Modeling the pressure Hessian and viscous Laplacian in Turbulence: comparisons with DNS and implications on velocity gradient dynamics
L. Chevillard, C. Meneveau, L. Biferale, F. Toschi

TL;DR
This paper evaluates a stochastic turbulence model for the velocity gradient tensor by comparing its predictions with DNS data, highlighting strengths in strain-dominated regions and limitations in rotation-dominated areas.
Contribution
It provides a detailed validation of the Recent Fluid Deformation closure-based model against DNS, identifying where it performs well and where improvements are needed.
Findings
Model accurately predicts statistical properties conditioned on invariants.
Good agreement in strain-dominated regions for pressure Hessian and viscous Laplacian.
Some discrepancies in rotation-dominated regions, especially for pressure Hessian features.
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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
