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We study the time evolution of velocity and pressure gradients in isotropicturbulence, by quantifying their decorrelation time scales as one follows fluidparticles in the flow. The Lagrangian analysis uses data in a public databasegenerated using direct numerical simulation of the Naiver-Stokes equations, ata Reynolds number 430. It is confirmed that when averaging over the entiredomain, correlation functions decay on timescales on the order of the meanKolmogorov turnover time scale, computed from the globally averaged rate ofdissipation and viscosity. However, when performing the analysis in differentsubregions of the flow, turbulence intermittency leads to large spatialvariability in the decay time scales. Remarkably, excellent collapse of theauto-correlation functions is recovered when using the `local Kolmogorovtime-scale' defined using the locally averaged, rather than the global,dissipation-rate. This provides new evidence for the validity of Kolmogorov'sRefined Similarity Hypothesis, but from a Lagrangian viewpoint that provides anatural frame to describe the dynamical time evolution of turbulence.

Lagrangian Refined Kolmogorov Similarity Hypothesis for Gradient Time-evolution in Turbulent Flows