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Title: Linear eddy mixing based tabulation and artificial neural networks for large eddy simulations of turbulent flames

Abstract

A large eddy simulation (LES) sub-grid model is developed based on the artificial neural network (ANN) approach to calculate the species instantaneous reaction rates for multi-step, multi-species chemical kinetics mechanisms. The proposed methodology depends on training the ANNs off-line on a thermo-chemical database representative of the actual composition and turbulence (but not the actual geometrical problem) of interest, and later using them to replace the stiff ODE solver (direct integration (DI)) to calculate the reaction rates in the sub-grid. The thermo-chemical database is tabulated with respect to the thermodynamic state vector without any reduction in the number of state variables. The thermo-chemistry is evolved by stand-alone linear eddy mixing (LEM) model simulations under both premixed and non-premixed conditions, where the unsteady interaction of turbulence with chemical kinetics is included as a part of the training database. The proposed methodology is tested in LES and in stand-alone LEM studies of three distinct test cases with different reduced mechanisms and conditions. LES of premixed flame-turbulence-vortex interaction provides direct comparison of the proposed ANN method against DI and ANNs trained on thermo-chemical database created using another type of tabulation method. It is shown that the ANN trained on the LEM database can capturemore » the correct flame physics with accuracy comparable to DI, which cannot be achieved by ANN trained on a laminar premix flame database. A priori evaluation of the ANN generality within and outside its training domain is carried out using stand-alone LEM simulations as well. Results in general are satisfactory, and it is shown that the ANN provides considerable amount of memory saving and speed-up with reasonable and reliable accuracy. The speed-up is strongly affected by the stiffness of the reduced mechanism used for the computations, whereas the memory saving is considerable regardless. (author)« less

Authors:
;  [1]
  1. School of Aerospace Engineering, Georgia Institute of Technology, 270 Ferst Drive, Atlanta, GA 30332-0150 (United States)
Publication Date:
OSTI Identifier:
21248851
Resource Type:
Journal Article
Journal Name:
Combustion and Flame
Additional Journal Information:
Journal Volume: 157; Journal Issue: 1; Other Information: Elsevier Ltd. All rights reserved; Journal ID: ISSN 0010-2180
Country of Publication:
United States
Language:
English
Subject:
37 INORGANIC, ORGANIC, PHYSICAL AND ANALYTICAL CHEMISTRY; CHEMICAL REACTION KINETICS; COMPARATIVE EVALUATIONS; FLAMES; NEURAL NETWORKS; MIXING; TURBULENCE; ACCURACY; VECTORS; VELOCITY; CALCULATION METHODS; VORTICES; LARGE-EDDY SIMULATION; Linear eddy mixing

Citation Formats

Sen, Baris Ali, and Menon, Suresh. Linear eddy mixing based tabulation and artificial neural networks for large eddy simulations of turbulent flames. United States: N. p., 2010. Web. doi:10.1016/J.COMBUSTFLAME.2009.06.005.
Sen, Baris Ali, & Menon, Suresh. Linear eddy mixing based tabulation and artificial neural networks for large eddy simulations of turbulent flames. United States. doi:10.1016/J.COMBUSTFLAME.2009.06.005.
Sen, Baris Ali, and Menon, Suresh. Fri . "Linear eddy mixing based tabulation and artificial neural networks for large eddy simulations of turbulent flames". United States. doi:10.1016/J.COMBUSTFLAME.2009.06.005.
@article{osti_21248851,
title = {Linear eddy mixing based tabulation and artificial neural networks for large eddy simulations of turbulent flames},
author = {Sen, Baris Ali and Menon, Suresh},
abstractNote = {A large eddy simulation (LES) sub-grid model is developed based on the artificial neural network (ANN) approach to calculate the species instantaneous reaction rates for multi-step, multi-species chemical kinetics mechanisms. The proposed methodology depends on training the ANNs off-line on a thermo-chemical database representative of the actual composition and turbulence (but not the actual geometrical problem) of interest, and later using them to replace the stiff ODE solver (direct integration (DI)) to calculate the reaction rates in the sub-grid. The thermo-chemical database is tabulated with respect to the thermodynamic state vector without any reduction in the number of state variables. The thermo-chemistry is evolved by stand-alone linear eddy mixing (LEM) model simulations under both premixed and non-premixed conditions, where the unsteady interaction of turbulence with chemical kinetics is included as a part of the training database. The proposed methodology is tested in LES and in stand-alone LEM studies of three distinct test cases with different reduced mechanisms and conditions. LES of premixed flame-turbulence-vortex interaction provides direct comparison of the proposed ANN method against DI and ANNs trained on thermo-chemical database created using another type of tabulation method. It is shown that the ANN trained on the LEM database can capture the correct flame physics with accuracy comparable to DI, which cannot be achieved by ANN trained on a laminar premix flame database. A priori evaluation of the ANN generality within and outside its training domain is carried out using stand-alone LEM simulations as well. Results in general are satisfactory, and it is shown that the ANN provides considerable amount of memory saving and speed-up with reasonable and reliable accuracy. The speed-up is strongly affected by the stiffness of the reduced mechanism used for the computations, whereas the memory saving is considerable regardless. (author)},
doi = {10.1016/J.COMBUSTFLAME.2009.06.005},
journal = {Combustion and Flame},
issn = {0010-2180},
number = 1,
volume = 157,
place = {United States},
year = {2010},
month = {1}
}