Prediction of sloshing pressure using image-based deep learning

Abstract

This study investigates an image-based deep learning method for predicting various types of sloshing pressure, including regular, weak impulse, random, and peak pressures. The time series of pressure at a specific location on a tank wall are estimated from independent features of the sequential images of free-surface waves in sloshing flow. A dataset of wave images labeled with pressure is inserted into the residual neural network (ResNet) to enable the training of neural networks without encountering the problem of vanishing gradients. The hyperparameters of the ResNet algorithm are tuned to determine the optimal learning rate, mini-batch size, and strength of L1 regularization. Compared with experimental measurements, the prediction performance regarding the time history of pressure is remarkably good in the non-resonance regime, and the predictions exhibit reasonable agreement in the resonance regime. Notably, the method for pressure normalization has a significant impact on the prediction performance for peak pressure in the resonance regime, affecting both the magnitude and profile of the pressure. Furthermore, to enable practical applications of the proposed model, training is conducted using a comprehensive dataset of wave images including all pressure types. The model successfully estimates the time history for the different pressure types that occur sequentially.