Physics-Informed Machine Learning of the Lagrangian Dynamics of Velocity Gradient Tensor

TL;DR

This paper develops physics-informed machine learning models for the Lagrangian dynamics of the velocity gradient tensor in turbulence, embedding physical constraints and validated against high-fidelity DNS data.

Contribution

It introduces a novel TBNN-based framework that explicitly incorporates physical invariances to model VGT dynamics across different turbulence scales.

Findings

Improved modeling of pressure Hessian contributions.

Good agreement with DNS data on flow invariants.

Identified challenges in inertial range modeling.

Abstract

Reduced models describing the Lagrangian dynamics of the Velocity Gradient Tensor (VGT) in Homogeneous Isotropic Turbulence (HIT) are developed under the Physics-Informed Machine Learning (PIML) framework. We consider VGT at both Kolmogorov scale and coarse-grained scale within the inertial range of HIT. Building reduced models requires resolving the pressure Hessian and sub-filter contributions, which is accomplished by constructing them using the integrity bases and invariants of VGT. The developed models can be expressed using the extended Tensor Basis Neural Network (TBNN). Physical constraints, such as Galilean invariance, rotational invariance, and incompressibility condition, are thus embedded in the models explicitly. Our PIML models are trained on the Lagrangian data from a high-Reynolds number Direct Numerical Simulation (DNS). To validate the results, we perform a comprehensive out-of-sample test. We observe that the PIML model provides an improved representation for the magnitude and orientation of the small-scale pressure Hessian contributions. Statistics of the flow, as indicated by the joint PDF of second and third invariants of the VGT, show good agreement with the "ground-truth" DNS data. A number of other important features describing the structure of HIT are reproduced by the model successfully. We have also identified challenges in modeling inertial range dynamics, which indicates that a richer modeling strategy is required. This helps us identify important directions for future research, in particular towards including inertial range geometry into TBNN.