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Optimal fuel injection strategies are obtained with a micro-genetic algorithm and an adaptive gradient method for a nonroad, medium-speed DI diesel engine equipped with a multi-orifice, asynchronous fuel injection system. The gradient optimization utilizes a fast-converging backtracking algorithm and an adaptive cost function which is based on the penalty method, where the penalty coefficient is increased after every line search. The micro-genetic algorithm uses parameter combinations of the best two individuals in each generation until a local convergence is achieved, and then generates a random population to continue the global search. The optimizations have been performed for a two pulse fuel injection strategy where the optimization parameters are the injection timings and the nozzle orifice diameters.

One-dimensional simulation methods for unsteady (transient) engine operations have been developed and published in previous studies. These 1-D methods utilize heat release and emissions results obtained from 3-D CFD simulations which are stored in a data library. The goal of this study is to improve the 1-D methodology by optimizing the control strategies. Also, additional independent parameters are introduced to extend the 3-D data library, while, as in the previous studies, the number of interpolation points for each parameter remains small. The data points for the 3-D simulations are selected in the vicinity of the expected trajectories obtained from the independent parameter changes, as predicted by the transient 1-D simulations. By this approach, the number of time-consuming 3-D simulations is limited to a reasonable amount.