Genetic Algorithms Optimization of Diesel Engine Emissions and Fuel Efficiency with Air Swirl, EGR,Injection Timing and Multiple Injections

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. It is found that the combination of HIDECS and NCGA is efficient and low in computational costs. The Pareto optimum solutions obtained from HIDECS-NCGA are very useful to the engine designers. They show that it is possible to reduce emissions without increasing the fuel consumption by the optimization of exhaust gas recirculation (EGR) and multiple injections.