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Title: Application of deep artificial neural networks to multi-dimensional flamelet libraries and spray flames

Abstract

The "curse of dimensionality" has limited the applicability and expansion of tabulated combustion models. While the tabulated flamelet model and other multi-dimensional manifold approaches have shown predictive capability, the associated tabulation involves the storage of large lookup tables, requiring large memory as well as multi-dimensional interpolation subroutines, all implemented during runtime. This work investigates the use of deep artificial neural networks to replace lookup tables in order to reduce the memory footprint and increase the computational speed of tabulated flamelets and related approaches. Specifically, different strategic approaches to training the artificial neural network models are explored and a grouped multi-target artificial neural network is introduced, which takes advantage of the ability of artificial neural networks to map an input space to multiple targets by classifying the species based on their correlation to one another. The grouped multi-target artificial neural network approach is validated by applying it to an n-dodecane spray flame using conditions of the Spray A flame from the Engine Combustion Network and comparing global flame characteristics for different ambient conditions using a well-established large-eddy simulation framework. The same framework is then extended to the simulations of methyl decanoate combustion in a compression ignition engine. The validation studies showmore » that the grouped multi-target artificial neural networks are able to accurately capture flame liftoff, autoignition, two-stage heat release and other quantitative trends over a range of conditions. The use of neural networks in conjunction with the grouping mechanism as performed in the grouped multi-target artificial neural network produces a significant reduction in the memory footprint and computational costs for the code and, thus, widens the operating envelope for higher fidelity engine simulations with detailed mechanisms.« less

Authors:
; ; ; ;
Publication Date:
Research Org.:
Argonne National Lab. (ANL), Argonne, IL (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE) - Office of Vehicle Technologies (VTO)
OSTI Identifier:
1605138
Grant/Contract Number:  
[AC02-06CH11357]
Resource Type:
Accepted Manuscript
Journal Name:
International Journal of Engine Research
Additional Journal Information:
[ Journal Volume: 21; Journal Issue: 1]
Country of Publication:
United States
Language:
English
Subject:
artificial neural networks; chemistry tabulation; flamelets

Citation Formats

Owoyele, Opeoluwa, Kundu, Prithwish, Ameen, Muhsin M, Echekki, Tarek, and Som, Sibendu. Application of deep artificial neural networks to multi-dimensional flamelet libraries and spray flames. United States: N. p., 2020. Web. doi:10.1177/1468087419837770.
Owoyele, Opeoluwa, Kundu, Prithwish, Ameen, Muhsin M, Echekki, Tarek, & Som, Sibendu. Application of deep artificial neural networks to multi-dimensional flamelet libraries and spray flames. United States. doi:10.1177/1468087419837770.
Owoyele, Opeoluwa, Kundu, Prithwish, Ameen, Muhsin M, Echekki, Tarek, and Som, Sibendu. Wed . "Application of deep artificial neural networks to multi-dimensional flamelet libraries and spray flames". United States. doi:10.1177/1468087419837770.
@article{osti_1605138,
title = {Application of deep artificial neural networks to multi-dimensional flamelet libraries and spray flames},
author = {Owoyele, Opeoluwa and Kundu, Prithwish and Ameen, Muhsin M and Echekki, Tarek and Som, Sibendu},
abstractNote = {The "curse of dimensionality" has limited the applicability and expansion of tabulated combustion models. While the tabulated flamelet model and other multi-dimensional manifold approaches have shown predictive capability, the associated tabulation involves the storage of large lookup tables, requiring large memory as well as multi-dimensional interpolation subroutines, all implemented during runtime. This work investigates the use of deep artificial neural networks to replace lookup tables in order to reduce the memory footprint and increase the computational speed of tabulated flamelets and related approaches. Specifically, different strategic approaches to training the artificial neural network models are explored and a grouped multi-target artificial neural network is introduced, which takes advantage of the ability of artificial neural networks to map an input space to multiple targets by classifying the species based on their correlation to one another. The grouped multi-target artificial neural network approach is validated by applying it to an n-dodecane spray flame using conditions of the Spray A flame from the Engine Combustion Network and comparing global flame characteristics for different ambient conditions using a well-established large-eddy simulation framework. The same framework is then extended to the simulations of methyl decanoate combustion in a compression ignition engine. The validation studies show that the grouped multi-target artificial neural networks are able to accurately capture flame liftoff, autoignition, two-stage heat release and other quantitative trends over a range of conditions. The use of neural networks in conjunction with the grouping mechanism as performed in the grouped multi-target artificial neural network produces a significant reduction in the memory footprint and computational costs for the code and, thus, widens the operating envelope for higher fidelity engine simulations with detailed mechanisms.},
doi = {10.1177/1468087419837770},
journal = {International Journal of Engine Research},
number = [1],
volume = [21],
place = {United States},
year = {2020},
month = {1}
}

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