1932

Abstract

Understanding and predicting turbulent flow phenomena remain a challenge for both theory and applications. The nonlinear and nonlocal character of small-scale turbulence can be comprehensively described in terms of the velocity gradients, which determine fundamental quantities like dissipation, enstrophy, and the small-scale topology of turbulence. The dynamical equation for the velocity gradient succinctly encapsulates the nonlinear physics of turbulence; it offers an intuitive description of a host of turbulence phenomena and enables establishing connections between turbulent dynamics, statistics, and flow structure. The consideration of filtered velocity gradients enriches this view to express the multiscale aspects of nonlinearity and flow structure in a formulation directly applicable to large-eddy simulations. Driven by theoretical advances together with growing computational and experimental capabilities, recent activities in this area have elucidated key aspects of turbulence physics and advanced modeling capabilities.

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2024-01-19
2024-04-28
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Literature Cited

  1. Adrian RJ. 1994. Stochastic estimation of conditional structure: a review. Appl. Sci. Res. 53:291–303
    [Google Scholar]
  2. Apolinário GB, Moriconi L, Pereira RM. 2019. Instantons and fluctuations in a Lagrangian model of turbulence. Physica A 514:741–57
    [Google Scholar]
  3. Ashurst WT, Kerstein AR, Kerr RM, Gibson CH. 1987. Alignment of vorticity and scalar gradient with strain rate in simulated Navier–Stokes turbulence. Phys. Fluids 30:82343–53
    [Google Scholar]
  4. Ballouz JG, Johnson PL, Ouellette NT. 2020. Temporal dynamics of the alignment of the turbulent stress and strain rate. Phys. Rev. Fluids 5:114606
    [Google Scholar]
  5. Ballouz JG, Ouellette NT. 2018. Tensor geometry in the turbulent cascade. J. Fluid Mech. 835:1048–64
    [Google Scholar]
  6. Bätge T, Fouxon I, Wilczek M. 2023. Quantitative prediction of sling events in turbulence at high Reynolds numbers. Phys. Rev. Lett. 131:054001
    [Google Scholar]
  7. Bec J, Gustavsson K, Mehlig B. 2024. Statistical models for the dynamics of heavy particles in turbulence. Annu. Rev. Fluid Mech. 56:189213
    [Google Scholar]
  8. Bec J, Khanin K. 2007. Burgers turbulence. Phys. Rep. 447:1/21–66
    [Google Scholar]
  9. Betchov R. 1956. An inequality concerning the production of vorticity in isotropic turbulence. J. Fluid Mech. 1:5497–504
    [Google Scholar]
  10. Bewley GP, Saw EW, Bodenschatz E. 2013. Observation of the sling effect. New J. Phys. 15:083051
    [Google Scholar]
  11. Biferale L, Chevillard L, Meneveau C, Toschi F. 2007. Multiscale model of gradient evolution in turbulent flows. Phys. Rev. Lett. 98:214501
    [Google Scholar]
  12. Borue V, Orszag SA. 1998. Local energy flux and subgrid-scale statistics in three-dimensional turbulence. J. Fluid Mech. 366:1–31
    [Google Scholar]
  13. Buaria D, Bodenschatz E, Pumir A. 2020a. Vortex stretching and enstrophy production in high Reynolds number turbulence. Phys. Rev. Fluids 5:104602
    [Google Scholar]
  14. Buaria D, Pumir A. 2021. Nonlocal amplification of intense vorticity in turbulent flows. Phys. Rev. Res. 3:L042020
    [Google Scholar]
  15. Buaria D, Pumir A. 2022. Vorticity–strain rate dynamics and the smallest scales of turbulence. Phys. Rev. Lett. 128:094501
