A methodology for IC engine design has been formulated which incorporates multi-dimensional modeling and experimental validation to simulate and optimize direct injection diesel engine combustion and emissions formation. The computer code, KIVA-GA, performs full cycle engine simulations within the framework of a Genetic Algorithm (GA) global optimization code. The methodology was applied to optimize a heavy-duty diesel truck engine. The study simultaneously investigated the effects of six engine input parameters on emissions and performance for both a high speed, medium load and medium speed, high load operating point. The start of injection (SOI), injection pressure, amount of exhaust gas recirculation (EGR), boost pressure and split injection rate-shape were optimized. The convergence of the GA optimization process was demonstrated and the results were compared to those of an available experimental optimization study employing a Response Surface Method (RSM) which uses statistically designed experiments to determine an optimum design. In addition, the parameters of one of the computationally predicted optimum cases was run experimentally and good agreement was obtained.
The potential for ultra-low emissions was assessed through additional computational GA runs that included higher maximum EGR levels than were available in the experimental data (up to 50%). The predicted optimum results in significantly lower soot and NOx emissions together with improved fuel consumption compared to the baseline design. The present results indicate that an efficient design methodology has been developed for optimization of internal combustion engines, one that allows simultaneous optimization of a large number of parameters.