Data mining and prediction of ship shock spectral velocity based on RBF neural network

Calculation and analysis of ship shock environment under action of underwater non-contact explosion load are a key work of ship equipment anti-shock design, and how to quickly and effectively predict ship shock environment is a problem of concern. Here, the finite element (FE) calculation models of more than 10 ships with reasonable size distribution and various forms were established. 400 groups of sample measuring points for each ship were chosen and they were evenly distributed on different deck layers along ship length. After loading underwater non-contact explosion load, FE calculations were performed to form more than 1. 2 million ship shock environment data, and establish a large-scale ship shock environment database. The radial basis function (RBF) neural network was used as the framework to establish the ship shock environment prediction model. Ship main-scale parameters, working condition setting parameters for numerical simulation of ship underwater explosion and position coordinates of investigation points were taken as input parameters of the neural network, and spectral velocity of ship investigation point was taken as the only output to train the established RBF network model. The network parameters were optimized using the clustering algorithm. After model training, shock environments of unknown ships under given working conditions were predicted and analyzed. The prediction results showed that the RBF neural network prediction model optimized using the optimization algorithm can not only have higher prediction accuracy, but also have better performances of generalization and robustness; this method can provide a new method for rapid prediction of ship shock environment in design stage. © 2022 Chinese Vibration Engineering Society. All rights reserved.