Heavy-Duty Diesel Combustion Optimization Using Multi-Objective Genetic Algorithm and Multi-Dimensional Modeling

A multi-objective genetic algorithm methodology was applied to a heavy-duty diesel engine at three different operating conditions of interest. Separate optimizations were performed over various fuel injection nozzle parameters, piston bowl geometries and swirl ratios (SR). Different beginning of injection (BOI) timings were considered in all optimizations. The objective of the optimizations was to find the best possible fuel economy, NOx, and soot emissions tradeoffs.

The input parameter ranges were determined using design of experiment methodology. A non-dominated sorting genetic algorithm II (NSGA II) was used for the optimization. For the optimization of piston bowl geometry, an automated grid generator was used for efficient mesh generation with variable geometry parameters. The KIVA3V release 2 code with improved ERC sub-models was used. The characteristic time combustion (CTC) model was employed to improve computational efficiency. Six individual optimizations were performed, with two of them performed for each of the three operating conditions (full load, mid-load, and low-load). The first set of three optimized BOI, spray angle, hole size, and the number of holes with fixed piston geometry. The second set optimized BOI, piston geometry, and swirl ratio with fixed fuel injector nozzle design. The optimizations were subject to design constraints including peak cylinder pressure and the temperature at exhaust valve opening. The sensitivity of engine performance to the design parameters of interest was evaluated using a response surface analysis method. The results show that significant reductions in engine-out emissions and fuel consumption can be achieved.