Investigation of Species from Negative Valve Overlap Reforming Using a Stochastic Reactor Model University of Minnesota

Format:

Conference/Event

Author/Creator:

Kane, Kane, author.

Contributor:

Ekoto, Isaac

Li, Xuesong

Northrop, William F.

Wolk, Benjamin

Conference Name:

WCX 17: SAE World Congress Experience (2017-04-04 : Detroit, Michigan, United States)

Language:

English

Physical Description:

1 online resource

Place of Publication:

Warrendale, PA SAE International 2017

Summary:

AbstractFuel reforming during a Negative Valve Overlap (NVO) period is an effective approach to control Low Temperature Gasoline Combustion (LTGC) ignition. Previous work has shown through experiments that primary reference fuels reform easily and produce several species that drastically affect ignition characteristics. However, our previous research has been unable to accurately predict measured reformate composition at the end of the NVO period using simple single-zone models. In this work, we use a stochastic reactor model (SRM) closed cycle engine simulation to predict reformate composition accounting for in-cylinder temperature and mixture stratification. The SRM model is less computationally intensive than CFD simulations while still allowing the use of large chemical mechanisms to predict intermediate species formation rates. By comparing model results with experimental speciation data from a single-cylinder engine, the presented work provides insight into the thermodynamic and kinetic processes that occur during in-cylinder fuel reformation. Three single-component fuels (iso-octane, n-heptane and ethanol) were modeled as a function of assumed thermal stratification. Across thermal stratification levels, the modeled reformate concentrations match well with measured values though they are very sensitive to initial conditions. The relationship between thermal stratification and resulting reformed species provides insight into the effect of non-homogeneity on products and illustrates the value of SRM over homogeneous reactor models to inexpensively predict in-cylinder processes

Notes:

Vendor supplied data

Publisher Number:

2017-01-0529

Access Restriction:

Restricted for use by site license