Deep neural network based unsteady flamelet progress variable approach in a supersonic combustor
Author:
Affiliation:
1. Argonne National Laboratory
2. NASA Langley Research Center
Publisher
American Institute of Aeronautics and Astronautics
Link
https://arc.aiaa.org/doi/pdf/10.2514/6.2022-2073
Reference15 articles.
1. Advances and challenges in modeling high-speed turbulent combustion in propulsion systems
2. Flamelet Modeling for Supersonic Combustion
3. A Priori Analysis of a Compressible Flamelet Model using RANS Data for a Dual-Mode Scramjet Combustor
4. Supersonic combustion of hydrogen in a vitiated air stream using stepped-wall injection
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1. Brief Review of Recent Achievements in the Flamelet Manifold Selection and Probability Density Distribution for Flamelet Manifold Variables;Journal of Energy Resources Technology;2024-02-12
2. Consistent Coupling of Compressibility Effects in Manifold-Based Models for Supersonic Combustion;AIAA Journal;2024-02
3. Flamelet Progress Variable Dynamic Source Term and Energy (FPV-DynaSTE) Approach for Compressible Reacting Flows with Heat Loss;AIAA SCITECH 2024 Forum;2024-01-04
4. Three-Dimensional Compressible Chemically Reacting Computational Fluid Dynamics with Tensor Trains;AIAA SCITECH 2024 Forum;2024-01-04
5. Study on the application of artificial neural network-based flamelet/progress variable model in supersonic combustion;AIP Advances;2023-11-01
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