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Automated mechanism reduction is important in enabling the use of kinetics data in engineering design. In this work, we report on a mechanism-reduction technique that serves as a practical tool for automated mechanism reduction when applied to engine-simulation, with particular focus on compression-ignition engines. For this application, a multi-zone engine model has been developed, which can capture the stratification in the engine due to crevice and boundary-layer cooling effects. The multi-zone model serves as the workhorse for the mechanism-reduction algorithm. The reduction process is designed to operate on model-solution data from a parametric matrix of runs, in which the multi-zone model is run under different conditions. A more accurate reduction can therefore be achieved while accounting for spatial variations in the engine, temporal variations over the compression cycle, and variations in operating conditions.

Designing advanced, clean and fuel-efficient engines requires detailed understanding of fuel chemistry. While knowledge of fuel combustion chemistry has grown rapidly in recent years, the representation of conventional fossil fuels in full detail is still intractable. A popular approach is to use a model-fuel or surrogate blend that can mimic various characteristics of a conventional fuel. Despite the use of surrogate blends, there remains a gap between detailed chemistry and its utilization in computational fluid dynamics (CFD), due to the prohibitive computational cost of using thousands of chemical species in large numbers of computational cells. This work presents a set of software tools that help to enable the use of detailed chemistry in representing conventional fuels in CFD simulation. The software tools include the Surrogate Blend Optimizer and a suite of automated mechanism reduction strategies.

Detailed knowledge of hydrocarbon fuel combustion chemistry has grown tremendously in recent years. However, the gap between detailed chemistry and computational fluid dynamics (CFD) remains, because of the high cost of solving detailed chemistry in a large number of computational cells. This paper presents the results of applying a suite of techniques aimed at closing this gap. The techniques include use of a surrogate blend optimizer and a guided mechanism reduction methodology, as well as advanced methods for efficiently and accurately coupling the pre-reduced kinetic models with the multidimensional transport equations. The advanced methods include dynamic adaptive chemistry (DAC) and dynamic cell clustering (DCC) algorithms.

At temperatures below 1100 K, the oxidation of nitric oxide (NO) impacts the oxidation of hydrocarbons, causing a sensitization effect in fuel combustion. This effect can be important in engine operations, especially those involving high levels of exhaust-gas recirculation (EGR). Many researchers have observed this NO sensitization for the oxidation of hydrocarbons in HCCI engines as well as stirred reactors. They used several model-fuel components relevant to gasoline, such as n-heptane, iso-octane, and toluene. As found in stirred reactor experiments, NO tends to increase the extent of oxidation for high-octane fuel components, such as isooctane and toluene. However, for the low-octane component n-heptane, NO has an inhibiting effect on hydrocarbon oxidation, particularly at low temperatures corresponding to the negative temperature coefficient (NTC) region.