A novel deep U-Net-LSTM framework for time-sequenced hydrodynamics prediction of the SUBOFF AFF-8

Abstract

The computational fluid dynamics (CFD) simulation method is commonly used for large-scale computational engineering problems. However, it usually leads to higher computational costs. The deep learning method has gained significant attention in recent years. However, the traditional methods fail to achieve high-precision pixel-level predictions. They are difficult to predict more detailed, multi-scale features of complex engineering. This study proposes a novel deep U-shaped network-long short term memory (U-Net-LSTM) framework for the rapid time-sequenced hydrodynamic prediction of the SUBOFF. First, a novel framework composed of a deep U-shaped network, two LSTM layers, and a skip connection part is proposed for time-sequenced hydrodynamics prediction. Second, the CFD simulation results of the SUBOFF AFF-8 are validated by referring to published experimental data. Finally, three types of AFF-8 motions are investigated to demonstrate the advantages of the proposed framework in detail. The results demonstrate that the predicted outputs agree well with the CFD simulation results, show good stability and ability to predict additional future results. Compared with the traditional hybrid convolutional neural network-LSTM (CNN-LSTM) framework, the mean square error and mean absolute error are reduced by almost one order of magnitude and two orders of magnitude, respectively, showing that the proposed framework is highly competitive. The GPU cost utilized for running the deep U-Net-LSTM is only 0.33 s for each result, making it possible to achieve real-time prediction. In addition, the computation costs are reduced by six orders of magnitude compared with those in the CFD method.

Keywords

• multi-scale feature
• hydrodynamic
• cfd
• time-sequenced prediction
• u-net-lstm framework