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https://doi.org/10.2139/ssrn.5...
Article . 2025 . Peer-reviewed
Data sources: Crossref
https://dx.doi.org/10.48550/ar...
Article . 2025
License: CC BY
Data sources: Datacite
DBLP
Preprint . 2025
Data sources: DBLP
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Skeletal Reaction Models for Gasoline Surrogate Combustion

Authors: Yinmin Liu; Hessam Babaee; Peyman Givi; Daniel Livescu; Arash G. Nouri;

Skeletal Reaction Models for Gasoline Surrogate Combustion

Abstract

Skeletal reaction models are derived for a four-component gasoline surrogate model via an instantaneous local sensitivity analysis technique. The sensitivities of the species mass fractions and the temperature with respect to the reaction rates are estimated by a reduced-order modeling (ROM) methodology. Termed "implicit time-dependent basis CUR (implicit TDB-CUR)," this methodology is based on the CUR matrix decomposition and incorporates implicit time integration for evolving the bases. The estimated sensitivities are subsequently analyzed to develop skeletal reaction models with a fully automated procedure. The 1389-species gasoline surrogate model developed at Lawrence Livermore National Laboratory (LLNL) is selected as the detailed kinetics model. The skeletal reduction procedure is applied to this model in a zero-dimensional constant-pressure reactor over a wide range of initial conditions. The performances of the resulting skeletal models are appraised by comparison against the results via the LLNL detailed model, and also predictions via other skeletal models. Two new skeletal models are developed consisting of 679 and 494 species, respectively. The first is an alternative to an existing model with the same number of species. The predictions with this model reproduces the detailed models vital flame results with less than 1% errors. The errors via the second model are less than 10%.

Keywords

Computational Engineering, Finance, and Science (cs.CE), FOS: Computer and information sciences, Computational Engineering, Finance, and Science

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green