2013 Mean exit times and the multilevel Monte Carlo method. SIAM/ASA J. Uncertain. Quantification 1 (1), 218.CrossRefGoogle Scholar
Hofmann, N. & Mathé, P. 1997 On quasi-Monte Carlo simulation of stochastic differential equations. Math. Comput. 66 (218), 573589.CrossRefGoogle Scholar
Huggins, E. R. 1970 Energy-dissipation theorem and detailed Josephson equation for ideal incompressible fluids. Phys. Rev. A 1 (2), 332.CrossRefGoogle Scholar
Huggins, E. R. 1994 Vortex currents in turbulent superfluid and classical fluid channel flow, the Magnus effect, and Goldstone boson fields. J. Low Temp. Phys. 96 (5–6), 317346.CrossRefGoogle Scholar
Iyer, G. 2006 A stochastic Lagrangian formulation of the incompressible Navier-Stokes and related transport equations. PhD thesis, The University of Chicago, Chicago, IL.Google Scholar
Johnson, P. L., Hamilton, S. S., Burns, R. & Meneveau, C. 2017 Analysis of geometrical and statistical features of Lagrangian stretching in turbulent channel flow using a database task-parallel particle tracking algorithm. Phys. Rev. Fluids 2 (1), 014605.CrossRefGoogle Scholar
Johnson, P. L. & Meneveau, C. 2016 Large-deviation statistics of vorticity stretching in isotropic turbulence. Phys. Rev. E 93 (3), 033118.CrossRefGoogle ScholarPubMed
Josephson, B. D. 1962 Possible new effects in superconductive tunnelling. Phys. Lett. 1 (7), 251253.CrossRefGoogle Scholar
Keanini, R. G. 2006 Random walk methods for scalar transport problems subject to Dirichlet, Neumann and mixed boundary conditions. Proc. R. Soc. Lond. A 463 (2078), 435460.CrossRefGoogle Scholar
Kelvin, L. 1868 VI.–On vortex motion. Trans. R. Soc. Edin. 25 (1), 217260.Google Scholar
Kim, J., Moin, P. & Moser, R. 1987 Turbulence statistics in fully developed channel flow at low Reynolds number. J. Fluid Mech. 177, 133166.CrossRefGoogle Scholar
Kloeden, P. E. & Platen, E. 2013 Numerical Solution of Stochastic Differential Equations, vol. 23, Springer Science, Business Media.Google Scholar
Koumoutsakos, P. 1999 Vorticity flux control for a turbulent channel flow. Phys. Fluids 11 (2), 248250.CrossRefGoogle Scholar
Kunita, H. 1997 Stochastic Flows and Stochastic Differential Equations, Cambridge Studies in Advanced Mathematics, vol. 24. Cambridge University PressGoogle Scholar
Kuz'min, G. A. 1983 Ideal incompressible hydrodynamics in terms of the vortex momentum density. Phys. Lett. A 96 (2), 8890.CrossRefGoogle Scholar
Lee, M., Malaya, N. & Moser, R. D. 2013 Petascale direct numerical simulation of turbulent channel flow on up to 786K cores. In SC ’13, Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis, Denver, Colorado, November 17–21, 2013, Article 61. ACM.CrossRefGoogle Scholar
Li, Y., Perlman, E., Wan, M., Yang, Y., Meneveau, C., Burns, R., Chen, S., Szalay, A. & Eyink, G. 2008 A public turbulence database cluster and applications to study Lagrangian evolution of velocity increments in turbulence. J. Turbul. 9, N31.CrossRefGoogle Scholar
Lighthill, M. J. 1963 Boundary layer theory. In Laminar Boundary Layers (ed. Rosenhead, L.), pp. 46113. Oxford University Press.Google Scholar
Lüthi, B., Tsinober, A. & Kinzelbach, W. 2005 Lagrangian measurement of vorticity dynamics in turbulent flow. J. Fluid Mech. 528, 87118.CrossRefGoogle Scholar
Lyman, F. A. 1990 Vorticity production at a solid boundary. Appl. Mech. Rev. 43 (8), 157158.Google Scholar
Matsumoto, M. 2011 Mersenne Twister Home Page. Available at: http://www.math.sci.hiroshima-u.ac.jp/~m-mat/MT/emt.html.Google Scholar
