TY - JOUR

T1 - Data-driven turbulence modelling using symbolic regression

AU - Chakrabarty, A.

AU - Yakovenko, S. N.

N1 - Funding Information: The research was carried out within the state assignment of Ministry of Science and Higher Education of the Russian Federation (project No. 121030500149-8). Publisher Copyright: © 2021 Institute of Physics Publishing. All rights reserved.

PY - 2021/12/13

Y1 - 2021/12/13

N2 - The study is focused on the performance of machine-learning methods applied to improve the velocity field predictions in canonical turbulent flows by the Reynolds-averaged Navier-Stokes (RANS) equation models. A key issue here is to approximate the unknown term of the Reynolds stress (RS) tensor needed to close the RANS equations. A turbulent channel flow with the curved backward-facing step on the bottom has the high-fidelity LES data set. It is chosen as the test case to examine possibilities of GEP (gene expression programming) of formulating the enhanced RANS approximations. Such a symbolic regression technique allows us to get the new explicit expressions for the RS anisotropy tensor. Results obtained by the new model produced using GEP are compared with those from the LES data (serving as the target benchmark solution during the machine-learning algorithm training) and from the conventional RANS model with the linear gradient Boussinesq hypothesis for the Reynolds stress tensor.

AB - The study is focused on the performance of machine-learning methods applied to improve the velocity field predictions in canonical turbulent flows by the Reynolds-averaged Navier-Stokes (RANS) equation models. A key issue here is to approximate the unknown term of the Reynolds stress (RS) tensor needed to close the RANS equations. A turbulent channel flow with the curved backward-facing step on the bottom has the high-fidelity LES data set. It is chosen as the test case to examine possibilities of GEP (gene expression programming) of formulating the enhanced RANS approximations. Such a symbolic regression technique allows us to get the new explicit expressions for the RS anisotropy tensor. Results obtained by the new model produced using GEP are compared with those from the LES data (serving as the target benchmark solution during the machine-learning algorithm training) and from the conventional RANS model with the linear gradient Boussinesq hypothesis for the Reynolds stress tensor.

UR - http://www.scopus.com/inward/record.url?scp=85123727079&partnerID=8YFLogxK

U2 - 10.1088/1742-6596/2099/1/012020

DO - 10.1088/1742-6596/2099/1/012020

M3 - Conference article

AN - SCOPUS:85123727079

VL - 2099

JO - Journal of Physics: Conference Series

JF - Journal of Physics: Conference Series

SN - 1742-6588

IS - 1

M1 - 012020

T2 - International Conference on Marchuk Scientific Readings 2021, MSR 2021

Y2 - 4 October 2021 through 8 October 2021

ER -