Lagrangian acceleration in fully developed turbulence and its Eulerian decompositions

Abstract

We study the properties of various Eulerian contributions to fluid particle acceleration by using well-resolved direct numerical simulations of isotropic turbulence, with the grid resolution as high as $12288^3$ and the Taylor-scale Reynolds number $R_{\lambda }$ in the range between 140 and 1300. The variance of convective acceleration, when normalized by Kolmogorov scales, increases linearly with $R_{\lambda }$, consistent with simple theoretical arguments, but very strongly differing from phenomenological predictions of Kolmogorov's hypothesis as well as Eulerian multifractal models. The scaling of the local acceleration is also linear $R_{\lambda }$ to the leading order, but more complex in detail. The strong cancellation between the local and convective acceleration -- faithful to the random sweeping hypothesis -- results in the variance of the Lagrangian acceleration increasing only as $R_{\lambda }^{0.25}$, as recently shown by Buaria \& Sreenivasan [Phys. Rev. Lett. 128, 234502 (2022)]. The acceleration variance is dominated by irrotational pressure gradient contributions, whose variance also follows an $R_{\lambda }^{0.25}$ scaling; the solenoidal viscous contributions are relatively small and follow a $R_{\lambda }^{0.13}$, consistent with Eulerian multifractal predictions.

8 pages, 3 figures

References in corpus (8)

• Multifractal statistics of Lagrangian velocity and acceleration in turbulence
• Self-attenuation of extreme events in Navier-Stokes turbulence
• Vorticity-strain rate dynamics and the smallest scales of turbulence
• Generation of intense dissipation in high Reynolds number turbulence
• Scaling of acceleration statistics in high Reynolds number turbulence
• Intermittency of turbulent velocity and scalar fields using 3D local averaging
• Non-local amplification of intense vorticity in turbulent flows
• A highly scalable particle tracking algorithm using partitioned global address space (PGAS) programming for extreme-scale turbulence simulations

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• Role of pressure in generation of intense velocity gradients in turbulent flows
• Twisting vortex lines regularize Navier-Stokes turbulence
• Universality of extreme events in turbulent flows