Machine learning adaptation for laminar and turbulent flows: applications to high order discontinuous Galerkin solvers
Abstract
We present a machine learning-based mesh refinement technique for steady and unsteady flows. The clustering technique proposed by Otmani et al. arXiv:2207.02929 [physics.flu-dyn] is used to mark the viscous and turbulent regions for the flow past a cylinder at Re=40 (steady laminar flow) and Re=3900 (unsteady turbulent flow). Within this clustered region, we increase the polynomial order to show that it is possible to obtain similar levels of accuracy to a uniformly refined mesh. The method is effective as the clustering successfully identifies the two flow regions, a viscous/turbulent dominated region (including the boundary layer and wake) and an inviscid/irrotational region (a potential flow region). The data used within this framework are generated using a high-order discontinuous Galerkin solver, allowing to locally refine the polynomial order (p-refinement) in each element of the clustered region. For the steady laminar test case we are able to reduce the computational cost up to 32% and for the unsteady turbulent case up to 33%.
Keywords
Cite
@article{arxiv.2209.02401,
title = {Machine learning adaptation for laminar and turbulent flows: applications to high order discontinuous Galerkin solvers},
author = {Kenza Tlales and Kheir-Eddine Otmani and Gerasimos Ntoukas and Gonzalo Rubio and Esteban Ferrer},
journal= {arXiv preprint arXiv:2209.02401},
year = {2022}
}