    [Google Scholar]
  16. Buaria D, Pumir A. 2023. Role of pressure in the dynamics of intense velocity gradients in turbulent flows. J. Fluid Mech 973A23
  17. Buaria D, Pumir A, Bodenschatz E. 2020b. Self-attenuation of extreme events in Navier–Stokes turbulence. Nat. Commun. 11:5852
    [Google Scholar]
  18. Buaria D, Pumir A, Bodenschatz E. 2022. Generation of intense dissipation in high Reynolds number turbulence. Philos. Trans. R. Soc. A 380:221820210088
    [Google Scholar]
  19. Buaria D, Pumir A, Bodenschatz E, Yeung PK. 2019. Extreme velocity gradients in turbulent flows. New J. Phys. 21:043004
    [Google Scholar]
  20. Buaria D, Sreenivasan KR. 2020. Dissipation range of the energy spectrum in high Reynolds number turbulence. Phys. Rev. Fluids 5:092601
    [Google Scholar]
  21. Buaria D, Sreenivasan KR. 2023. Forecasting small scale dynamics of fluid turbulence using deep neural networks. PNAS 120:30e2305765120
    [Google Scholar]
  22. Bürger K, Treib M, Westermann R, Werner S, Lalescu CC et al. 2012. Vortices within vortices: hierarchical nature of vortex tubes in turbulence. arXiv:1210.3325 [physics.flu-dyn]
  23. Burgers JM. 1948. A mathematical model illustrating the theory of turbulence. Adv. Appl. Mech. 1:171–99
    [Google Scholar]
  24. Cantwell BJ. 1992. Exact solution of a restricted Euler equation for the velocity gradient tensor. Phys. Fluids 4:4782–93
    [Google Scholar]
  25. Carbone M, Bragg AD. 2020. Is vortex stretching the main cause of the turbulent energy cascade?. J. Fluid Mech. 883:R2
    [Google Scholar]
  26. Carbone M, Iovieno M, Bragg AD. 2020. Symmetry transformation and dimensionality reduction of the anisotropic pressure Hessian. J. Fluid Mech. 900:A38
    [Google Scholar]
  27. Carbone M, Wilczek M. 2022. Only two Betchov homogeneity constraints exist for isotropic turbulence. J. Fluid Mech. 948:R2
    [Google Scholar]
  28. Chertkov M, Pumir A, Shraiman B. 1999. Lagrangian tetrad dynamics and the phenomenology of turbulence. Phys. Fluids 11:82394–410
    [Google Scholar]
  29. Chevillard L, Meneveau C. 2006. Lagrangian dynamics and statistical geometric structure of turbulence. Phys. Rev. Lett. 97:174501
    [Google Scholar]
  30. Chevillard L, Meneveau C. 2011. Lagrangian time correlations of vorticity alignments in isotropic turbulence: observations and model predictions. Phys. Fluids 23:101704
    [Google Scholar]
  31. Chevillard L, Meneveau C, Biferale L, Toschi F. 2008. Modeling the pressure Hessian and viscous Laplacian in turbulence: comparisons with direct numerical simulation and implications on velocity gradient dynamics. Phys. Fluids 20:101504
    [Google Scholar]
  32. Clark RA, Ferziger JH, Reynolds WC. 1979. Evaluation of subgrid-scale models using an accurately simulated turbulent flow. J. Fluid Mech. 91:11–16
    [Google Scholar]
  33. Constantin P, Iyer G. 2008. A stochastic Lagrangian representation of the three-dimensional incompressible Navier–Stokes equations. Commun. Pure Appl. Math. 61:3330–45
    [Google Scholar]
  34. Danish M, Meneveau C. 2018. Multiscale analysis of the invariants of the velocity gradient tensor in isotropic turbulence. Phys. Rev. Fluids 3:044604
    [Google Scholar]
  35. Das R, Girimaji SS. 2019. On the Reynolds number dependence of velocity-gradient structure and dynamics. J. Fluid Mech. 861:163–79
