Utilizing Artificial Neural Network for Load Prediction Caused by Fluid Sloshing in Tanks

Abstract

In this research, neural network models were used to predict the action of sloshing phenomena in a tank containing fluid under harmonic excitation. A new methodology is proposed in this analysis to test and simulate fluid sloshing behavior in the tank. The sloshing behavior was first modeled using the smooth particle hydrodynamics (SPH) method. The backpropagation of the error algorithm was then used to apply the two multilayer feed-forward neural networks and the recurrent neural network. The findings of the SPH process are employed in the training and testing of neural networks. Input neural network data include the tank position, velocity, and acceleration, neural output data, and fluid sloshing curve wave position. The findings of the neural networks were correlated with the experimental evidence provided in the literature. The findings revealed that neural networks can be used to predict fluid sloshing.