Robust optimisation of vibro-acoustic system based on an elliptical basis function neural network

Abstract

Reducing the sound pressure level (SPL) reliably and effectively is paramount in the presence of uncertainty. Herein, the robust optimisation of a vibro-acoustic system is performed by combining a probabilistic analysis approach and an approximate methodology. A surrogate model containing uncertain variables is established based on the elliptical basis function (EBF) neural network. Using Monte Carlo (MC) simulations, the probability distribution of the output responses are obtained, and the result shows that the sound pressure is influenced by uncertain factors. The simulated annealing (SA) algorithm is adopted to perform the robust optimisation. It shows that the optimisation results not only reduced the SPL inside the passenger car, but also enabled the SPL to be less sensitive to the fluctuations of the uncertain variables.