    [Google Scholar]
  36. Das R, Girimaji SS. 2020. Revisiting turbulence small-scale behavior using velocity gradient triple decomposition. New J. Phys. 22:063015
    [Google Scholar]
  37. Das R, Girimaji SS. 2023. Data-driven model for Lagrangian evolution of velocity gradients in incompressible turbulent flows. arXiv:2304.14529 [physics.flu-dyn]
  38. Davidson PA. 2011. Long-range interactions in turbulence and the energy decay problem. Philos. Trans. R. Soc. A 369:1937796–810
    [Google Scholar]
  39. Davidson PA. 2015. Turbulence: An Introduction for Scientists and Engineers Oxford, UK: Oxford Univ. Press
  40. de Kármán T, Howarth L. 1938. On the statistical theory of isotropic turbulence. Proc. R. Soc. A 164:917192–215
    [Google Scholar]
  41. Doan NAK, Swaminathan N, Davidson PA, Tanahashi M. 2018. Scale locality of the energy cascade using real space quantities. Phys. Rev. Fluids 3:084601
    [Google Scholar]
  42. Douady S, Couder Y, Brachet ME. 1991. Direct observation of the intermittency of intense vorticity filaments in turbulence. Phys. Rev. Lett. 67:983–86
    [Google Scholar]
  43. Drivas TD, Johnson PL, Lalescu CC, Wilczek M. 2017. Large-scale sweeping of small-scale eddies in turbulence: a filtering approach. Phys. Rev. Fluids 2:104603
    [Google Scholar]
  44. Dubrulle B. 2019. Beyond Kolmogorov cascades. J. Fluid Mech. 867:P1
    [Google Scholar]
  45. Eaton J, Fessler J. 1994. Preferential concentration of particles by turbulence. Int. J. Multiph. Flow 20:169–209
    [Google Scholar]
  46. Esmaily-Moghadam M, Mani A. 2016. Analysis of the clustering of inertial particles in turbulent flows. Phys. Rev. Fluids 1:084202
    [Google Scholar]
  47. Eyink GL. 1995. Local energy flux and the refined similarity hypothesis. J. Stat. Phys. 78:1/2335–51
    [Google Scholar]
  48. Eyink GL. 2006. Multi-scale gradient expansion of the turbulent stress tensor. J. Fluid Mech. 549:159–90
    [Google Scholar]
  49. Eyink GL. 2007. Turbulence theory Course Not., Johns Hopkins Univ. Baltimore, MD: https://www.ams.jhu.edu/∼eyink/Turbulence/notes.html
  50. Eyink GL, Sreenivasan KR. 2006. Onsager and the theory of hydrodynamic turbulence. Rev. Mod. Phys. 78:187–135
    [Google Scholar]
  51. Falkovich G, Fouxon A, Stepanov MG. 2002. Acceleration of rain initiation by cloud turbulence. Nature 419:6903151–54
    [Google Scholar]
  52. Fiscaletti D, Elsinga GE, Attili A, Bisetti F, Buxton ORH. 2016. Scale dependence of the alignment between strain rate and rotation in turbulent shear flow. Phys. Rev. Fluids 1:064405
    [Google Scholar]
  53. Friedrich R, Daitche A, Kamps O, Lülff J, Voßkuhle M, Wilczek M. 2012. The Lundgren–Monin–Novikov hierarchy: kinetic equations for turbulence. C. R. Phys. 13:9/10929–53
    [Google Scholar]
  54. Frisch U. 1995. Turbulence: The Legacy of A.N. Kolmogorov. Cambridge, UK: Cambridge Univ. Press
  55. Frisch U, Bec J. 2002. Burgulence. New Trends in Turbulence. Turbulence: Nouveaux Aspects. Les Houches Session LXXIV, 31 July–1 September 2000341–83. Berlin: Springer
    [Google Scholar]
  56. Gao Y, Liu C. 2019. Rortex based velocity gradient tensor decomposition. Phys. Fluids 31:011704
    [Google Scholar]
  57. Germano M. 1992. Turbulence: the filtering approach. J. Fluid Mech. 238:325–36
    [Google Scholar]
  58. Germano M, Piomelli U, Moin P, Cabot WH. 1991. A dynamic subgrid-scale eddy viscosity model. Phys. Fluids A 3:71760–65
