Invariant Data-Driven Subgrid Stress Modeling in the Strain-Rate Eigenframe for Large Eddy Simulation

Abstract

We present a new approach for constructing data-driven subgrid stress models for large eddy simulation of turbulent flows. The key to our approach is representation of model input and output tensors in the filtered strain rate eigenframe. Provided inputs and outputs are selected and non-dimensionalized in a suitable manner, this yields a model form that is symmetric, Galilean invariant, rotationally invariant, reflectionally invariant, and unit invariant. We use this model form to train a simple and efficient neural network model using only one time step of filtered direct numerical simulation data from a forced homogeneous isotropic turbulence simulation. We demonstrate the accuracy of this model as well as the model's ability to generalize to previously unseen filter widths, Reynolds numbers, and flow physics using a priori and a posteriori tests.

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Cited by in corpus (4)

• Learning physics-constrained subgrid-scale closures in the small-data regime for stable and accurate LES
• Deep reinforcement learning for large-eddy simulation modeling in wall-bounded turbulence
• A physics-inspired alternative to spatial filtering for large-eddy simulations of turbulent flows
• Optimal Clipping of Structural Subgrid Stress Closures for Large Eddy Simulation