Abstract
Manifold-based representations of the thermochemistry are often employed in conjunction with large eddy eimulation (LES) to lower the cost of combustion simulations. This work describes steps taken to implement this modeling approach in PeleLM, a scalable and performance-portable low Mach number flow solver. Most significantly, this includes adapting the projection method used by PeleLM to satisfy the mass conservation constraint for use with manifold-based models. The implementation is designed to be general across manifold-based models, including both those that employ traditional tabulation and those that employ neural networks. An initial demonstration for simple test cases is presented and will be used for performance assessment.
Original language | American English |
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Number of pages | 9 |
State | Published - 2022 |
Event | 2022 WSSCI Spring Technical Meeting - Stanford, California Duration: 21 Mar 2022 → 22 Mar 2022 |
Conference
Conference | 2022 WSSCI Spring Technical Meeting |
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City | Stanford, California |
Period | 21/03/22 → 22/03/22 |
NREL Publication Number
- NREL/CP-2C00-82000
Keywords
- high-performance computing
- large eddy simulation
- reduced-order manifolds