    [Google Scholar]
  59. Girimaji SS, Pope SB. 1990. A diffusion model for velocity gradients in turbulence. Phys. Fluids A 2:2242–56
    [Google Scholar]
  60. Gulitski G, Kholmyansky M, Kinzelbach W, Lüthi B, Tsinober A, Yorish S. 2007. Velocity and temperature derivatives in high-Reynolds-number turbulent flows in the atmospheric surface layer. Part 1. Facilities, methods and some general results. J. Fluid Mech. 589:57–81
    [Google Scholar]
  61. Hamlington PE, Schumacher J, Dahm WJA. 2008. Local and nonlocal strain rate fields and vorticity alignment in turbulent flows. Phys. Rev. E 77:026303
    [Google Scholar]
  62. Hill RJ. 2001. Equations relating structure functions of all orders. J. Fluid Mech. 434:379–88
    [Google Scholar]
  63. Holzner M, Guala M, Lüthi B, Liberzon A, Nikitin N et al. 2010. Viscous tilting and production of vorticity in homogeneous turbulence. Phys. Fluids 22:061701
    [Google Scholar]
  64. Ishihara T, Gotoh T, Kaneda Y. 2009. Study of high-Reynolds number isotropic turbulence by direct numerical simulation. Annu. Rev. Fluid Mech. 41:165–80
    [Google Scholar]
  65. Jeong E, Girimaji SS. 2003. Velocity-gradient dynamics in turbulence: effect of viscosity and forcing. Theor. Comput. Fluid. Dyn. 16:6421–32
    [Google Scholar]
  66. Jiménez J, Wray AA, Saffman PG, Rogallo RS. 1993. The structure of intense vorticity in isotropic turbulence. J. Fluid Mech. 255:65–90
    [Google Scholar]
  67. Johnson PL. 2020. Energy transfer from large to small scales in turbulence by multiscale nonlinear strain and vorticity interactions. Phys. Rev. Lett. 124:104501
    [Google Scholar]
  68. Johnson PL. 2021. On the role of vorticity stretching and strain self-amplification in the turbulence energy cascade. J. Fluid Mech. 922:A3
    [Google Scholar]
  69. Johnson PL. 2022. A physics-inspired alternative to spatial filtering for large-eddy simulations of turbulent flows. J. Fluid Mech. 934:A30
    [Google Scholar]
  70. Johnson PL, Meneveau C. 2016a. A closure for Lagrangian velocity gradient evolution in turbulence using recent-deformation mapping of initially Gaussian fields. J. Fluid Mech. 804:387–419
    [Google Scholar]
  71. Johnson PL, Meneveau C. 2016b. Large-deviation statistics of vorticity stretching in isotropic turbulence. Phys. Rev. E 93:033118
    [Google Scholar]
  72. Johnson PL, Meneveau C. 2017a. Restricted Euler dynamics along trajectories of small inertial particles in turbulence. J. Fluid Mech. 816:R2
    [Google Scholar]
  73. Johnson PL, Meneveau C. 2017b. Turbulence intermittency in a multiple-time-scale Navier-Stokes-based reduced model. Phys. Rev. Fluids 2:072601(R)
    [Google Scholar]
  74. Johnson PL, Meneveau C. 2018. Predicting viscous-range velocity gradient dynamics in large-eddy simulations of turbulence. J. Fluid Mech. 837:80–114
    [Google Scholar]
  75. Keylock CJ. 2018. The Schur decomposition of the velocity gradient tensor for turbulent flows. J. Fluid Mech. 848:876–905
    [Google Scholar]
  76. Khurshid S, Donzis DA, Sreenivasan KR. 2018. Energy spectrum in the dissipation range. Phys. Rev. Fluids 3:082601(R)
    [Google Scholar]
  77. Kolmogorov AN. 1941a. Dissipation of energy in the locally isotropic turbulence. Dokl. Akad. Nauk SSSR 32:16–18 (in Russian); 1991. Proc. R. Soc. A 434:15–17 ( English transl. )
    [Google Scholar]
