Chapter 14: Machine Learning of Combustion LES Models from Reacting Direct Numerical Simulation
In this chapter we demonstrate how supervised deep learning techniques can be used to construct models for the filtered progress variable source term necessary for large eddy simulation (LES). The source data for the model is a direct numerical simulation (DNS) of a reacting flow in a low swirl burner configuration. Filtered quantities taken from the DNS data are used to train a deep neural network (DNN)-based model. An efficient data sampling strategy was devised to ensure that a uniform representation of all the states observed in the filtered DNS data are equally present in the training dataset. A-priori testing of the DNN-based model highlights the representative power of DNN to accurately reproduce the filtered reaction progress variable source term over a range of scales and various flame regimes as seen in an industrial burner.
- Research Organization:
- National Renewable Energy Laboratory (NREL), Golden, CO (United States)
- Sponsoring Organization:
- USDOE National Nuclear Security Administration (NNSA), Office of Defense Nuclear Security (NA-70); USDOE Office of Science (SC)
- DOE Contract Number:
- AC36-08GO28308; AC36-08GO28308
- OSTI ID:
- 1660243
- Report Number(s):
- NREL/CH-2C00-74457; MainId:7058; UUID:ec3f0b9d-bbaf-e911-9c24-ac162d87dfe5; MainAdminID:17340
- Country of Publication:
- United States
- Language:
- English
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