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Machine learning methods for development of data-driven turbulence models. / Yakovenko, Sergey N.; Razizadeh, Omid.

High-Energy Processes in Condensed Matter, HEPCM 2020: Proceedings of the XXVII Conference on High-Energy Processes in Condensed Matter, Dedicated to the 90th Anniversary of the Birth of RI Soloukhin. ed. / Vasily M. Fomin. American Institute of Physics Inc., 2020. 030065 (AIP Conference Proceedings; Vol. 2288).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review

Harvard

Yakovenko, SN & Razizadeh, O 2020, Machine learning methods for development of data-driven turbulence models. in VM Fomin (ed.), High-Energy Processes in Condensed Matter, HEPCM 2020: Proceedings of the XXVII Conference on High-Energy Processes in Condensed Matter, Dedicated to the 90th Anniversary of the Birth of RI Soloukhin., 030065, AIP Conference Proceedings, vol. 2288, American Institute of Physics Inc., 27th Conference on High-Energy Processes in Condensed Matter, HEPCM 2020, Novosibirsk, Russian Federation, 29.06.2020. https://doi.org/10.1063/5.0028572

APA

Yakovenko, S. N., & Razizadeh, O. (2020). Machine learning methods for development of data-driven turbulence models. In V. M. Fomin (Ed.), High-Energy Processes in Condensed Matter, HEPCM 2020: Proceedings of the XXVII Conference on High-Energy Processes in Condensed Matter, Dedicated to the 90th Anniversary of the Birth of RI Soloukhin [030065] (AIP Conference Proceedings; Vol. 2288). American Institute of Physics Inc.. https://doi.org/10.1063/5.0028572

Vancouver

Yakovenko SN, Razizadeh O. Machine learning methods for development of data-driven turbulence models. In Fomin VM, editor, High-Energy Processes in Condensed Matter, HEPCM 2020: Proceedings of the XXVII Conference on High-Energy Processes in Condensed Matter, Dedicated to the 90th Anniversary of the Birth of RI Soloukhin. American Institute of Physics Inc. 2020. 030065. (AIP Conference Proceedings). doi: 10.1063/5.0028572

Author

Yakovenko, Sergey N. ; Razizadeh, Omid. / Machine learning methods for development of data-driven turbulence models. High-Energy Processes in Condensed Matter, HEPCM 2020: Proceedings of the XXVII Conference on High-Energy Processes in Condensed Matter, Dedicated to the 90th Anniversary of the Birth of RI Soloukhin. editor / Vasily M. Fomin. American Institute of Physics Inc., 2020. (AIP Conference Proceedings).

BibTeX

@inproceedings{0d364ef5761c4f608fff66cfd2e32cd8,
title = "Machine learning methods for development of data-driven turbulence models",
abstract = "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.",
keywords = "SIMULATION, FLOW",
author = "Yakovenko, {Sergey N.} and Omid Razizadeh",
note = "Funding Information: The present study is partly supported by Russian Foundation for Basic Research (Project No. 17-01-00332) and performed within the framework of the Program of Fundamental Scientific Research of the state academies of sciences in 2013-2020 (Project No. ȺȺȺȺ-Ⱥ17-117030610128-8). Publisher Copyright: {\textcopyright} 2020 Author(s). Copyright: Copyright 2020 Elsevier B.V., All rights reserved.; 27th Conference on High-Energy Processes in Condensed Matter, HEPCM 2020 ; Conference date: 29-06-2020 Through 03-07-2020",
year = "2020",
month = oct,
day = "26",
doi = "10.1063/5.0028572",
language = "English",
series = "AIP Conference Proceedings",
publisher = "American Institute of Physics Inc.",
editor = "Fomin, {Vasily M.}",
booktitle = "High-Energy Processes in Condensed Matter, HEPCM 2020",

}

RIS

TY - GEN

T1 - Machine learning methods for development of data-driven turbulence models

AU - Yakovenko, Sergey N.

AU - Razizadeh, Omid

N1 - Funding Information: The present study is partly supported by Russian Foundation for Basic Research (Project No. 17-01-00332) and performed within the framework of the Program of Fundamental Scientific Research of the state academies of sciences in 2013-2020 (Project No. ȺȺȺȺ-Ⱥ17-117030610128-8). Publisher Copyright: © 2020 Author(s). Copyright: Copyright 2020 Elsevier B.V., All rights reserved.

PY - 2020/10/26

Y1 - 2020/10/26

N2 - 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.

AB - 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.

KW - SIMULATION

KW - FLOW

UR - http://www.scopus.com/inward/record.url?scp=85096722207&partnerID=8YFLogxK

U2 - 10.1063/5.0028572

DO - 10.1063/5.0028572

M3 - Conference contribution

AN - SCOPUS:85096722207

T3 - AIP Conference Proceedings

BT - High-Energy Processes in Condensed Matter, HEPCM 2020

A2 - Fomin, Vasily M.

PB - American Institute of Physics Inc.

T2 - 27th Conference on High-Energy Processes in Condensed Matter, HEPCM 2020

Y2 - 29 June 2020 through 3 July 2020

ER -

ID: 27123932