  78. Kolmogorov AN. 1941b. The local structure of turbulence in incompressible viscous fluid for very large Reynolds numbers. Dokl. Akad. Nauk SSSR 30:4301–5 ( in Russian ); 1991. Proc. R. Soc. A 434:9–13 ( English transl. )
    [Google Scholar]
  79. Kolmogorov AN. 1962. A refinement of previous hypotheses concerning the local structure of turbulence in a viscous incompressible fluid at high Reynolds number. J. Fluid Mech. 13:182–85
    [Google Scholar]
  80. Lawson JM, Dawson JR. 2014. A scanning PIV method for fine-scale turbulence measurements. Exp. Fluids 55:121857
    [Google Scholar]
  81. Lawson JM, Dawson JR. 2015. On velocity gradient dynamics and turbulent structure. J. Fluid Mech. 780:60–98
    [Google Scholar]
  82. Leppin LA, Wilczek M. 2020. Capturing velocity gradients and particle rotation rates in turbulence. Phys. Rev. Lett. 125:224501
    [Google Scholar]
  83. Leung T, Swaminathan N, Davidson PA. 2012. Geometry and interaction of structures in homogeneous isotropic turbulence. J. Fluid Mech. 710:453–81
    [Google Scholar]
  84. Li Y, Perlman E, Wan M, Yang Y, Meneveau C et al. 2008. A public turbulence database cluster and applications to study Lagrangian evolution of velocity increments in turbulence. J. Turbul. 9:N31
    [Google Scholar]
  85. Lilly DK. 1992. A proposed modification of the Germano subgrid-scale closure method. Phys. Fluids A 4:3633–35
    [Google Scholar]
  86. Ling J, Kurzawski A, Templeton J. 2016. Reynolds averaged turbulence modelling using deep neural networks with embedded invariance. J. Fluid Mech. 807:155–66
    [Google Scholar]
  87. Lozano-Durán A, Holzner M, Jiménez J. 2016. Multiscale analysis of the topological invariants in the logarithmic region of turbulent channels at a friction Reynolds number of 932. J. Fluid Mech. 803:356–94
    [Google Scholar]
  88. Lund TS, Rogers MM. 1994. An improved measure of strain state probability in turbulent flows. Phys. Fluids 6:51838–47
    [Google Scholar]
  89. Lundgren TS. 1967. Distribution functions in the statistical theory of turbulence. Phys. Fluids 10:5969–75
    [Google Scholar]
  90. Lundgren TS. 1982. Strained spiral vortex model for turbulent fine structure. Phys. Fluids 25:122193–203
    [Google Scholar]
  91. Luo Y, Shi Y, Meneveau C. 2022. Multifractality in a nested velocity gradient model for intermittent turbulence. Phys. Rev. Fluids 7:014609
    [Google Scholar]
  92. Martín J, Dopazo C, Valiño L. 1998. Dynamics of velocity gradient invariants in turbulence: restricted Euler and linear diffusion models. Phys. Fluids 10:82012–25
    [Google Scholar]
  93. Martins Afonso M, Meneveau C. 2010. Recent fluid deformation closure for velocity gradient tensor dynamics in turbulence: timescale effects and expansions. Physica D 239:141241–50
    [Google Scholar]
  94. Maxey MR. 1987. The gravitational settling of aerosol particles in homogeneous turbulence and random flow fields. J. Fluid Mech. 174:441–65
    [Google Scholar]
  95. Meibohm J, Gustavsson K, Mehlig B. 2023. Caustics in turbulent aerosols form along the Vieillefosse line at weak particle inertia. Phys. Rev. Fluids 8:024305
    [Google Scholar]
  96. Meneveau C. 2011. Lagrangian dynamics and models of the velocity gradient tensor in turbulent flows. Annu. Rev. Fluid Mech. 43:219–45
    [Google Scholar]
  97. Meneveau C, Katz J. 2000. Scale-invariance and turbulence models for large-eddy simulation. Annu. Rev. Fluid Mech. 32:1–32
