Search Results

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.

In this paper, the simulation of the multi-objective optimization problem of a diesel engine is performed using the phenomenological model of a diesel engine and the genetic algorithm. The target purpose functions are Specific fuel consumption, NOx, and Soot. The design variable is a shape of injection rate. In this research, we emphasize the following three topics by applying the optimization techniques to an emission problem of a diesel engine. Firstly, the multiple injections control the objectives. Secondly, the multi-objective optimization is very useful in an emission problem. Finally, the phenomenological model has a great advantage for optimization. The developed system is illustrated with the simulation examples.

The present study extends the recently developed HIDECS-GA computer code to optimize diesel engine emissions and fuel economy with the existing techniques, such as exhaust gas recirculation (EGR) and multiple injections. A computational model of diesel engines named HIDECS is incorporated with the genetic algorithm (GA) to solve multi-objective optimization problems related to engine design. The phenomenological model, HIDECS code is used for analyzing the emissions and performance of a diesel engine. An extended Genetic Algorithm called the ‘Neighborhood Cultivation Genetic Algorithm’ (NCGA) is used as an optimizer due to its ability to derive the solutions with high accuracy effectively. In this paper, the HIDECS-NCGA methodology is used to optimize engine emissions and economy, simultaneously. The multiple injection patterns are included, along with the start of injection timing, and EGR rate.