Reuse & Permissions

It is not necessary to obtain permission to reuse this article or its components as it is available under the terms of the Creative Commons Attribution 4.0 International license. This license permits unrestricted use, distribution, and reproduction in any medium, provided attribution to the author(s) and the published article's title, journal citation, and DOI are maintained. Please note that some figures may have been included with permission from other third parties. It is your responsibility to obtain the proper permission from the rights holder directly for these figures.

Export citation

Export citation

Choose format for download:

Download Citation
  • Open Access
  • Access by Xinjiang University

Complex network approach to turbulent velocity gradient dynamics: High- and low-probability Lagrangian paths

C. J Keylock1,2,* and M. Carbone2,3,4

  • *Contact author: C.J.Keylock@lboro.ac.uk

Phys. Rev. Fluids 10, 054606 – Published 15 May, 2025

DOI: https://doi.org/10.1103/PhysRevFluids.10.054606

Abstract

Understanding the dynamics of the turbulent velocity gradient tensor (VGT) is essential to gain insights into the Navier-Stokes equations and improve small-scale turbulence modeling. However, characterizing the VGT dynamics conditional on all its relevant invariants in a continuous fashion is extremely difficult. Hence, in this paper, we represent the Lagrangian dynamics using a network where each node represents a unique flow state in the space of the invariants of the VGT. The sign and relative magnitude ranking of the invariants resulting from a Schur decomposition of the VGT determines the discrete flow state vector. Our analysis reveals intriguing features of the resulting network dynamics, such as a grouping of the influential nodes where the eigenvalues of the VGT are real, in the proximity of the right Vieillefosse tail. We relate our complex network approach to the well-established VGT discretization based on the sign of its principal invariants, Q and R, and its discriminant, Δ into six regions representing very different physical and topological states. We study the shortest paths on the network based on the six regions to which their starting and ending nodes belong. This analysis shows that the typically clockwise path on the QR diagram arises in a varying manner: sometimes region transitions are based on multiple pathways of more equal probability, while others are focused on specific nodes that are pivotal in these interregion dynamics. Comparisons to an enhanced Gaussian closure model show that the model captures the out-degree distribution for the nodes well. However, it uses a greater number of nodes to transition from real to complex eigenvalues and is far less efficient at generating clockwise motions that are counter to the restricted Euler dynamics.

View figure in article

Physics Subject Headings (PhySH)

Article Text

References (70)