    [Google Scholar]
  98. Meneveau C, Lund TS. 1994. On the Lagrangian nature of the turbulence energy cascade. Phys. Fluids 6:82820–25
    [Google Scholar]
  99. Meneveau C, Sreenivasan KR. 1987. Simple multifractal cascade model for fully developed turbulence. Phys. Rev. Lett. 59:131424–27
    [Google Scholar]
  100. Monin AS. 1967. Equations of turbulent motion. Prikl. Mat. Mekh. 31:61057–68 ( in Russian ); J. Appl. Math. Mech. 31:61057–68 ( English transl. )
    [Google Scholar]
  101. Moser RD, Haering SW, Yalla GR. 2021. Statistical properties of subgrid-scale turbulence models. Annu. Rev. Fluid Mech. 53:255–86
    [Google Scholar]
  102. Nagata R, Watanabe T, Nagata K, da Silva CB. 2020. Triple decomposition of velocity gradient tensor in homogeneous isotropic turbulence. Comput. Fluids 198:104389
    [Google Scholar]
  103. Naso A, Pumir A. 2005. Scale dependence of the coarse-grained velocity derivative tensor structure in turbulence. Phys. Rev. E 72:056318
    [Google Scholar]
  104. Nelkin M. 1994. Universality and scaling in fully developed turbulence. Adv. Phys. 43:2143–81
    [Google Scholar]
  105. Ni R, Ouellette NT, Voth GA. 2014. Alignment of vorticity and rods with Lagrangian fluid stretching in turbulence. J. Fluid Mech. 743:R3
    [Google Scholar]
  106. Novikov EA. 1967. Kinetic equations for a vortex field. Dokl. Akad. Nauk SSSR 177:2299–301 ( in Russian ); 1968. Sov. Phys. Dokl. 12:111006–8 ( English transl. )
    [Google Scholar]
  107. Oboukhov AM. 1962. Some specific features of atmospheric turbulence. J. Fluid Mech. 13:177–81
    [Google Scholar]
  108. Ohkitani K, Kishiba S. 1995. Nonlocal nature of vortex stretching in an inviscid fluid. Phys. Fluids 7:2411–21
    [Google Scholar]
  109. Onsager L. 1949. Statistical hydrodynamics. Nuovo Cim. 6:279–87
    [Google Scholar]
  110. Parashar N, Srinivasan B, Sinha SS. 2020. Modeling the pressure-Hessian tensor using deep neural networks. Phys. Rev. Fluids 5:114604
    [Google Scholar]
  111. Pereira RM, Moriconi L, Chevillard L. 2018. A multifractal model for the velocity gradient dynamics in turbulent flows. J. Fluid Mech. 839:430–67
    [Google Scholar]
  112. Pope SB. 2000. Turbulent Flows Cambridge, UK: Cambridge Univ. Press
  113. Pumir A. 1994. A numerical study of pressure fluctuations in three-dimensional, incompressible, homogeneous, isotropic turbulence. Phys. Fluids 6:62071–83
    [Google Scholar]
  114. Richardson LF. 1922. Weather Prediction by Numerical Process Cambridge, UK: Cambridge Univ. Press
  115. Schumacher J. 2007. Sub-Kolmogorov-scale fluctuations in fluid turbulence. Europhys. Lett. 80:54001
    [Google Scholar]
  116. She ZS, Jackson E, Orszag SA. 1990. Intermittent vortex structures in homogeneous isotropic turbulence. Nature 344:6263226–28
    [Google Scholar]
  117. She ZS, Lévêque E. 1994. Universal scaling laws in fully developed turbulence. Phys. Rev. Lett. 72:3336–39
    [Google Scholar]
  118. Siggia ED. 1981. Numerical study of small-scale intermittency in three-dimensional turbulence. J. Fluid Mech. 107:375–406
    [Google Scholar]
  119. Sundaram S, Collins LR. 1997. Collision statistics in an isotropic particle-laden turbulent suspension. Part 1. Direct numerical simulations. J. Fluid Mech. 335:75–109
    [Google Scholar]
  120. Taylor GI. 1938. Production and dissipation of vorticity in a turbulent fluid. Proc. R. Soc. A 164:91615–23
