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Title: Sparsity Facilitates Chemical-Reaction Selection for Engine Simulations

Abstract

Analysis of large-scale, realistic models incorporating detailed chemistry can be challenging because each simulation is computationally expensive, and a complete analysis may require many simulations. This paper addresses one such problem of this type, chemical-reaction selection in engine simulations. In this computationally challenging case, it is demonstrated how the important concept of sparsity can facilitate chemical-reaction selection, which is the process of finding the most important chemical reactions for modeling a chemical process. It is difficult to perform accurate reaction selection for engine simulations using realistic models of the chemistry, as each simulation takes processor weeks to complete. We developed a procedure to efficiently accomplish this selection process with a relatively small number of simulations using a form of global sensitivity analysis based on sparse regression. The chemical-reaction selection leads to an analysis of the ignition chemistry as it evolves within the compression-ignition engine simulations and allows for the spatial development of the selected chemical reactions to be studied in detail.

Authors:
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Publication Date:
Research Org.:
Argonne National Lab. (ANL), Argonne, IL (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22); USDOE Office of Energy Efficiency and Renewable Energy (EERE), Vehicle Technologies Office (EE-3V)
OSTI Identifier:
1477455
DOE Contract Number:  
AC02-06CH11357
Resource Type:
Journal Article
Journal Name:
Journal of Physical Chemistry. A, Molecules, Spectroscopy, Kinetics, Environment, and General Theory
Additional Journal Information:
Journal Volume: 122; Journal Issue: 36; Journal ID: ISSN 1089-5639
Publisher:
American Chemical Society
Country of Publication:
United States
Language:
English

Citation Formats

Magnotti, Gina M., Wang, Zihan, Liu, Wei, Sivaramakrishnan, Raghu, Som, Sibendu, and Davis, Michael J. Sparsity Facilitates Chemical-Reaction Selection for Engine Simulations. United States: N. p., 2018. Web. doi:10.1021/acs.jpca.8b05436.
Magnotti, Gina M., Wang, Zihan, Liu, Wei, Sivaramakrishnan, Raghu, Som, Sibendu, & Davis, Michael J. Sparsity Facilitates Chemical-Reaction Selection for Engine Simulations. United States. doi:10.1021/acs.jpca.8b05436.
Magnotti, Gina M., Wang, Zihan, Liu, Wei, Sivaramakrishnan, Raghu, Som, Sibendu, and Davis, Michael J. Mon . "Sparsity Facilitates Chemical-Reaction Selection for Engine Simulations". United States. doi:10.1021/acs.jpca.8b05436.
@article{osti_1477455,
title = {Sparsity Facilitates Chemical-Reaction Selection for Engine Simulations},
author = {Magnotti, Gina M. and Wang, Zihan and Liu, Wei and Sivaramakrishnan, Raghu and Som, Sibendu and Davis, Michael J.},
abstractNote = {Analysis of large-scale, realistic models incorporating detailed chemistry can be challenging because each simulation is computationally expensive, and a complete analysis may require many simulations. This paper addresses one such problem of this type, chemical-reaction selection in engine simulations. In this computationally challenging case, it is demonstrated how the important concept of sparsity can facilitate chemical-reaction selection, which is the process of finding the most important chemical reactions for modeling a chemical process. It is difficult to perform accurate reaction selection for engine simulations using realistic models of the chemistry, as each simulation takes processor weeks to complete. We developed a procedure to efficiently accomplish this selection process with a relatively small number of simulations using a form of global sensitivity analysis based on sparse regression. The chemical-reaction selection leads to an analysis of the ignition chemistry as it evolves within the compression-ignition engine simulations and allows for the spatial development of the selected chemical reactions to be studied in detail.},
doi = {10.1021/acs.jpca.8b05436},
journal = {Journal of Physical Chemistry. A, Molecules, Spectroscopy, Kinetics, Environment, and General Theory},
issn = {1089-5639},
number = 36,
volume = 122,
place = {United States},
year = {2018},
month = {8}
}