Because of their durability and fuel efficiency, diesel engines are installed in small to large vehicles. With increasing environmental concerns and legislated emission standards, current research is focused on reduction of Soot and NOx simultaneously while maintaining reasonable fuel economy.

In this research, the optimization system designs a diesel engine with small amounts of Soot and NOx along with high fuel efficiency. There are three components; those are the phenomenological diesel engine model, the Genetic Algorithm, and NetSolve.

HIDECS is the most sophisticated phenomenological spray-combustion model currently available, originally developed at the University of Hiroshima. It has already demonstrated potential as a predictive tool for both performance and emissions in several types of direct injection diesel engines.

Genetic Algorithm (GA) is the optimization algorithm that imitates the evolution of living creatures. In nature, inadaptable creatures to an environment meet extinction, and only adapted creatures can survive and reproduce. A repetition of this natural selection spreads the superior genes to conspecifics and then the species prospers. GA models this process of nature on computers.

GA can be applied to several types of optimization problems by encoding design variables of individuals. Searching for the solution proceeds by performing the three genetic operations on the individuals; selection, crossover, and mutation, which play an important role in GA. Selection is an operation that imitates the survival of the fittest in nature. The individuals are selected for the next generation according to their fitness. Crossover is an operation that imitates the reproduction of living creatures. The crossover exchanges the information of the chromosomes among individuals. Mutation is an operation that imitates the failure that occurs when copying the information of DNA. Mutating the individuals in a proper probability maintains the diversity of the population. NetSolve is Grid RPC middleware. Since it takes a lot of time to derive the optimum solution by GA, parallel processing is preferred. GA is a very suitable algorithm for performing parallel processing and the farming function of NetSolve is very easy to apply to GA. The following picture illustrates the system overview.

In this system, GA is performed on the client side. When the searching points are evaluated, the data is sent to the server and calculated using the faming function.

The above figure illustrates one of the examples. In this case, the engine that has the minimum amounts of smoke, NOx, and SFC at the same time is to be found. Non-dominant solutions are derived. It needs a huge computational cost to derive these results. However, NetSolve farming helps this system to reduce the total calculation cost.

Researcher: Tomo Hiroyasu ( tomo@is.doshisha.ac.jp)
Institution: Intelligent Systems Design Lab/Doshisha University