Investigation of methyl decanoate combustion in an optical direct-injection diesel engine

Cheng, A. S.; Dumitrescu, Cosmin E.; Mueller, Charles J.

doi:10.1021/ef501934n

Title: Investigation of methyl decanoate combustion in an optical direct-injection diesel engine

Journal Article · Mon Nov 24 00:00:00 EST 2014 · Energy and Fuels

DOI:https://doi.org/10.1021/ef501934n· OSTI ID:1235249

Cheng, A. S. ^[1]; Dumitrescu, Cosmin E. ^[2]; Mueller, Charles J. ^[2]

San Francisco State Univ., CA (United States)
Sandia National Lab. (SNL-CA), Livermore, CA (United States)

In this study, an optically accessible heavy-duty diesel engine was used to investigate the impact of methyl decanoate (MD) on combustion and emissions. A specific goal of the study was to determine if MD could enable soot-free leaner-lifted flame combustion (LLFC) – a mode of mixing-controlled combustion associated with fuel-air equivalence ratios below approximately two. An ultra-low sulfur diesel certification fuel (CF) was used as the baseline fuel, and experiments were conducted at two fuel-injection pressures with three levels of charge-gas dilution. In addition to conventional pressure-based and engine-out emissions measurements, exhaust laser-induced incandescence, in-cylinder natural luminosity (NL), and in-cylinder chemiluminescence (CL) diagnostics were used to provide detailed insight into combustion processes.

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Research Organization:: Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

Sponsoring Organization:: USDOE National Nuclear Security Administration (NNSA)

Grant/Contract Number:: AC04-94AL85000

OSTI ID:: 1235249

Report Number(s):: SAND-2014-17363J; 537256

Journal Information:: Energy and Fuels, Vol. 28, Issue 12; ISSN 0887-0624

Publisher:: American Chemical Society (ACS)Copyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 13 works

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Selective production of decanoic acid from iterative reversal of β-oxidation pathway Kim, Seohyoung; Gonzalez, Ramon Biotechnology and Bioengineering, Vol. 115, Issue 5 https://doi.org/10.1002/bit.26540	journal	February 2018
Application of deep artificial neural networks to multi-dimensional flamelet libraries and spray flames Owoyele, Opeoluwa; Kundu, Prithwish; Ameen, Muhsin M. International Journal of Engine Research, Vol. 21, Issue 1 https://doi.org/10.1177/1468087419837770	journal	March 2019

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