Browse Publications Technical Papers 2009-01-1511
2009-04-20

CO Emission Model for an Integrated Diesel Engine, Emissions, and Exhaust Aftertreatment System Level Model 2009-01-1511

A kinetic carbon monoxide (CO) emission model is developed to simulate engine out CO emissions for conventional diesel combustion. The model also incorporates physics governing CO emissions for low temperature combustion (LTC). The emission model will be used in an integrated system level model to simulate the operation and interaction of conventional and low temperature diesel combustion with aftertreatment devices. The Integrated System Model consists of component models for the diesel engine, engine-out emissions (such as NOx and Particulate Matter), and aftertreatment devices (such as DOC and DPF). The addition of CO emissions model will enhance the capability of the Integrated System Model to predict major emission species, especially for low temperature combustion.
In this work a CO emission model is developed based on a two-step global kinetic mechanism [8]. In addition, effect of physical parameters such as start of injection (SOI) and ignition delay are studied and modeled to develop a phenomenological CO emission model. This includes development of a simple spray model to compute amount of fuel missing the bowl in case of PCCI and HCCI type combustion. Results from the spray model are used to simulate the motion of fuel in the cylinder bowl during various stages of combustion. Ignition delay effects are analyzed and modeled to change the activation temperature of CO oxidation reaction to simulate the affect of increasing peak temperatures with advancing SOI. The results from the CO emission model are compared with experimental data from a multi-cylinder conventional diesel engine as well as a single cylinder version of a low temperature combustion diesel engine. For the LTC engine, the start of injection is advanced using two different injection strategies (single and two-step injection). Overall the results show good agreement of the trends between the experiments and model predictions.

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