Development of machine learning techniques to enhance turbulence models

O. Razizadeh, S. N. Yakovenko

Результат исследования: Научные публикации в периодических изданияхстатья по материалам конференциирецензирование


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.

Язык оригиналаанглийский
Номер статьи012012
ЖурналJournal of Physics: Conference Series
Номер выпуска1
СостояниеОпубликовано - 4 янв 2021
СобытиеInternational Conference on Marchuk Scientific Readings 2020, MSR 2020 - Akademgorodok, Novosibirsk, Российская Федерация
Продолжительность: 19 окт 202023 окт 2020


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