Graph Convolutional Network Surrogate Model for Mesh-Based Structure-Borne Noise Simulation

Abstract

This study presents a unique method of building a surrogate model using a graph convolutional network (GCN) for mesh-based structure-borne noise analysis of a fluid–structure coupled system. Structure-borne noise generated from irregular shape panel vibration and sound pressure was measured in a closed-volume cavity coupled with the panel. The proposed network was trained to predict the sound pressure level with three steps. The first step is predicting the natural frequency of panels and cavities using the graph convolutional network, the second step is to predict the averaged vibration and acoustic response of the panel and cavity, respectively, in a given excitation condition using a triangular wave-type inference function based on the natural frequency predicted from the first step, and the third step is to predict the sound pressure in a cavity using a panel and cavity average response as an input to a 2D convolutional neural network (CNN). This method is an efficient way to build a surrogate model for predicting the response of a system which consisted of several sub-systems, like a full vehicle system model. We predicted the response of each sub-system and then combined this to obtain the response of the whole system. Using this method, an average 0.86 r-square value was achieved to predict the panel-induced structure-borne noise in a cavity from 10 to 500 Hz range in 1/12 octave band. This study is the first step towards creating a surrogate model of an engineering system with various sub-systems by changing it into a heterogeneous graph.