Robust optimisation of vibro-acoustic system based on an elliptical basis function neural network. (February 2019)

• Record Type: Journal Article
• Authors: Chen, Wanglin; Gu, Zhengqi; Ma, Xiaokui; Zhang, Sha; Han, Zhengtong; Zong, Yiqi
• Is Part Of: Applied acoustics. Volume 145(2019)
• Page Start: 41
• Page End: 51
• Publication Date: 2019-02
• Subjects: Vibro-acoustic system -- Uncertainty -- Robust optimisation -- Monte Carlo simulation; Acoustical engineering -- Periodicals; Periodicals; 620.2
• DOI: 10.1016/j.apacoust.2018.09.013
• Languages: English
• ISSNs: 0003-682X

Abstract

Graphical abstract: Highlights: A surrogate model containing uncertain variables is established based on Elliptical Basis Functions neural network. The performance and robustness of the sound pressure level are obtained by using Monte Carlo simulation. Different forms of robust optimization of the vibro-acoustic system are carried out. 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.