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Capturing Velocity Gradients and Particle Rotation Rates in Turbulence

Leonhard A. Leppin1,2,3 and Michael Wilczek1,2,*

  • 1Max Planck Institute for Dynamics and Self-Organization (MPI DS), Am Faßberg 17, 37077 Göttingen, Germany
  • 2Faculty of Physics, Georg-August-Universität Göttingen, Friedrich-Hund-Platz 1, 37077 Göttingen, Germany
  • 3Max Planck Institute for Plasma Physics, Boltzmannstraße 2, 85748 Garching, Germany

  • *michael.wilczek@ds.mpg.de

Turbulence characteristics and energy distribution in hydraulic jumps downstream of planar gate orifices: a particle image velocimetry experimental study

Xin Guan, Liang Zhong, Jinyang Liu, and Yuheng Wu

Eng. Res. Express 8 075101 (2026)

Dynamical evolution of kinematic properties: The steady forcing case

A. D. Kirwan, Helga S. Huntley, and James Turbett

Physics of Fluids 37 076635 (2025)

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

C. J Keylock and M. Carbone

Phys. Rev. Fluids 10 054606 (2025)

Tailor-Designed Models for the Turbulent Velocity Gradient through Normalizing Flow

M. Carbone, V. J. Peterhans, A. S. Ecker, and M. Wilczek

Phys. Rev. Lett. 133 184001 (2024)

Mixing artificial and natural intelligence: from statistical mechanics to AI and back to turbulence

Michael (Misha) Chertkov

J. Phys. A: Math. Theor. 57 333001 (2024)

Asymptotic predictions on the velocity gradient statistics in low-Reynolds-number random flows: onset of skewness, intermittency and alignments

Maurizio Carbone and Michael Wilczek

J. Fluid Mech. 986 A25 (2024)

Data-driven model for Lagrangian evolution of velocity gradients in incompressible turbulent flows

Rishita Das and Sharath S. Girimaji

J. Fluid Mech. 984 A39 (2024)

Multiscale Velocity Gradients in Turbulence

Perry L. Johnson and Michael Wilczek

Annu. Rev. Fluid Mech. 56 463 (2024)

Forecasting small-scale dynamics of fluid turbulence using deep neural networks

Dhawal Buaria and Katepalli R. Sreenivasan

Proc. Natl. Acad. Sci. U.S.A. 120 e2305765120 (2023)

Homogeneity constraints on the mixed moments of velocity gradient and pressure Hessian in incompressible turbulence

Zhideng Zhou and Ping-Fan Yang

Phys. Rev. Fluids 8 024601 (2023)

Lagrangian model for passive scalar gradients in turbulence

Xiaolong Zhang, Maurizio Carbone, and Andrew D. Bragg

J. Fluid Mech. 964 A39 (2023)

Only two Betchov homogeneity constraints exist for isotropic turbulence

Maurizio Carbone and Michael Wilczek

J. Fluid Mech. 948 R2 (2022)

Physics-informed machine learning of the Lagrangian dynamics of velocity gradient tensor

Yifeng Tian, Daniel Livescu, and Michael Chertkov

Phys. Rev. Fluids 6 094607 (2021)

Shape- and scale-dependent coupling between spheroids and velocity gradients in turbulence

Nimish Pujara, José-Agustín Arguedas-Leiva, Cristian C. Lalescu, Bérenger Bramas, and Michael Wilczek

J. Fluid Mech. 922 R6 (2021)

Exploring the turbulent velocity gradients at different scales from the perspective of the strain-rate eigenframe

Josin Tom, Maurizio Carbone, and Andrew D. Bragg

J. Fluid Mech. 910 A24 (2021)

No title available

Maurizio Carbone and Michael Wilczek

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