  1. J. C. Vassilicos, Dissipation in turbulent flows, Annu. Rev. Fluid Mech. 47, 95 (2015).
  2. M. Carbone and A. D. Bragg, Is vortex stretching the main cause of the turbulent energy cascade? J. Fluid Mech. 883, R2 (2020).
  3. P. L. Johnson, Energy transfer from large to small scales in turbulence by multiscale nonlinear strain and vorticity interactions, Phys. Rev. Lett. 124, 104501 (2020).
  4. D. Buaria, A. Pumir, E. Bodenschatz, and P. K. Yeung, Extreme velocity gradients in turbulent flows, New J. Phys. 21, 043004 (2019).
  5. A. Vela-Martín, The energy cascade as the origin of intense events in small-scale turbulence, J. Fluid Mech. 937, A13 (2022).
  6. C. Meneveau, Lagrangian dynamics and models of the velocity gradient tensor in turbulent flows, Annu. Rev. Fluid Mech. 43, 219 (2011).
  7. U. Frisch, Turbulence (Cambridge University Press, Cambridge, 1995).
  8. K. Ohkitani, Study of the Navier–Stokes regularity problem with critical norms, Fluid Dyn. Res. 48, 021401 (2016).
  9. T. D. Drivas and T. M. Elgindi, Singularity formation in the incompressible Euler equation in finite and infinite time, EMS Surv. Math. 10, 1 (2023).
  10. E. Miller, Finite-time blowup for a Navier-Stokes model equation for the self-amplification of strain, Analysis & PDE 16, 997 (2023).
  11. P. Vieillefosse, Internal motion of a small element of fluid in an inviscid flow, Physica A 125, 150 (1984).
  12. A. Tsinober, L. Shtilman, and H. Vaisburd, A study of properties of vortex stretching and enstrophy generation in numerical and laboratory turbulence, Fluid Dyn. Res. 21, 477 (1997).
  13. B. J. Cantwell, On the behavior of velocity gradient tensor invariants in direct numerical simulations of turbulence, Phys. Fluids 5, 2008 (1993).
  14. A. Ooi, J. Martin, J. Soria, and M. S. Chong, A study of the evolution and characteristics of the invariants of the velocity gradient tensor in isotropic turbulence, J. Fluid Mech. 381, 141 (1999).
  15. J. M. Chacin and B. J. Cantwell, Dynamics of a low Reynolds number turbulent boundary layer, J. Fluid Mech. 404, 87 (2000).
  16. P. L. Johnson and M. Wilczek, Multiscale velocity gradients in turbulence, Annu. Rev. Fluid Mech. 56, 463 (2024).
  17. K. Ohkitani and S. Kishiba, Nonlocal nature of vortex stretching in an inviscid fluid, Phys. Fluids 7, 411 (1995).
  18. P. Vieillefosse, Local interaction between vorticity and shear in a perfect incompressible fluid, J. Phys. France 43, 837 (1982).
  19. B. J. Cantwell, Exact solution of a restricted Euler equation for the velocity gradient tensor, Phys. Fluids A 4, 782 (1992).
  20. J. H. Chen, M. S. Chong, J. Soria, R. Sondergaard, A. E. Perry, M. M. Rogers, R. D. Moser, and B. J. Cantwell, A study of the topology of dissipating motions in direct numerical simulations of time-developing compressible and incompressible mixing layers Phys. Fluids 6, 871 (1994).
  21. S. S. Girimaji and S. B. Pope, A diffusion model for velocity gradients in turbulence, Phys. Fluids 2, 242 (1990).
  22. J. Martín, A. Ooi, M. S. Chong, and J. Soria, Dynamics of the velocity gradient tensor invariants in isotropic turbulence, Phys. Fluids 10, 2336 (1998).
  23. L. Chevillard, C. Meneveau, L. Biferale, and F. Toschi, Modeling the pressure Hessian and viscous Laplacian in turbulence: Comparisons with direct numerical simulation and implications on velocity gradient dynamics, Phys. Fluids 20, 101504 (2008).
  24. M. Wilczek and C. Meneveau, Pressure Hessian and viscous contributions to velocity gradient statistics based on Gaussian random fields, J. Fluid Mech. 756, 191 (2014).
  25. P. L. Johnson and C. Meneveau, A closure for Lagrangian velocity gradient evolution in turbulence using recent-deformation mapping of initially Gaussian fields, J. Fluid Mech. 804, 387 (2016).
  26. D. Buaria and K. R. Sreenivasan, Forecasting small-scale dynamics of fluid turbulence using deep neural networks, Proc. Natl. Acad. Sci. USA 120, e2305765120 (2023).
  27. M. Carbone, V. J. Peterhans, A. S. Ecker, and M. Wilczek, Tailor-designed models for the turbulent velocity gradient through normalizing flow, Phys. Rev. Lett. 133, 184001 (2024).
  28. B. Sharma, R. Das, and S. S. Girimaji, Local vortex line topology and geometry in turbulence, J. Fluid Mech. 924, A13 (2021).
  29. V. Latora, V. Nicosia, and G. Russo, Complex Networks: Principles, Methods and Applications (Cambridge University Press, New York, 2017).
  30. M. E. J. Newman, The structural and function of complex networks, SIAM Rev. 45, 167 (2003).
  31. X. Xiao, H. Chen, and P. Bogdan, Deciphering the generating rules and functionalities of complex networks, Sci. Rep. 11, 22964 (2021).
  32. A. G. Nair and K. Taira, Network-theoretic approach to sparsified discrete vortex dynamics, J. Fluid Mech. 768, 549 (2015).
  33. K. Taira, A. G. Nair, and S. L. Brunton, Network structure of two-dimensional decaying isotropic turbulence, J. Fluid Mech. 795, R2 (2016).
  34. S. Scarsoglio, G. Iacobello, and L. Ridolfi, Complex networks unveiling spatial patterns in turbulence, Int. J. Bifurcation Chaos 26, 1650223 (2016).
  35. S. Chowdhuri, G. Iacobello, and T. Banerjee, Visibility network analysis of large-scale intermittency in convective surface layer turbulence, J. Fluid Mech. 925, A38 (2021).
