Simultaneous Reduction of Engine Emissions and Fuel Consumption Using Genetic Algorithms and Multi-Dimensional Spray and Combustion Modeling

A computational optimization study is performed for a heavy-duty direct-injection diesel engine using the recently developed KIVA-GA computer code. KIVA-GA performs full cycle engine simulations within the framework of a Genetic Algorithm (GA) global optimization code. Design fitness is determined using a one-dimensional gas -dynamics code for calculation of the gas exchange process, and a three-dimensional CFD code based on KIVA-3V for spray, combustion and emissions formation.

The performance of the present Genetic Algorithm is demonstrated using a test problem with a multi-modal analytic function in which the optimum is known a priori. The KIVA-GA methodology is next used to simultaneously investigate the effects of six engine input parameters on emissions and performance for a high speed, medium load operating point for which baseline experimental validation data is available. Start of injection (SOI), injection pressure, amount of exhaust gas recirculation (EGR), boost pressure and split injection rate-shape are all explored. 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 a new and efficient computational design methodology has been developed for optimization of internal combustion engines with respect to a large number of parameters.