Результаты исследований: Научные публикации в периодических изданиях › статья › Рецензирование
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.
в: Journal of Applied Mechanics and Technical Physics, Том 64, № 3, 06.2023, стр. 437-441.Результаты исследований: Научные публикации в периодических изданиях › статья › Рецензирование
}
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