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.
|Journal||Journal of Physics: Conference Series|
|Publication status||Published - 4 Jan 2021|
|Event||International Conference on Marchuk Scientific Readings 2020, MSR 2020 - Akademgorodok, Novosibirsk, Russian Federation|
Duration: 19 Oct 2020 → 23 Oct 2020