Physics Constrained Deep Learning For Turbulence Model Uncertainty Quantification

TL;DR

This paper introduces a physics-constrained deep learning framework that enhances turbulence model uncertainty quantification by combining physics-based eigenspace perturbations with deep learning guidance for improved calibration.

Contribution

It presents a novel deep learning approach that controls the spatial variation of perturbations, improving upon existing physics-based methods for turbulence uncertainty estimation.

Findings

Enhanced uncertainty calibration over traditional physics-based methods

Deep learning effectively guides perturbation modulation

Framework improves reliability of turbulence simulations

Abstract

Engineering design and scientific analysis rely upon computer simulations of turbulent fluid flows using turbulence models. These turbulence models are empirical and approximate, leading to large uncertainties in their predictions that hamper scientific and engineering advances. We outline a Physics Constrained Deep Learning framework to estimate turbulence model uncertainties using physics based Eigenspace Perturbations along with Deep Learning based guidance. The Deep Learning based modulation controls the spatial variation in perturbation magnitude to improve the calibration of uncertainty estimates over the state of the art physics based methods.