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

Owoyele, Opeoluwa; Kundu, Prithwish; Ameen, Muhsin M.; Echekki, Tarek; Som, Sibendu

doi:10.1177/1468087419837770

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

Journal Article · Fri Mar 29 00:00:00 EDT 2019 · International Journal of Engine Research

DOI:https://doi.org/10.1177/1468087419837770· OSTI ID:1605138

^[1]; Kundu, Prithwish ^[2]; Ameen, Muhsin M. ^[2]; Echekki, Tarek ^[3]; Som, Sibendu ^[2]

North Carolina State Univ., Raleigh, NC (United States); Argonne National Lab. (ANL), Lemont, IL (United States)
Argonne National Lab. (ANL), Lemont, IL (United States)
North Carolina State Univ., Raleigh, NC (United States)

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. As such, 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.

View Accepted Manuscript (DOE)

Research Organization:: Argonne National Laboratory (ANL), Argonne, IL (United States)

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

Grant/Contract Number:: AC02-06CH11357

OSTI ID:: 1605138

Journal Information:: International Journal of Engine Research, Journal Name: International Journal of Engine Research Journal Issue: 1 Vol. 21; ISSN 1468-0874

Publisher:: SAGECopyright Statement

Country of Publication:: United States

Language:: English

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