Computational Optimization of Reactivity Controlled Compression Ignition in a Heavy-Duty Engine with Ultra Low Compression Ratio

Paper #:
  • 2011-24-0015

Published:
  • 2011-09-11
Citation:
Dempsey, A. and Reitz, R., "Computational Optimization of Reactivity Controlled Compression Ignition in a Heavy-Duty Engine with Ultra Low Compression Ratio," SAE Int. J. Engines 4(2):2222-2239, 2011, https://doi.org/10.4271/2011-24-0015.
Pages:
18
Abstract:
Many studies have demonstrated ability of low temperature combustion to yield low NOx and soot while maintaining diesel-like thermal efficiencies. Methods of achieving low temperature combustion are numerous and range from using high cetane number fuels, like diesel, with large amounts of exhaust gas recirculation, to completely premixing a high octane number fuel, like gasoline, and approaching an HCCI-like condition. Both of the aforementioned techniques have relatively short combustion duration that results in very a rapid rate of heat release, and hence very rapid rates of pressure rise. This has been one of the major challenges for premixed, low temperature combustion at mid and high load. Reactivity Controlled Compression Ignition (RCCI) has been introduced recently, which is a dual fuel partially premixed combustion concept. In this strategy in-cylinder fuel blending is used to develop fuel reactivity gradients in the combustion chamber that result in a broad combustion event and reduced pressure rise rates. RCCI has been demonstrated to yield low NOx and soot with high thermal efficiency in a heavy-duty engine using a compression ratio of 16.1 at loads up to 15 bar gross IMEP. However, extension to full-load operation has proven to be difficult with a high compression ratio. The objective of this work was to optimize the engine with a low compression ratio of 11.7 using computational tools. The KIVA3V-CHEMKIN code, a multi-dimensional engine CFD model was coupled to a Nondominated Sorting Genetic Algorithm (NSGA II), which is a multi-objective genetic algorithm. Three engine operating conditions were investigated in this study, a low-load, mid-load, and a high-load point, 4, 9, and 23 bar gross IMEP, respectively. The goal of the optimization study was to simultaneously reduce six objectives, which are soot, NOx, unburned hydrocarbons, carbon monoxide, indicated specific fuel consumption, and the maximum pressure rise rate. The genetic algorithm was allowed to vary six engine design parameters, namely percent premixed gasoline, EGR fraction, and diesel direct injection parameters.
Access
Now
SAE MOBILUS Subscriber? You may already have access.
Buy
Select
Price
List
Download
$28.00
Mail
$28.00
Members save up to 42% off list price.
Share
HTML for Linking to Page
Page URL

Related Items

Training / Education
2018-03-26
Training / Education
2009-12-15
Event
2018-04-10
Training / Education
2018-06-05
Article
2017-07-26
Training / Education
2011-04-12