A Computational Investigation of Two-Stage Combustion in a Light-Duty Engine

The objective of this investigation is to optimize light-duty diesel engine operating parameters using Adaptive Injection Strategies (AIS) for optimal fuel preparation. A multi-dimensional Computational Fluid Dynamics (CFD) code with detailed chemistry, the KIVA-CHEMKIN code, is employed and a Multi-Objective Genetic Algorithm (MOGA) is used to study a Two-Stage Combustion (TSC) concept. The combustion process is considered at a light load operating condition (nominal IMEP of 5.5 bar and high speed (2000 rev/min)), and two combustion modes are combined in this concept. The first stage is ideally Homogeneous Charge Compression Ignition (HCCI) combustion and the second stage is diffusion combustion under high temperature and low oxygen concentration conditions. Available experimental data on a 1.9L single-cylinder research engine is used for model validation. The results show that the computations are able to adequately predict the emissions trends and quantities over an injection timing sweep for the Partially Premixed Compression Ignition (PCCI) cases investigated. A preliminary investigation was performed to gain an understanding of two-stage combustion in the light duty engine. At this condition it was found that pure HCCI combustion could yield very low engine out emissions, but extreme pressure rise rates would lead to excessive combustion noise. A multi-dimensional optimization code, NSGAII, was used for optimization of six objectives (NOx, soot, CO, HC, ISFC, and peak PRR) by adjusting four parameters (boost pressure, EGR rate, fraction of premixed fuel, and start of late injection timing). The optimization has shown that two-stage combustion is a feasible concept for noise reduction while maintaining reasonable emissions and fuel consumption. A Pareto solution yielding a peak pressure rise rate of 4.3 bar/deg was found using a high EGR rate (54%), relatively low IVC pressure (1.74 bar), premixing 36% of the total fuel, and injecting the remainder of the fuel at 2.9 degrees after TDC. Introduction Since its introduction in the late 1800’s, the diesel engine has been utilized in almost every aspect of modern life, from transportation to energy generation to food production. Several emissions are of prime concern for air pollution: nitrogen oxides (NOx), carbon monoxide (CO), unburned hydrocarbons (HC), and particulates (soot). These pollutants are damaging to the environment and human health. Reduction of these harmful pollutants while maintaining fuel economy has been a primary driving factor for internal combustion engine research in recent years. Homogeneous Charge Compression Ignition (HCCI) and Premixed Charge Compression Ignition (PCCI) concepts have been shown as promising techniques for simultaneous NOx and soot reduction [1-3]. However, a major concern for light duty engines is noise generated during the combustion process. HCCI combustion tends to produce high rates of pressure rise and therefore can result in higher combustion noise than conventional diesel operation. Sun [4, 5] has shown the possibility of emissions reduction using a Two-Stage combustion concept in a heavy-duty diesel engine. The first stage is HCCI combustion and the second stage is diffusion combustion under high temperature and low oxygen concentration conditions. Because only a fraction of the total fuel is burnt in pure HCCI combustion it may be possible to use the TSC concept for noise reduction. This study aims to apply the TSC concept to a light-duty diesel engine in order to minimize pollutant emissions and to improve fuel economy while maintaining low engine noise. A multi-dimensional CFD code with detailed chemistry, the KIVA-CHEMKIN code, was employed in this investigation. Model validation was performed using experimental data of Opat et al. [3]. After model validation, a preliminary investigation was performed to gain a basic understanding of the two-stage combustion concept in a light-duty diesel engine. With a basic understanding of two-stage combustion, a multi-objective genetic algorithm (MOGA) was used to optimize engine parameters to minimize pollutants, engine noise, and fuel consumption.