Multiobjective design optimization of merging configuration for an exhaust manifold of a car engine

Abstract

Multiobjective design optimization system of exhaust manifold shapes for a car engine has been developed using Divided Range Multiobjective Genetic Algorithm (DRMOGA) to obtain more engine power as well as to achieve less environmental impact. The three-dimensional manifold shapes are evaluated by the unstructured, unsteady Euler code coupled with the empirical engine cycle simulation code. This automated design system using DRMOGA was confirmed to find Pareto solutions for the highly nonlinear problems.