Invariant Data-Driven Subgrid Stress Modeling on Anisotropic Grids for Large Eddy Simulation
Fluid Dynamics
2023-07-18 v2 Computational Physics
Abstract
We present a new approach for constructing data-driven subgrid stress models for large eddy simulation of turbulent flows using anisotropic grids. The key to our approach is a Galilean, rotationally, reflectionally and unit invariant model form that also embeds filter anisotropy in such a way that an important subgrid stress identity is satisfied. We use this model form to train a data-driven subgrid stress model using only a small amount of anisotropically filtered DNS data and a simple and inexpensive neural network architecture. A priori and a posteriori tests indicate that the trained data-driven model generalizes well to filter anisotropy ratios, Reynolds numbers and flow physics outside the training dataset.
Cite
@article{arxiv.2212.00332,
title = {Invariant Data-Driven Subgrid Stress Modeling on Anisotropic Grids for Large Eddy Simulation},
author = {Aviral Prakash and Kenneth E. Jansen and John A. Evans},
journal= {arXiv preprint arXiv:2212.00332},
year = {2023}
}