Articles | Volume 14, issue 6
https://doi.org/10.5194/gmd-14-3769-2021
https://doi.org/10.5194/gmd-14-3769-2021
Development and technical paper
 | 
24 Jun 2021
Development and technical paper |  | 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, Caspar M. van Leeuwen, Damian Podareanu, Valeriu Codreanu, Menno A. Veerman, Martin Janssens, Oscar K. Hartogensis, and Chiel C. van Heerwaarden

Related authors

Understanding wind-driven melt of patchy snow cover
Luuk D. van der Valk, Adriaan J. Teuling, Luc Girod, Norbert Pirk, Robin Stoffer, and Chiel C. van Heerwaarden
The Cryosphere, 16, 4319–4341, https://doi.org/10.5194/tc-16-4319-2022,https://doi.org/10.5194/tc-16-4319-2022, 2022
Short summary

Related subject area

Atmospheric sciences
Low-level jets in the North and Baltic seas: mesoscale model sensitivity and climatology using WRF V4.2.1
Bjarke T. E. Olsen, Andrea N. Hahmann, Nicolas G. Alonso-de-Linaje, Mark Žagar, and Martin Dörenkämper
Geosci. Model Dev., 18, 4499–4533, https://doi.org/10.5194/gmd-18-4499-2025,https://doi.org/10.5194/gmd-18-4499-2025, 2025
Short summary
SynRad v1.0: a radar forward operator to simulate synthetic weather radar observations from volcanic ash clouds
Vishnu Nair, Anujah Mohanathan, Michael Herzog, David G. Macfarlane, and Duncan A. Robertson
Geosci. Model Dev., 18, 4417–4432, https://doi.org/10.5194/gmd-18-4417-2025,https://doi.org/10.5194/gmd-18-4417-2025, 2025
Short summary
Chempath 1.0: an open-source pathway analysis program for photochemical models
Daniel Garduno Ruiz, Colin Goldblatt, and Anne-Sofie Ahm
Geosci. Model Dev., 18, 4433–4454, https://doi.org/10.5194/gmd-18-4433-2025,https://doi.org/10.5194/gmd-18-4433-2025, 2025
Short summary
PALACE v1.0: Paranal Airglow Line And Continuum Emission model
Stefan Noll, Carsten Schmidt, Patrick Hannawald, Wolfgang Kausch, and Stefan Kimeswenger
Geosci. Model Dev., 18, 4353–4398, https://doi.org/10.5194/gmd-18-4353-2025,https://doi.org/10.5194/gmd-18-4353-2025, 2025
Short summary
Atmospheric moisture tracking with WAM2layers v3
Peter Kalverla, Imme Benedict, Chris Weijenborg, and Ruud J. van der Ent
Geosci. Model Dev., 18, 4335–4352, https://doi.org/10.5194/gmd-18-4335-2025,https://doi.org/10.5194/gmd-18-4335-2025, 2025
Short summary

Cited articles

Bardina, J., Ferziger, J., and Reynolds, W.: Improved subgrid-scale models for large-eddy simulation, in: 13th fluid and plasmadynamics conference, 1357, https://doi.org/10.2514/6.1980-1357, 1980. a
Beck, A., Flad, D., and Munz, C.: Deep neural networks for data-driven LES closure models, J. Comput. Phys., 398, 108910, https://doi.org/10.1016/j.jcp.2019.108910, 2019. a, b, c, d, e, f
Bolton, T. and Zanna, L.: Applications of deep learning to ocean data inference and subgrid parameterization, J. Adv. Model. Earth Sy., 11, 376–399, https://doi.org/10.1029/2018MS001472, 2019. a
Bou-Zeid, E., Meneveau, C., and Parlange, M.: A scale-dependent Lagrangian dynamic model for large eddy simulation of complex turbulent flows, Phys. Fluids, 17, 025105, https://doi.org/10.1063/1.1839152, 2005. a
Download
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.
Share