Modeling subgrid scale production rates on complex meshes using graph neural networks
Large-eddy simulations (LES) require closures for filtered production rates because the resolved fields do not contain all correlations that govern chemical source terms. We develop a graph neural network (GNN) that predicts filtered species production rates on non-uniform meshes from inputs of filtered mass fractions and temperature. Direct numerical simulations of turbulent premixed hydrogen-methane jet flames with hydrogen fractions of 10%, 50%, and 80% provide the dataset. All fields are Favre filtered with the filter width matched to the operating mesh, and learning is performed on subdomain graphs constructed from mesh-point connectivity. A compact set of reactants, intermediates, a...
| Authors | Priyabrat Dash, Mathis Bode, Konduri Aditya |
|---|---|
| Primary category | physics.flu-dyn |
| Published | 20 March 2026 |
| Last updated at source | 20 March 2026 |
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