A PSO-based computational framework to design active noise cancelation systems for smart vehicle enclosures

Abstract

In this study, active noise cancelation (ANC) systems are developed by a computational optimization framework based on particle swarm optimization (PSO), aiming to attenuate engine noise inside smart cubic vehicle enclosures. To have rapid estimation of acoustic properties, the main PSO algorithm is coupled with an analytical solution based on modified modal interaction method to evaluate the cost function. The optimum configurations, i.e., best positions and volume velocities of secondary sound sources, are defined for each resonant frequency. For numerical simulations, two vehicle enclosures of different size are considered to assess the applicability of the optimization algorithm. The overall performance of determined ANC systems is investigated, and it is shown that substantial noise reduction is achieved.