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

Understand

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.