Regulations concerning emissions from diesel- and
gasoline-fuelled engines are becoming ever more stringent in all
parts of the world. Historically these targets have been achieved
through on-going technological development using an iterative
process of computational modeling, design, build and test.
Computational modeling is certainly the cheapest aspect within this
process and if employed to meet more of the challenges associated
with development, has the potential to significantly reduce
developmental cost and time scales. Furthermore, computational
models are an effective means to retain and apply often highly
focused technical knowledge of complex processes within development
teams thus delivering greater insight into processes.
As such there is a great deal of interest in advanced simulation
technologies; one such technology is srm suite™ which has proven
effective in simulating in-cylinder combustion processes to enable
engineers to identify optimal injection, valve train and spark
timing operating strategy to achieve a particular load-speed point
with reduced target emissions. The model accounts for the impact of
fuel injection strategies, detailed chemical kinetics, turbulent
mixing, and heat losses on the inhomogeneities associated with the
in-cylinder composition and temperature, within practical computing
time scales.
In order to account for the valve train dynamics and engine
breathing within the context of engine cycle simulation, the srm
suite has been coupled with standard 1D engine cycle simulators and
applied to investigate three industry relevant problems (1)
investigating cycle-to-cycle variations on emissions in an SI
engine, (2) investigating emissions at different injection timings,
speeds and loads in a diesel engine operated with pilot injection
and high levels of Exhaust Gas Recirculation (EGR), and (3)
simulating a dual injection Homogeneous Charge Compression Ignition
(HCCI) engine operated with an injection (fuel reformation) during
Negative Valve Overlap (NVO). In each context, computational
results are compared with experimental observations and conclusions
presented.