Development of machine learning techniques to enhance turbulence models

Abstract

The implementation of the machine learning methods of convolutional neural networks to enhance RANS closure models is presented. The RANS models are not universal and accurate, however they are computationally affordable. Finding a way to improve the model predictability will be an advantage. For this, machine learning algorithms based on available high-fidelity data sets for canonical flow cases obtained from DNS and measurements can be helpful. The application of these algorithms for a fully-developed turbulent channel flows with periodic hills, in a square duct and for other cases is considered.