A deep learning framework for turbulence modeling using data assimilation and feature extraction

TL;DR

This paper introduces a deep learning framework that uses data assimilation and feature extraction to improve turbulence modeling, aiming to enhance the accuracy of RANS models in industrial applications.

Contribution

It develops a novel deep learning approach combining CNNs and DNS data to extract features and create improved PDEs for turbulence modeling.

Findings

Features correlate strongly with flow statistics

Improved PDEs outperform classical RANS models

Enhanced prediction accuracy demonstrated

Abstract

Turbulent problems in industrial applications are predominantly solved using Reynolds Averaged Navier Stokes (RANS) turbulence models. The accuracy of the RANS models is limited due to closure assumptions that induce uncertainty into the RANS modeling. We propose the use of deep learning algorithms via convolution neural networks along with data from direct numerical simulations to extract the optimal set of features that explain the evolution of turbulent flow statistics. Statistical tests are used to determine the correlation of these features with the variation in the quantities of interest that are to be predicted. These features are then used to develop improved partial differential equations that can replace classical Reynolds Averaged Navier Stokes models and show improvement in the accuracy of the predictions.