Neural Network for Subgrid Turbulence Modeling for Large Eddy Simulations

TL;DR

This paper introduces a neural network-based subgrid turbulence model for Large Eddy Simulations, integrating data-driven approaches with traditional physics to improve turbulence modeling accuracy.

Contribution

It presents a comprehensive workflow combining data generation, a priori learning, and a posteriori testing for neural network-based turbulence modeling in LES.

Findings

Enhanced turbulence modeling accuracy in LES

Successful integration of neural networks with physics-based models

Robust workflow for data-driven turbulence simulation

Abstract

When simulating multiscale systems, where some fields cannot be fully prescribed despite their effects on the simulation's accuracy, closure models are needed. This phenomenon is observed in turbulent fluid dynamics, where Large Eddy Simulations (LES) depict global behavior while turbulence modeling introduces dissipation correspondent to smaller sub-grid scales. Recently, scientific machine learning techniques have emerged to address this problem by integrating traditional (physics-based) equations with data-driven (machine-learned) models, typically coupling numerical solvers with neural networks. This work presents a comprehensive workflow, encompassing high-fidelity data generation and post-processing, a priori learning, and a posteriori testing, where data-driven models enrich differential equations.