Deep learning-based model for progress variable dissipation rate in turbulent premixed flames

Yellapantula, Shashank; Perry, Bruce A.; Grout, Ray W.

doi:10.1016/j.proci.2020.06.205

Title: Deep learning-based model for progress variable dissipation rate in turbulent premixed flames

Journal Article · Fri Sep 11 00:00:00 EDT 2020 · Proceedings of the Combustion Institute

DOI:https://doi.org/10.1016/j.proci.2020.06.205· OSTI ID:1665878

^[1]; Perry, Bruce A. ^[1]; Grout, Ray W. ^[1]

National Renewable Energy Lab. (NREL), Golden, CO (United States)

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.

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Research Organization:: National Renewable Energy Lab. (NREL), Golden, CO (United States)

Sponsoring Organization:: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Vehicle Technologies Office (EE-3V)

Grant/Contract Number:: AC36-08GO28308

OSTI ID:: 1665878

Report Number(s):: NREL/JA-2C00-75428; MainId:6223; UUID:4d7fbbc7-8a05-ea11-9c2a-ac162d87dfe5; MainAdminID:18561

Journal Information:: Proceedings of the Combustion Institute, Vol. 38, Issue 2; ISSN 1540-7489

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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