Machine-Learned Manifold-Based Models for Large Eddy Simulation of Turbulent Combustion

Conference · Wed Sep 01 00:00:00 EDT 2021

OSTI ID:1819068

Perry, Bruce A.; Henry de Frahan, Marc T.; Yellapantula, Shashank

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.

• Research Organization: National Renewable Energy Laboratory (NREL), Golden, CO (United States)
• Sponsoring Organization: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Vehicle Technologies Office (EE-3V)
• DOE Contract Number: AC36-08GO28308
• OSTI ID: 1819068
• Report Number(s): NREL/PO-2C00-78586; MainId:32503; UUID:2df9f6cc-5736-4c9f-9482-2ffb50ec7360; MainAdminID:61667
• Conference Information: Presented at the 38th International Symposium on Combustion, 24-29 January 2021
• Country of Publication: United States
• Language: English

Related Subjects

47 OTHER INSTRUMENTATION
large eddy simulation
machine learning
turbulent combustion