Articles | Volume 14, issue 6
https://doi.org/10.5194/gmd-14-3769-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-14-3769-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
24 Jun 2021
Development and technical paper |
| 24 Jun 2021
| 24 Jun 2021
Development of a large-eddy simulation subgrid model based on artificial neural networks: a case study of turbulent channel flow
Robin Stoffer
CORRESPONDING AUTHOR
Meteorology and Air Quality Group, Wageningen University, Wageningen, the Netherlands
Caspar M. van Leeuwen
SURFsara, Amsterdam, the Netherlands
Damian Podareanu
SURFsara, Amsterdam, the Netherlands
Valeriu Codreanu
SURFsara, Amsterdam, the Netherlands
Menno A. Veerman
Meteorology and Air Quality Group, Wageningen University, Wageningen, the Netherlands
Martin Janssens
Meteorology and Air Quality Group, Wageningen University, Wageningen, the Netherlands
Oscar K. Hartogensis
Meteorology and Air Quality Group, Wageningen University, Wageningen, the Netherlands
Chiel C. van Heerwaarden
Meteorology and Air Quality Group, Wageningen University, Wageningen, the Netherlands
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Cited
15 citations as recorded by crossref.
- A physics-inspired alternative to spatial filtering for large-eddy simulations of turbulent flows P. Johnson https://doi.org/10.1017/jfm.2021.1150
- Large eddy simulation of turbulent flow over a backward-facing step with a neural-network-based subgrid-scale model J. Park & H. Choi https://doi.org/10.1063/5.0303085
- A posteriori study on wall modeling in large eddy simulation using a nonlocal data-driven approach G. Tabe Jamaat et al. https://doi.org/10.1063/5.0210851
- Differentiable turbulence: Closure as a partial differential equation constrained optimization V. Shankar et al. https://doi.org/10.1103/PhysRevFluids.10.024605
- Large eddy simulation of flow over a circular cylinder with a neural-network-based subgrid-scale model M. Kim et al. https://doi.org/10.1017/jfm.2024.154
- A recursive neural-network-based subgrid-scale model for large eddy simulation: application to homogeneous isotropic turbulence C. Cho et al. https://doi.org/10.1017/jfm.2024.992
- Perspective on machine-learning-based large-eddy simulation H. Choi et al. https://doi.org/10.1103/bxcn-rmdv
- Frame invariant neural network closures for Kraichnan turbulence S. Pawar et al. https://doi.org/10.1016/j.physa.2022.128327
- Neural networks for large eddy simulations of wall-bounded turbulence: numerical experiments and challenges M. Benjamin et al. https://doi.org/10.1140/epje/s10189-023-00314-6
- Two neural network Unet architecture for subfilter stress modeling A. Wu & S. Lele https://doi.org/10.1103/PhysRevFluids.10.014601
- Discretize first, filter next: Learning divergence-consistent closure models for large-eddy simulation S. Agdestein & B. Sanderse https://doi.org/10.1016/j.jcp.2024.113577
- Benchmarking of Machine Learning Ocean Subgrid Parameterizations in an Idealized Model A. Ross et al. https://doi.org/10.1029/2022MS003258
- Learning physics-constrained subgrid-scale closures in the small-data regime for stable and accurate LES Y. Guan et al. https://doi.org/10.1016/j.physd.2022.133568
- Challenges in the modeling and simulation of turbulent supercritical fluid flows and heat transfer T. Wan et al. https://doi.org/10.1007/s44270-024-00005-3
- Nitrate isotopes in catchment hydrology: Insights, ideas and implications for models I. Matiatos et al. https://doi.org/10.1016/j.jhydrol.2023.130326
15 citations as recorded by crossref.
- A physics-inspired alternative to spatial filtering for large-eddy simulations of turbulent flows P. Johnson https://doi.org/10.1017/jfm.2021.1150
- Large eddy simulation of turbulent flow over a backward-facing step with a neural-network-based subgrid-scale model J. Park & H. Choi https://doi.org/10.1063/5.0303085
- A posteriori study on wall modeling in large eddy simulation using a nonlocal data-driven approach G. Tabe Jamaat et al. https://doi.org/10.1063/5.0210851
- Differentiable turbulence: Closure as a partial differential equation constrained optimization V. Shankar et al. https://doi.org/10.1103/PhysRevFluids.10.024605
- Large eddy simulation of flow over a circular cylinder with a neural-network-based subgrid-scale model M. Kim et al. https://doi.org/10.1017/jfm.2024.154
- A recursive neural-network-based subgrid-scale model for large eddy simulation: application to homogeneous isotropic turbulence C. Cho et al. https://doi.org/10.1017/jfm.2024.992
- Perspective on machine-learning-based large-eddy simulation H. Choi et al. https://doi.org/10.1103/bxcn-rmdv
- Frame invariant neural network closures for Kraichnan turbulence S. Pawar et al. https://doi.org/10.1016/j.physa.2022.128327
- Neural networks for large eddy simulations of wall-bounded turbulence: numerical experiments and challenges M. Benjamin et al. https://doi.org/10.1140/epje/s10189-023-00314-6
- Two neural network Unet architecture for subfilter stress modeling A. Wu & S. Lele https://doi.org/10.1103/PhysRevFluids.10.014601
- Discretize first, filter next: Learning divergence-consistent closure models for large-eddy simulation S. Agdestein & B. Sanderse https://doi.org/10.1016/j.jcp.2024.113577
- Benchmarking of Machine Learning Ocean Subgrid Parameterizations in an Idealized Model A. Ross et al. https://doi.org/10.1029/2022MS003258
- Learning physics-constrained subgrid-scale closures in the small-data regime for stable and accurate LES Y. Guan et al. https://doi.org/10.1016/j.physd.2022.133568
- Challenges in the modeling and simulation of turbulent supercritical fluid flows and heat transfer T. Wan et al. https://doi.org/10.1007/s44270-024-00005-3
- Nitrate isotopes in catchment hydrology: Insights, ideas and implications for models I. Matiatos et al. https://doi.org/10.1016/j.jhydrol.2023.130326
Saved (final revised paper)
Latest update: 09 Jun 2026
Short summary
Turbulent flows are often simulated with the large-eddy simulation (LES) technique, which requires subgrid models to account for the smallest scales. Current subgrid models often require strong simplifying assumptions. We therefore developed a subgrid model based on artificial neural networks, which requires fewer assumptions. Our data-driven SGS model showed high potential in accurately representing the smallest scales but still introduced instability when incorporated into an actual LES.
Turbulent flows are often simulated with the large-eddy simulation (LES) technique, which...
