Bubble collapse patterns recognition and flow field prediction based on machine learning

Abstract

A machine learning method is proposed to predict the collapse patterns and flow field state of underwater explosion bubbles subjected to the vertical sidewall and free surface, which can overcome the limitations of high costs of traditional experimental tests and long computation times of numerical simulations. The dataset was obtained by the boundary element method, including the cases of the bubble with different buoyancy parameters at different distances from the free surface and vertical sidewall. Due to the strong geometric nonlinearity of the bubble influenced by boundary, three classification models are adopted to identify the collapse patterns of bubbles, which are support vector machines, K nearest neighbor, and decision tree. Meanwhile, an ensemble learning (EL) model based on the three classification models is adopted to enhance the prediction accuracy. Furthermore, three regression models, which are deep neural network (DNN), extreme learning machine (ELM), and random forest (RF), were adopted and compared to predict flow field information around the bubble. The results show that EL exhibits better robustness to the distribution and proportion of samples when identifying collapse patterns. Meanwhile, compared with ELM and RF, DNN demonstrates stronger performance in capturing nonlinear relationships, especially in regions where the bubble curvature changes abruptly. Moreover, a learning rate decay strategy is proposed to effectively suppress the phenomenon of loss oscillation in the training process of DNN based on adaptive activation functions.