Computational Optimization of a Down-Scaled Diesel Engine Operating in the Conventional 
Diffusion Combustion Regime Using a Multi-Objective Genetic Algorithm

Abstract

Computational optimization of a high-speed diesel engine, combined with diesel engine 
size-scaling, is presented. A multi-objective genetic algorithm was employed to simultaneously 
optimize fuel consumption and engine-out emissions of the down-scaled version of a previously 
optimized baseline engine. By separating the design parameters into hardware parameters (e.g., 
the piston bowl geometry) and controllable parameters (e.g., injection pressure and timings), 
multiple operating conditions were optimized simultaneously. A new variable was introduced to 
evaluate the convergence of the optimization, defined as the ratio of the number of Pareto designs 
and the number of valid designs in each generation. Particular interest was placed on the effect 
of injection pressure on the optimization of the engine and whether the previously optimized 
baseline engine design holds for different engine sizes. For 32 generations, totaling 1024 designs, 
no better design than the initial optimum, which was generated for the baseline engine, was found. 
This indicates that the current engine size-scaling model works well.