Enhancement of Rans Models by Means of the Tensor Basis Random Forest for Turbulent Flows in Two-Dimensional Channels with Bumps

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Enhancement of Rans Models by Means of the Tensor Basis Random Forest for Turbulent Flows in Two-Dimensional Channels with Bumps. / Bernard, A.; Yakovenko, S. N.

In: Journal of Applied Mechanics and Technical Physics, Vol. 64, No. 3, 06.2023, p. 437-441.

Research output: Contribution to journal › Article › peer-review

BibTeX

@article{14b30e5305b64cf188f7f58491022d01,

title = "Enhancement of Rans Models by Means of the Tensor Basis Random Forest for Turbulent Flows in Two-Dimensional Channels with Bumps",

abstract = "DNS and RANS computation results for flows in two-dimensional channels with bumps are processed to generate input and output data for a machine learning method aimed to enhance the Reynolds stress anisotropy model and, thus, improve the RANS approach accuracy. The tensor basis random forest method is chosen as a machine learning tool. The prediction of the new model for the Reynolds stress anisotropy tensor is in better agreement with DNS data for two channel flow geometries than those obtained by the conventional linear eddy viscosity model.",

keywords = "Reynolds stress, machine learning, random forest, turbulence modeling",

author = "A. Bernard and Yakovenko, {S. N.}",

note = "The research was carried out within the state assignment of the Ministry of Science and Higher Education of the Russian Federation. Публикация для корректировки.",

year = "2023",

month = jun,

doi = "10.1134/S0021894423030094",

language = "English",

volume = "64",

pages = "437--441",

journal = "Journal of Applied Mechanics and Technical Physics",

issn = "0021-8944",

publisher = "Maik Nauka-Interperiodica Publishing",

number = "3",

}

RIS

TY - JOUR

T1 - Enhancement of Rans Models by Means of the Tensor Basis Random Forest for Turbulent Flows in Two-Dimensional Channels with Bumps

AU - Bernard, A.

AU - Yakovenko, S. N.

N1 - The research was carried out within the state assignment of the Ministry of Science and Higher Education of the Russian Federation. Публикация для корректировки.

PY - 2023/6

Y1 - 2023/6

N2 - DNS and RANS computation results for flows in two-dimensional channels with bumps are processed to generate input and output data for a machine learning method aimed to enhance the Reynolds stress anisotropy model and, thus, improve the RANS approach accuracy. The tensor basis random forest method is chosen as a machine learning tool. The prediction of the new model for the Reynolds stress anisotropy tensor is in better agreement with DNS data for two channel flow geometries than those obtained by the conventional linear eddy viscosity model.

AB - DNS and RANS computation results for flows in two-dimensional channels with bumps are processed to generate input and output data for a machine learning method aimed to enhance the Reynolds stress anisotropy model and, thus, improve the RANS approach accuracy. The tensor basis random forest method is chosen as a machine learning tool. The prediction of the new model for the Reynolds stress anisotropy tensor is in better agreement with DNS data for two channel flow geometries than those obtained by the conventional linear eddy viscosity model.

KW - Reynolds stress

KW - machine learning

KW - random forest

KW - turbulence modeling

UR - https://www.scopus.com/record/display.uri?eid=2-s2.0-85168412190&origin=inward&txGid=9953856bfc148e5afcad9fb6fbd91d06

UR - https://www.mendeley.com/catalogue/9cfe69a1-249d-3c3c-8d60-ebc8207da06f/

U2 - 10.1134/S0021894423030094

DO - 10.1134/S0021894423030094

M3 - Article

VL - 64

SP - 437

EP - 441

JO - Journal of Applied Mechanics and Technical Physics

JF - Journal of Applied Mechanics and Technical Physics

SN - 0021-8944

IS - 3

ER -

ID: 59618477