Machine learning adaptation for laminar and turbulent flows: applications to high order discontinuous Galerkin solvers
Abstrak
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%.
Topik & Kata Kunci
Penulis (5)
Kenza Tlales
Kheir-eddine Otmani
G. Ntoukas
G. Rubio
E. Ferrer
Akses Cepat
- Tahun Terbit
- 2022
- Bahasa
- en
- Total Sitasi
- 8×
- Sumber Database
- Semantic Scholar
- DOI
- 10.48550/arXiv.2209.02401
- Akses
- Open Access ✓