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We describe a CHEMKIN-based multi-zone model that simulates the expected combustion variations in a single-cylinder engine fueled with iso-octane as the engine transitions from spark-ignited (SI) combustion to homogenous charge compression ignition (HCCI) combustion. The model includes a 63-species reaction mechanism and mass and energy balances for the cylinder and the exhaust flow. For this study we assumed that the SI-to-HCCI transition is implemented by means of increasing the internal exhaust gas recirculation (EGR) at constant engine speed. This transition scenario is consistent with that implemented in previously reported experimental measurements on an experimental engine equipped with variable valve actuation. We find that the model captures many of the important experimental trends, including stable SI combustion at low EGR (~0.10), a transition to highly unstable combustion at intermediate EGR, and finally stable HCCI combustion at very high EGR (~0.75).

Downsized turbocharged engines have been increasingly popular in modern light-duty vehicles due to their fuel efficiency benefits. However, high power density in such engines is achieved thanks to high in-cylinder pressure and temperature conditions that increase knock propensity. Next-cycle control has been studied as a method to reduce the damaging effects of knock by operating the engine in a low knock probability condition. This exploratory study looks at the feasibility of in-cycle knock prediction as a tool for advanced knock control algorithms. A methodology is proposed to 1) choose in-cycle features of the pressure trace that highly correlate with knock events and 2) train artificial neural networks to predict in-cycle knock events before knock onset. The methodology was validated at different operating conditions and different levels of generalization. Precision and recall were used as metrics to evaluate the binary classifier.

1Increasing adoption of downsized, boosted, spark-ignition engines has improved vehicle fuel economy, and continued improvement is desirable to reduce carbon emissions in the near-term. However, this strategy is limited by damaging preignition events which can cause hardware failure. Research to date has shed light on various contributing factors related to fuel and lubricant properties as well as calibration strategies, but the causal factors behind an individual preignition cycle remain elusive. If actionable precursors could be identified, mitigation through active control strategies would be possible. This paper uses artificial neural networks to search for identifiable precursors in the cylinder pressure data from a large real-world data set containing many preignition cycles. It is found that while follow-up preignition cycles in clusters can be readily predicted, the initial preignition cycle is not predictable based on features of the cylinder pressure.

Operation of spark-ignition (SI) engines with high levels of charge dilution through exhaust gas recirculation (EGR) achieves significant engine efficiency gains while maintaining stoichiometric operation for compatibility with three-way catalysts. Dilution levels, however, are limited by cyclic variability - including significant numbers of misfires - that becomes more pronounced with increasing dilution. This variability has been shown to have both stochastic and deterministic components. Stochastic effects include turbulence, mixing variations, and the like, while the deterministic effect is primarily due to the nonlinear dependence of flame propagation rates and ignition characteristics on the charge composition, which is influenced by the composition of residual gases from prior cycles.

The widespread adoption of boosted, downsized SI engines has brought pre-ignition phenomena into greater focus, as the knock events resulting from pre-ignitions can cause significant hardware damage. Much attention has been given to understanding the causes of pre-ignition and identify lubricant or fuel properties and engine design and calibration considerations that impact its frequency. This helps to shift the pre-ignition limit to higher specific loads and allow further downsizing but does not fundamentally eliminate the problem. Real-time detection and mitigation of pre-ignition would thus be desirable to allow safe engine operation in pre-ignition-prone conditions. This study focuses on advancing the time of detection of pre-ignition in an engine cycle where it occurs.

In order to achieve proposed fuel economy requirements, engines must make better use of the available fuel energy. Regardless of how efficient the engine is, there will still be a significant fraction of the fuel energy that is rejected in the exhaust and coolant streams. One viable technology for recovering this waste heat is an Organic Rankine Cycle. This cycle heats a working fluid using these heat streams and expands the fluid through a turbine to produce shaft power. The present work was the development of such a system applied to a light duty diesel engine. This lab demonstration was designed to maximize the peak brake thermal efficiency of the engine, and the combined system achieved an efficiency of 45%. The design of the system is discussed, as are the experimental performance results. The system potential at typical operating conditions was evaluated to determine the practicality of installing such a system in a vehicle.

In order to further characterize and optimize the performance of Selective Catalytic Reduction (SCR) aftertreatment systems used on heavy-duty diesel engines, an accurately calibrated high-fidelity multi-step global kinetic SCR model and a reduced order estimator for on-board diagnostic (OBD) and control are desirable. In this study, a Cu-zeolite SCR catalyst from a 2010 Cummins ISB engine was experimentally studied in a flow reactor using carefully designed protocols. A 2-site SCR model describing mass transfer and the SCR chemical reaction mechanisms is described in the paper. The model was calibrated to the reactor test data sets collected under temperatures from 200 to 425 °C and SCR space velocities of 60000, 90000, and 120000 hr-1. The model parameters were calibrated using an optimization code to minimize the error between measured and simulated NO, NO₂, N₂O, and NH₃ gas concentration time histories.