Reduction of Heavy Duty Diesel Engine Emission and Fuel Economy with Multi-Objective Genetic Algorithm and Phenomenological Model 2004-01-0531
In this study, a system to perform a parameter search of heavy-duty diesel engines is proposed. Recently, it has become essential to use design methodologies including computer simulations for diesel engines that have small amounts of NOx and SOOT while maintaining reasonable fuel economy. For this purpose, multi-objective optimization techniques should be used. Multi-objective optimization problems have several types of objectives and they should be minimized or maximized at the same time. There is often a trade-off relationship between objects and derivation of the Pareto optimum solutions that express the relationship between the objects is one of the goals in this case. The proposed system consists of a multi-objective genetic algorithm (MOGA) and phenomenological model. MOGA has strong search capability for Pareto optimum solutions. However, MOGA requires a large number of iterations. Therefore, for MOGA, a diesel combustion simulator that can express combustion precisely with small calculation cost is essential. Phenomenological models can simulate diesel engine combustions precisely with small calculation cost. Therefore, phenomenological models are suitable for MOGA. In the optimization simulations, fuel injection shape, boost pressure, EGR rate, start angle of injection, duration angle of injection, and swirl ration were chosen as design variables. The values of these design variables were optimized to reduce SFC, NOx, and SOOT. Through the optimization simulations, the following five points were made clarified. First, the proposed system can find the Pareto optimum solutions successfully. Second, MOGAs are very effective to derive the solutions. Third, phenomenological models are very suitable for MOGAs, as they can perform precise simulations with small calculation cost. Fourth, multi-pulse injection shape can affect the amounts of SFC, NOx, and SOOT. Finally, parameter optimization is essential for in diesel engine design.
Citation: Hiroyasu, T., Miki, M., Kim, M., Watanabe, S. et al., "Reduction of Heavy Duty Diesel Engine Emission and Fuel Economy with Multi-Objective Genetic Algorithm and Phenomenological Model," SAE Technical Paper 2004-01-0531, 2004, https://doi.org/10.4271/2004-01-0531. Download Citation
T. Hiroyasu, M. Miki, M. Kim, S. Watanabe, H. Hiroyasu, H. Miao
Doshisha University, National Institute of Advanced Industrial Science and Technology, Kinki University
SAE 2004 World Congress & Exhibition
Diesel Fuel Injection and Sprays 2004-SP-1824