Machine Learning Methods for Development of Data-Driven Turbulence Models

The implementation of the machine learning methods of convolutional neural network combined with support vector machines 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, and machine learning algorithms based on available high-fidelity data sets for canonical flow cases obtained from DNS and measurements can be helpful for this. The application of these algorithms for a fully-developed turbulent channel flow between parallel walls, with periodic hills and for other cases is considered.