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Experiments and Modeling of Adaptive Injection Strategies (AIS) in Low Emissions Diesel Engines
Abstract:
Homogeneous Charge Compression Ignition (HCCI) has been shown as a promising technique for simultaneous NOx and soot reduction while maintaining diesel-like efficiency. Although HCCI has been shown to yield very low emissions levels, spray-wall impingement and high pressure rise rates can be problematic due to the early injection timings necessary for certain HCCI operations. To address spray-wall impingement, an Adaptive Injection Strategy (AIS) was employed. This strategy uses multiple pulses at both low and high injection pressures to prepare an optimal in-cylinder mixture.
A unique Variable Pressure Pulse (VPP) was developed to investigate the AIS concept experimentally. The VPP has the capability of delivering multiple injections at both low and high injection pressures (∼100 bar and ∼1000 bar respectively) through a single injector in the same engine cycle. Comparisons were made between model predictions and engine experiments using the VPP system. The models were able to adequately capture the emissions and performances trends observed in the experiments.
This study uses engine experiments and a multidimensional CFD code coupled with detailed chemistry and a Multi-Objective Genetic Algorithm (MOGA), the KIVA-CHEMKIN-MOGA code, to explore the AIS concept. The computational optimization was performed at a high speed - light load operating condition (2000 rev/min and 5.5 bar IMEP) considering six objectives (NOx, soot, CO, HC, ISFC, and peak PRR) and seven parameters (IVC timing, EGR rate, fuel split, early injection timing, late injection timing, early injection pressure, and late injection pressure). The results show low pressure early injections are key to minimizing spray-wall impingement. Furthermore, a Pareto solution was found with near zero NOx and soot, a peak pressure rise rate of 6.3 bar/deg, and a net ISFC of 190 g/kW-hr.