  36. G. Iacobello, S. Chowdhuri, L. Ridolfi, L. Rondoni, and S. Scarsoglio, Coherent structures at the origin of time irreversibility in wall turbulence, Commun. Phys. 6, 91 (2023).
  37. E. Ser-Giacomi, V. Rossi, C. López, and E. Hernandez-Garcia, Flow networks: A characterization of geophysical fluid transport, Chaos 25, 036404 (2015).
  38. S. B. Pope, A more general effective-viscosity hypothesis, J. Fluid Mech. 72, 331 (1975).
  39. M. Itskov, Tensor Algebra and Tensor Analysis for Engineers (Springer International Publishing, Cham, 2015).
  40. L. A. Leppin and M. Wilczek, Capturing velocity gradients and particle rotation rates in turbulence, Phys. Rev. Lett. 125, 224501 (2020).
  41. M. Carbone and M. Wilczek, Asymptotic predictions on the velocity gradient statistics in low-Reynolds number random flows: Onset of skewness, intermittency and alignments, arXiv:2305.03454 (2023).
  42. C. J. Keylock, The Schur decomposition of the velocity gradient tensor for turbulent flows, J. Fluid Mech. 848, 876 (2018).
  43. D. Fernex, B. R. Noack, and R. Semaan, Cluster-based network modeling—From snapshots to complex dynamical systems, Sci. Adv. 7, eabf5006 (2021).
  44. G. Iacobello, F. Kaiser, and D. E. Rival, Load estimation in unsteady flows from sparse pressure measurements: Application of transition networks to experimental data, Phys. Fluids 34, 025105 (2022).
  45. P. Chakraborty, S. Balachandar, and R. J. Adrian, On the relationships between local vortex identification schemes, J. Fluid Mech. 535, 189 (2005).
  46. D. Perrone, J. G. M. Kuerten, L. Ridolfi, and S. Scarsoglio, Wall-induced anisotropy effects on turbulent mixing in channel flow: A network-based analysis, Phys. Rev. E 102, 043109 (2020).
  47. J. C. R. Hunt, A. A. Wray, and P. Moin, Eddies, stream, and convergence zones in turbulent flows, Technical Report CTR-S88 (Center for Turbulence Research, Stanford University, 1988), p. 193.
  48. R. Betchov, An inequality concerning the production of vorticity in isotropic turbulence, J. Fluid Mech. 1, 497 (1956).
  49. P. Henrici, Bounds for iterates, inverses, spectral variation and fields of values of non-normal matrices, Numer. Math. 4, 24 (1962).
  50. P. Bonacich, Factoring and weighting approaches to status scores and clique identification, J. Math. Sociol. 2, 113 (1972).
  51. K. K. Nomura and G. K. Post, The structure and dynamics of vorticity and rate of strain in incompressible homogeneous turbulence, J. Fluid Mech. 377, 65 (1998).
  52. M. Carbone and M. Wilczek, Only two Betchov homogeneity constraints exist for isotropic turbulence, J. Fluid Mech. 948, R2 (2022).
  53. Y. Li, E. Perlman, M. Wan, Y. Yang, R. Burns, C. Meneveau, S. Chen, A. Szalay, and G. Eyink, A public turbulence database cluster and applications to study Lagrangian evolution of velocity increments in turbulence, J. Turbul. 9, N31 (2008).
  54. H. Yu, K. Kanov, E. Perlman, J. Graham, E. Frederix, R. Burns, A. Szalay, G. Eyink, and C. Meneveau, Studying Lagrangian dynamics of turbulence using on-demand fluid particle tracking in a public turbulence database, J. Turbul. 13, N12 (2012).
  55. C. J. Keylock, S. N. Lane, and K. S. Richards, Quadrant/octant sequencing and the role of coherent structures in bed load sediment entrainment, JGR Earth Surface 119, 264 (2014).
  56. J. Lawson and J. Dawson, On velocity gradient dynamics and turbulent structure, J. Fluid Mech. 780, 60 (2015).
  57. L. C. Freeman, A set of measures of centrality based upon betweenness, Sociometry 40, 35 (1977).
  58. S. P. Borgatti and M. G. Everett, A graph-theoretic perspective on centrality, Social Netw. 28, 466 (2006).
  59. D. Boley, G. Ranjan, and Z.-L. Zhang, Commute times for a directed graph using an asymmetric Laplacian, Linear Algebra Appl. 435, 224 (2011).
  60. A.-L. Barabási and R. Albert, Emergence of scaling in random networks, Science 286, 509 (1999).
  61. C. J. Keylock, Turbulence at the Lee bound: maximally non-normal vortex filaments and the decay of a local dissipation rate, J. Fluid Mech. 881, 283 (2019).
  62. C. J. Keylock, Synthetic velocity gradient tensors and the identification of significant aspects of the structure of turbulence, Phys. Rev. Fluids 2, 084607 (2017).
  63. Y. Zhou, K. Nagata, Y. Sakai, Y. Ito, and T. Hayase, On the evolution of the invariants of the velocity gradient tensor in single square-grid-generated turbulence, Phys. Fluids 27, 075107 (2015).
  64. F. Harary, Status and contrastatus, Sociometry 22, 23 (1959).
  65. J. Smagorinsky, General circulation experiments with the primitive equations, Mon. Weather Rev. 91, 99 (1963).
  66. M. S. Chong, A. E. Perry, and B. J. Cantwell, A general classification of three-dimensional flow fields, Phys. Fluids A 2, 765 (1990).
  67. H. Xu, X. S. Cai, and C. Liu, Liutex (vortex) core definition and automatic identification for turbulence vortex structures, J. Hydrodyn. 31, 857 (2019).
  68. S. Laizet, J. Nedic, and J. C. Vassilicos, Influence of the spatial resolution on fine-scale features in DNS of turbulence generated by a single square grid, Int. J. Comput. Fluid Dyn. 29, 286 (2015).
  69. B. Lüthi, M. Holzner, and A. Tsinober, Expanding the Q-R space to three dimensions, J. Fluid Mech. 641, 497 (2009).
  70. https://turbulence.idies.jhu.edu/home.

Outline

Information

Sign In to Your Journals Account

Filter

Filter

Article Lookup

Enter a citation