Matsumoto, M. & Nishimura, T. 1998 Mersenne twister: a 623-dimensionally equidistributed uniform pseudo-random number generator. ACM Trans. Model. Comput. Simul. (TOMACS) 8 (1), 330.CrossRefGoogle Scholar
Michael, J. R., Schucany, W. R. & Haas, R. W. 1976 Generating random variates using transformations with multiple roots. Am. Stat. 30 (2), 8890.Google Scholar
Morton, B. R. 1984 The generation and decay of vorticity. Geophys. Astrophys. Fluid Dyn. 28 (3–4), 277308.CrossRefGoogle Scholar
Oksendal, B. 2013 Stochastic Differential Equations: An Introduction with Applications. Springer Science, Business Media.Google Scholar
Orszag, S. A. 1971 On the elimination of aliasing in finite-difference schemes by filtering high-wavenumber components. J. Atmos. Sci. 28 (6), 10741074.2.0.CO;2>CrossRefGoogle Scholar
Oseledets, V. I. 1989 On a new way of writing the Navier–Stokes equation. The Hamiltonian formalism. Russ. Math. Surv. 44 (3), 210.CrossRefGoogle Scholar
Packard, R. E. 1998 The role of the Josephson–Anderson equation in superfluid helium. Rev. Mod. Phys. 70 (2), 641.CrossRefGoogle Scholar
Panton, R. L. 1984 Incompressible Flow. John Wiley & Sons.Google Scholar
Rapoport, D. L. 2002 Random diffeomorphisms and integration of the classical Navier–Stokes equations. Rep. Math. Phys. 49 (1), 127.CrossRefGoogle Scholar
Rezakhanlou, F. 2016 Stochastically symplectic maps and their applications to the Navier–Stokes equation. Annales de l'Institut Henri Poincare (C) Non Linear Analysis 33 (1), 122.CrossRefGoogle Scholar
Roberts, P. H. 1972 A Hamiltonian theory for weakly interacting vortices. Mathematika 19 (2), 169179.CrossRefGoogle Scholar
Salmon, R. 1988 Hamiltonian fluid mechanics. Annu. Rev. Fluid Mech. 20 (1), 225256.CrossRefGoogle Scholar
Sawford, B. 2001 Turbulent relative dispersion. Annu. Rev. Fluid Mech. 33 (1), 289317.CrossRefGoogle Scholar
Shraiman, B. I. & Siggia, E. D. 1994 Lagrangian path integrals and fluctuations in random flow. Phys. Rev. E 49 (4), 2912.CrossRefGoogle ScholarPubMed
Taylor, G. I. 1932 The transport of vorticity and heat through fluids in turbulent motion. Proc. R. Soc. Lond. A 135 (828), 685702.Google Scholar
Taylor, G. I. 1937 The statistical theory of isotropic turbulence. J. Aeronaut. Sci. 4 (8), 311315.CrossRefGoogle Scholar
Taylor, G. I. 1938 Production and dissipation of vorticity in a turbulent fluid. Proc. R. Soc. Lond. A 164 (916), 1523.Google Scholar
Taylor, G. I. & Green, A. E. 1937 Mechanism of the production of small eddies from large ones. Proc. R. Soc. Lond. A 158 (895), 499521.Google Scholar
Tur, A. V. & Yanovsky, V. V. 1993 Invariants in dissipationless hydrodynamic media. J. Fluid Mech. 248, 67106.CrossRefGoogle Scholar
Varoquaux, E. 2015 Anderson's considerations on the flow of superfluid helium: some offshoots. Rev. Mod. Phys. 87 (3), 803.CrossRefGoogle Scholar
Weber, H. 1868 Über eine Transformation der hydrodynamischen Gleichungen. J. Reine Angew. Math. 68, 286292.Google Scholar
Wu, J.-Z. & Wu, J.-M. 1993 Interactions between a solid surface and a viscous compressible flow field. J Fluid Mech 254, 183211.CrossRefGoogle Scholar
Wu, J.-Z. & Wu, J.-M. 1996 Vorticity dynamics on boundaries. Adv. Appl. Mech. 32, 119275.CrossRefGoogle Scholar
Wu, J.-Z. & Wu, J.-M. 1998 Boundary vorticity dynamics since Lighthill's 1963 article: review and development. J. Theor. Comput. Fluid Dyn. 10 (1–4), 459474.CrossRefGoogle Scholar
Zhao, H., Wu, J.-Z. & Luo, J.-S. 2004 Turbulent drag reduction by traveling wave of flexible wall. Fluid. Dyn. Res. 34 (3), 175.CrossRefGoogle Scholar
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