Modeling the pressure Hessian and viscous Laplacian in Turbulence: comparisons with DNS and implications on velocity gradient dynamics

Abstract

Modeling the velocity gradient tensor A along Lagrangian trajectories in turbulent flow requires closures for the pressure Hessian and viscous Laplacian of A. Based on an Eulerian-Lagrangian change of variables and the so-called Recent Fluid Deformation closure, such models were proposed recently. The resulting stochastic model was shown to reproduce many geometric and anomalous scaling properties of turbulence. In this work, direct comparisons between model predictions and Direct Numerical Simulation (DNS) data are presented. First, statistical properties of A are described using conditional averages of strain skewness, enstrophy production, energy transfer and vorticity alignments, conditioned upon invariants of A. These conditionally averaged quantities are found to be described accurately by the stochastic model. More detailed comparisons that focus directly on the terms being modeled in the closures are also presented. Specifically, conditional statistics associated with the pressure Hessian and the viscous Laplacian are measured from the model and are compared with DNS. Good agreement is found in strain-dominated regions. However, some features of the pressure Hessian linked to rotation dominated regions are not reproduced accurately by the model. Geometric properties such as vorticity alignment with respect to principal axes of the pressure Hessian are mostly predicted well. In particular, the model predicts that an eigenvector of the rate-of-strain will be also an eigenvector of the pressure Hessian, in accord to basic properties of the Euler equations. The analysis identifies under what conditions the Eulerian-Lagrangian change of variables with the Recent Fluid Deformation closure works well, and in which flow regimes it requires further improvements.