Deep learning-based model for progress variable dissipation rate in turbulent premixed flames
Abstract
A deep neural network (DNN) based large eddy simulation (LES) model for progress variable dissipation rate in turbulent premixed flames is presented. The DNN model is trained using filtered data from direct numerical simulations (DNS) of statistically planar turbulent premixed flames with n-heptane as fuel. Training data was comprised of flames with varying turbulence levels leading to a range of Karlovitz numbers. Through a-priori tests the DNN model is shown to predict the subfilter contribution to progress variable dissipation rate accurately over a range of filter widths and for all Karlovitz numbers examined in this study. Superior performance of the DNN model relative to an established physics-based model is also demonstrated. Additionally, transferability of the DNN model is highlighted by a-priori evaluation of the model using filtered DNS data from multiple cases with different Karlovitz numbers and fuel species than those that were used for training the model.
- Authors:
-
- National Renewable Energy Lab. (NREL), Golden, CO (United States)
- Publication Date:
- Research Org.:
- National Renewable Energy Lab. (NREL), Golden, CO (United States)
- Sponsoring Org.:
- USDOE Office of Energy Efficiency and Renewable Energy (EERE), Vehicle Technologies Office (EE-3V)
- OSTI Identifier:
- 1665878
- Report Number(s):
- NREL/JA-2C00-75428
Journal ID: ISSN 1540-7489; MainId:6223;UUID:4d7fbbc7-8a05-ea11-9c2a-ac162d87dfe5;MainAdminID:18561
- Grant/Contract Number:
- AC36-08GO28308
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Proceedings of the Combustion Institute
- Additional Journal Information:
- Journal Volume: 38; Journal Issue: 2; Journal ID: ISSN 1540-7489
- Publisher:
- Elsevier
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 33 ADVANCED PROPULSION SYSTEMS; deep neural network (DNN); large eddy simulation (LES); premixed flames; progress variable dissipation rate
Citation Formats
Yellapantula, Shashank, Perry, Bruce A., and Grout, Ray W. Deep learning-based model for progress variable dissipation rate in turbulent premixed flames. United States: N. p., 2020.
Web. doi:10.1016/j.proci.2020.06.205.
Yellapantula, Shashank, Perry, Bruce A., & Grout, Ray W. Deep learning-based model for progress variable dissipation rate in turbulent premixed flames. United States. https://doi.org/10.1016/j.proci.2020.06.205
Yellapantula, Shashank, Perry, Bruce A., and Grout, Ray W. Fri .
"Deep learning-based model for progress variable dissipation rate in turbulent premixed flames". United States. https://doi.org/10.1016/j.proci.2020.06.205. https://www.osti.gov/servlets/purl/1665878.
@article{osti_1665878,
title = {Deep learning-based model for progress variable dissipation rate in turbulent premixed flames},
author = {Yellapantula, Shashank and Perry, Bruce A. and Grout, Ray W.},
abstractNote = {A deep neural network (DNN) based large eddy simulation (LES) model for progress variable dissipation rate in turbulent premixed flames is presented. The DNN model is trained using filtered data from direct numerical simulations (DNS) of statistically planar turbulent premixed flames with n-heptane as fuel. Training data was comprised of flames with varying turbulence levels leading to a range of Karlovitz numbers. Through a-priori tests the DNN model is shown to predict the subfilter contribution to progress variable dissipation rate accurately over a range of filter widths and for all Karlovitz numbers examined in this study. Superior performance of the DNN model relative to an established physics-based model is also demonstrated. Additionally, transferability of the DNN model is highlighted by a-priori evaluation of the model using filtered DNS data from multiple cases with different Karlovitz numbers and fuel species than those that were used for training the model.},
doi = {10.1016/j.proci.2020.06.205},
journal = {Proceedings of the Combustion Institute},
number = 2,
volume = 38,
place = {United States},
year = {Fri Sep 11 00:00:00 EDT 2020},
month = {Fri Sep 11 00:00:00 EDT 2020}
}
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