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A computational optimization study was performed for a direct-injection diesel engine using a recently developed 1-D-KIVA3v-GA (1-Dimensional-KIVA3v-Genetic Algorithm) computer code. The code performs a full engine cycle simulation within the framework of a genetic algorithm (GA) code. Design fitness is determined using a 1-D (one-dimensional) gas dynamics code for the simulation of the gas exchange process, coupled with the KIVA3v code for three-dimensional simulations of spray, combustion and emissions formation. The 1-D-KIVA3v-GA methodology was used to simultaneously investigate the effect of eight engine input parameters on emissions and performance for four cases, which include cases at 2500 RPM and 1000 RPM, with both simulated at high-load and low-load conditions.

Multidimensional models require physical inputs about the engine operating conditions. This paper explores the effects of unavoidable experimental uncertainties in the specification of important parameters such as the start of injection, duration of injection, amount of fuel injected per cycle, gas temperature at IVC, and the spray nozzle hole diameter. The study was conducted for a Caterpillar 3401 heavy-duty diesel engine for which extensive experimental data is available. The engine operating conditions include operation at high and low loads, with single and double injections. The computations were performed using a modified version of the KIVA3V code. Initially the model was calibrated to give very good agreement with experimental data in terms of trends and also to a lesser degree in absolute values, over a range of operating conditions and injection timings.