This paper presents a fluid sloshing model using the artificial neural network method (ANN). Determining the fluid sloshing model in the tank is a challenging task due to its nonlinearity and complexity of behavior to its environmental and operational conditions. Due to the problems of laboratory modeling, the use of numerical modeling to analyze this phenomenon can be justified. In this paper, first, the fluid sloshing in the tank is simulated by the smooth particle hydrodynamics method (SPH). The input-output data for training the artificial neural network is based on the obtained results. Finally, the maximum force due to the fluid sloshing is obtained by changing different parameters.

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.