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.