Development of machine learning techniques to enhance turbulence models

O. Razizadeh, S. N. Yakovenko

Research output: Contribution to journalConference articlepeer-review

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.

Original languageEnglish
Article number012012
JournalJournal of Physics: Conference Series
Volume1715
Issue number1
DOIs
Publication statusPublished - 4 Jan 2021
EventInternational Conference on Marchuk Scientific Readings 2020, MSR 2020 - Akademgorodok, Novosibirsk, Russian Federation
Duration: 19 Oct 202023 Oct 2020

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