    [Google Scholar]
  121. Tennekes H, Lumley JL. 1972. A First Course in Turbulence Cambridge, MA: MIT Press
  122. Tian Y, Livescu D, Chertkov M. 2021. Physics-informed machine learning of the Lagrangian dynamics of velocity gradient tensor. Phys. Rev. Fluids 6:094607
    [Google Scholar]
  123. Tom J, Carbone M, Bragg AD. 2021. Exploring the turbulent velocity gradients at different scales from the perspective of the strain-rate eigenframe. J. Fluid Mech. 910:A24
    [Google Scholar]
  124. Townsend AA. 1951. On the fine-scale structure of turbulence. Proc. R. Soc. A 208:1095534–42
    [Google Scholar]
  125. Tsinober A. 2009. An Informal Conceptual Introduction to Turbulence Berlin: Springer
  126. Tsinober A, Kit E, Dracos T. 1992. Experimental investigation of the field of velocity gradients in turbulent flows. J. Fluid Mech. 242:169–92
    [Google Scholar]
  127. van der Bos F, Tao B, Meneveau C, Katz J. 2002. Effects of small-scale turbulent motions on the filtered velocity gradient tensor as deduced from holographic particle image velocimetry measurements. Phys. Fluids 14:72456–74
    [Google Scholar]
  128. Vela-Martín A. 2022. Subgrid-scale models of isotropic turbulence need not produce energy backscatter. J. Fluid Mech. 937:A14
    [Google Scholar]
  129. Vela-Martín A, Jiménez J. 2021. Entropy, irreversibility and cascades in the inertial range of isotropic turbulence. J. Fluid Mech. 915:A36
    [Google Scholar]
  130. Vieillefosse P. 1982. Local interaction between vorticity and shear in a perfect incompressible fluid. J. Phys. 43:6837–42
    [Google Scholar]
  131. Vieillefosse P. 1984. Internal motion of a small element of fluid in an inviscid flow. Physica A 125:1150–62
    [Google Scholar]
  132. Vlaykov DG, Wilczek M. 2019. On the small-scale structure of turbulence and its impact on the pressure field. J. Fluid Mech. 861:422–46
    [Google Scholar]
  133. Wallace JM. 2009. Twenty years of experimental and direct numerical simulation access to the velocity gradient tensor: What have we learned about turbulence?. Phys. Fluids 21:021301
    [Google Scholar]
  134. Wan M, Xiao Z, Meneveau C, Eyink GL, Chen S. 2010. Dissipation-energy flux correlations as evidence for the Lagrangian energy cascade in turbulence. Phys. Fluids 22:061702
    [Google Scholar]
  135. Wilczek M, Meneveau C. 2014. Pressure Hessian and viscous contributions to velocity gradient statistics based on Gaussian random fields. J. Fluid Mech. 756:191–225
    [Google Scholar]
  136. Wilkinson M, Mehlig B. 2005. Caustics in turbulent aerosols. Europhys. Lett. 71:2186–92
    [Google Scholar]
  137. Xu H, Pumir A, Bodenschatz E. 2011. The pirouette effect in turbulent flows. Nat. Phys. 7:9709–12
    [Google Scholar]
  138. Yang PF, Zhou Z, Xu H, He G. 2023. Strain self-amplification is larger than vortex stretching due to an invariant relation of filtered velocity gradients. J. Fluid Mech. 955:A15
    [Google Scholar]
  139. Yeung PK, Ravikumar K. 2020. Advancing understanding of turbulence through extreme-scale computation: intermittency and simulations at large problem sizes. Phys. Rev. Fluids 5:110517
    [Google Scholar]
  140. Yeung PK, Zhai XM, Sreenivasan KR. 2015. Extreme events in computational turbulence. PNAS 112:4112633–38
    [Google Scholar]
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