Kriging optimal design of an intake system with meta-heuristic methods for reducing noise

Abstract

Recently, government regulations and public concern have increased interest in noise pollution levels. Therefore, many researchers have studied to reduce this noise in the field of automotive engineering. The current paper proposes an optimal design scheme to reduce the noise of the intake system by adapting Kriging with two meta-heuristic techniques. This was achieved by using performance prediction software (developed by one of the present authors and his co-workers) as a measuring tool for the performance of the intake system. The length and radius of each component of the current intake system were then selected as input variables and the L, table of orthogonal arrays was adapted as a space-filling design. With these simulated data, a correlation parameter can be estimated in Kriging by solving the non-linear problem with a genetic algorithm to find an optimal level for the intake system by optimizing Kriging estimated through simulated annealing. This optimal design scheme gives significant results and is a preferable way to analyse the intake system. Therefore, an optimal design for the intake system is proposed for reducing the noise of its system.