Convolutional Neural Networks For Turbulent Model Uncertainty Quantification

TL;DR

This paper introduces a deep learning approach using convolutional neural networks to better quantify uncertainties in turbulence models, aiming to produce more realistic uncertainty bounds than traditional physics-based methods.

Contribution

It presents a novel deep learning strategy employing CNNs to predict optimal perturbations for Reynolds stresses, improving uncertainty quantification in turbulence simulations.

Findings

CNN accurately distinguishes high-fidelity data from model projections.

The method provides more realistic uncertainty bounds compared to physics-based approaches.

Deep learning enhances turbulence model reliability in engineering simulations.

Abstract

Complex turbulent flow simulations are an integral aspect of the engineering design process. The mainstay of these simulations is represented by eddy viscosity based turbulence models. Eddy viscosity models are computationally cheap due to their underlying simplifications, but their predictions are also subject to structural errors. At the moment, the only method available to forecast these uncertainties is the Eigenspace Perturbation Method. This method's strictly physics-based approach frequently results in unreasonably high uncertainty bounds, which drive the creation of extremely cautious designs. To tackle this problem, we employ a strategy based on deep learning. In order to control the perturbations, our trained deep learning models forecast the appropriate amount of disturbance to apply to the anticipated Reynolds stresses. A Convolutional Neural Network is used to carry out this, and it is trained to distinguish between high fidelity data, which is a mapping of flow characteristics, and model projections based on eddy viscosity.