Computational optimization of reactivity controlled compression ignition combustion to achieve high efficiency and clean combustion

Abstract

One of the major limitations of reactivity controlled compression ignition is higher unburned hydrocarbon and carbon monoxide emissions and lower thermal efficiency at part load operating conditions. In the present study, a combined numerical approach using a commercial three-dimensional computational fluid dynamics code CONVERGE along with artificial neural network and genetic algorithm is presented to address the above limitation. A production light-duty diesel engine is modified to run in reactivity controlled compression ignition by replacing an existing mechanical fuel injection system with a flexible electronic port fuel injection and common rail direct injection systems. The injection schedules of port fuel injection and direct injection injectors are controlled using National Instruments port and direct injection driver modules. Upon validation of combustion and emission parameters, parametric investigations are carried out to establish the effects of direct-injected diesel fuel timing start of injection (SOI), premixed fuel ratio and intake charge temperature on the engine performance and emissions in reactivity controlled compression ignition. The results obtained show that the start of injection timing and intake charge temperature significantly influence combustion phasing, while the premixed fuel ratio controls mixture reactivity and combustion quality. By utilizing the data generated with the validated computational fluid dynamics models, the artificial neural network models are trained to predict the engine exhaust emissions and efficiency. The artificial neural network models for gross indicated efficiency and oxides of nitrogen (NOx) are then coupled with genetic algorithm to maximize gross indicated efficiency while keeping the NOx and soot emissions within Euro VI emission limits. By optimizing the start of injection timing, premixed fuel ratio and intake charge temperature simultaneously using the artificial neural network models coupled with genetic algorithm, 19% improvement in gross indicated efficiency, 60% and 64% reduction in hydrocarbon and carbon monoxide emissions, respectively, are obtained in reactivity controlled compression ignition compared to the baseline case.