Invariant Data-Driven Subgrid Stress Modeling on Anisotropic Grids for Large Eddy Simulation

Aviral Prakash; Kenneth E. Jansen; John A. Evans

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arXiv · 2212.00332

Invariant Data-Driven Subgrid Stress Modeling on Anisotropic Grids for Large Eddy Simulation

Aviral Prakash·Kenneth E. Jansen·John A. Evans

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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.

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BibTeX RIS

Aviral Prakash, Kenneth E. Jansen, John A. Evans. 2022-12-01. Invariant Data-Driven Subgrid Stress Modeling on Anisotropic Grids for Large Eddy Simulation. https://arxiv.org/abs/2212.00332

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