| Description |
1 online resource (1 page) : color illustrations. |
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text txt rdacontent |
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computer c rdamedia |
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online resource cr rdacarrier |
| Series |
NREL/PO ; 2C00-78586 |
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NREL/PO ; 2C00-78586.
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| Note |
"38th International Symposium on Combustion, Adelaide, Australia and virtual, 24-29 January 2021." |
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"Funding provided by the U.S. Department of Energy Office of Energy Efficiency and Renewable Energy Vehicle Technologies Office." |
| Funding |
DE-AC36-08GO28308 |
| Note |
Description based on online resource; title from PDF title page (NREL, viewed May 3, 2022). |
| Summary |
Reduced-order manifold combustion models are commonly used to lower the cost of reacting Large Eddy Simulations (LES) and may be derived either from physical principles as in flamelet models or from data-driven methods like Principal Component Analysis (PCA). In either case, neural networks are increasingly used as part of these models to provide a nonlinear mapping between a small set of pre-defined variables that parameterize the manifold and outputs of interest, such as reaction rates. In this work, we propose a new manifold-based modeling approach that combines the definition of the manifold-parameterizing variables (linear combinations of species), the nonlinear mapping to the outputs, and closure of filtered quantities for LES into the structure of a single neural network. This allows the process used to train the neural network to simultaneously optimize both the functional form of the model and the identities of the inputs to the model. The new approach can flexibly incorporate thermochemical data from any combustion system; if trained on data from 1D flames it can be interpreted as an optimized flamelet model, but it can also be used to learn models from data from more complex configurations. This work presents a priori evaluations of the new approach in both contexts. Evaluation using data from 1D premixed flames demonstrates the physical interpretability of the manifold variables generated by the new approach. Evaluation using data from direct numerical simulations of turbulent flames shows improved predictions relative to either flamelet or PCA-based models in a more complex configuration. |
| Subject |
Machine learning.
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Manifolds (Mathematics)
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Combustion -- Mathematical models.
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Apprentissage automatique.
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Variétés (Mathématiques)
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Combustion -- Modèles mathématiques.
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Combustion -- Mathematical models
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Machine learning
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Manifolds (Mathematics)
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| Indexed Term |
large eddy simulation |
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machine learning |
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turbulent combustion |
| Added Author |
de Frahan, Marc T. Henry, author.
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Yellapantula, Shashank, author.
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National Renewable Energy Laboratory (U.S.), issuing body.
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| Standard No. |
1819068 OSTI ID |
| Gpo Item No. |
0430-P-17 (online) |
| Sudoc No. |
E 9.28:NREL/PO-2 C 00-78586 |
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