Standard

Development of machine learning techniques to enhance turbulence models. / Razizadeh, O.; Yakovenko, S. N.

In: Journal of Physics: Conference Series, Vol. 1715, No. 1, 012012, 04.01.2021.

Research output: Contribution to journal › Conference article › peer-review

Harvard

Razizadeh, O & Yakovenko, SN 2021, 'Development of machine learning techniques to enhance turbulence models', Journal of Physics: Conference Series, vol. 1715, no. 1, 012012. https://doi.org/10.1088/1742-6596/1715/1/012012

APA

Razizadeh, O., & Yakovenko, S. N. (2021). Development of machine learning techniques to enhance turbulence models. Journal of Physics: Conference Series, 1715(1), [012012]. https://doi.org/10.1088/1742-6596/1715/1/012012

Vancouver

Razizadeh O, Yakovenko SN. Development of machine learning techniques to enhance turbulence models. Journal of Physics: Conference Series. 2021 Jan 4;1715(1):012012. doi: 10.1088/1742-6596/1715/1/012012

Author

Razizadeh, O. ; Yakovenko, S. N. / Development of machine learning techniques to enhance turbulence models. In: Journal of Physics: Conference Series. 2021 ; Vol. 1715, No. 1.

BibTeX

@article{fd6a3468ddde45e7820c15670326570a,
title = "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.",
author = "O. Razizadeh and Yakovenko, {S. N.}",
note = "Funding Information: The study was partly supported by Russian Foundation for Basic Research (Project No. 17-01-00332) and is performed within the framework of the Program of Fundamental Scientific Research of the state academies of sciences in 2013-2020 (Project No. AAAA-A17-117030610128-8). Publisher Copyright: {\textcopyright} 2021 Institute of Physics Publishing. All rights reserved. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.; International Conference on Marchuk Scientific Readings 2020, MSR 2020 ; Conference date: 19-10-2020 Through 23-10-2020",
year = "2021",
month = jan,
day = "4",
doi = "10.1088/1742-6596/1715/1/012012",
language = "English",
volume = "1715",
journal = "Journal of Physics: Conference Series",
issn = "1742-6588",
publisher = "IOP Publishing Ltd.",
number = "1",

}

RIS

TY - JOUR

T1 - Development of machine learning techniques to enhance turbulence models

AU - Razizadeh, O.

AU - Yakovenko, S. N.

N1 - Funding Information: The study was partly supported by Russian Foundation for Basic Research (Project No. 17-01-00332) and is performed within the framework of the Program of Fundamental Scientific Research of the state academies of sciences in 2013-2020 (Project No. AAAA-A17-117030610128-8). Publisher Copyright: © 2021 Institute of Physics Publishing. All rights reserved. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.

PY - 2021/1/4

Y1 - 2021/1/4

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85100738224&partnerID=8YFLogxK

U2 - 10.1088/1742-6596/1715/1/012012

DO - 10.1088/1742-6596/1715/1/012012

M3 - Conference article

AN - SCOPUS:85100738224

VL - 1715

JO - Journal of Physics: Conference Series

JF - Journal of Physics: Conference Series

SN - 1742-6588

IS - 1

M1 - 012012

T2 - International Conference on Marchuk Scientific Readings 2020, MSR 2020

Y2 - 19 October 2020 through 23 October 2020

ER -

ID: 27880887