Data-driven turbulence modelling using symbolic regression

A. Chakrabarty, S. N. Yakovenko

Результат исследования: Научные публикации в периодических изданияхстатья по материалам конференциирецензирование


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.

Язык оригиналаанглийский
Номер статьи012020
ЖурналJournal of Physics: Conference Series
Номер выпуска1
СостояниеОпубликовано - 13 дек 2021
СобытиеInternational Conference on Marchuk Scientific Readings 2021, MSR 2021 - Novosibirsk, Virtual, Российская Федерация
Продолжительность: 4 окт 20218 окт 2021

Предметные области OECD FOS+WOS



Подробные сведения о темах исследования «Data-driven turbulence modelling using symbolic regression». Вместе они формируют уникальный семантический отпечаток (fingerprint).