Artificial-neural-network-based subgrid-scale models in the strain-rate eigenframe for large-eddy simulation of compressible turbulent channel flow
Artificial-neural-network-based (ANN-based) subgrid-scale (SGS) models for turbulent channel flow often suffer from instability and poor generalization. Here, we propose an ANN-SGS model based on the strain-rate eigenframe and apply it to large eddy simulations of compressible turbulent channel flow. Our results indicate that the newly proposed model can predict flow statistics more accurately than traditional SGS models, and it also exhibits generalization capability for both Reynolds and Mach numbers.
Authors
- Xiao Ren (ORCID: https://orcid.org/0009-0009-6052-5697)
- Jianchun Wang (ORCID: https://orcid.org/0000-0001-5101-7791)
- Dehao Xu (ORCID: https://orcid.org/0000-0003-2442-4150)
- Shiyi Chen (ORCID: https://orcid.org/0000-0002-2155-9167)
Institutions
- Peking University (CN)
- Southern University of Science and Technology (CN)
Publication Details
- Journal
- Physical Review Fluids
- Published
- 2025-01-10
- DOI
- https://doi.org/10.1103/physrevfluids.10.014603
- Citations
- 6
- Primary Topic
- Fluid Dynamics and Turbulent Flows
- Type
- article
- Field-Weighted Citation Impact
- 8.01
Funders
- National Natural Science Foundation of China
- Guangdong Science and Technology Department
- Science, Technology and Innovation Commission of Shenzhen Municipality
- Southern University of Science and Technology