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A set of elementary surface reactions is proposed for modeling the chemistry in a lean NOx trap during regeneration (reduction of stored NOx). The proposed reaction mechanism can account for the observed product distribution from the trap over a range of temperatures and inlet gas compositions similar to those expected for realistic operation. The mechanism includes many reactions already discussed in the literature, together with some hypothesized reactions that are required to match observations from temperature programmed reactor experiments with a commercial lean NOx trap catalyst. Preliminary results indicate that the NOx trap regeneration and byproduct formation rates can be effectively captured by using a relatively compact set of elementary reactions.

This paper describes the reduction of cyclic combustion variations in spark-ignited engines, especially under idle conditions in which the air-fuel mixture is lean of stoichiometry. Under such conditions, the combination of residual cylinder gas and parametric variations (such as variations in fuel preparation) gives rise to significant combustion instabilities that may lead to customer-perceived engine roughness and transient emissions spikes. Such combustion instabilities may preclude operation at air-fuel ratios that would otherwise be advantageous for fuel economy and emissions. This approach exploits the recognition that a component of the observed combustion instability results from a noise-driven, nonlinear deterministic mechanism that can be actively stabilized by small feedback control actions which result in little if any additional use of fuel.

We present techniques of symbolic time-series analysis which are useful for analyzing temporal patterns in dynamic measurements of engine combustion variables. We focus primarily on techniques that characterize predictability and the occurrence of repeating temporal patterns. These methods can be applied to standard, cycle-resolved engine combustion measurements, such as IMEP and heat release. The techniques are especially useful in cases with high levels of measurement and/or dynamic noise. We illustrate their application to experimental data from a production V8 engine and a laboratory single-cylinder engine.

We describe a method for detecting and quantifying time irreversibility in experimental engine data. We apply this method to experimental heat-release measurements from four spark-ignited engines under leaning fueling conditions. We demonstrate that the observed behavior is inconsistent with a linear Gaussian random process and is more appropriately described as a noisy nonlinear dynamical process.