Novel convolutional long short-term memory networks for spatiotemporal prediction of unsteady flow with moving boundary

Accurate simulations of unsteady flow with a moving boundary demand sophisticated numerical schemes and dynamic mesh techniques, which incur high computational costs and remain a challenge in engineering applications. To mitigate this realistic challenge, this paper introduces two novel convolutional long short-term memory (ConvLSTM) networks: (1) K-ConvLSTM (kinetic-convolutional LSTM network), which integrates the kinetic features of the unsteady flow, and (2) EK-ConvLSTM (enhanced kinetic-convolutional LSTM network), which enables further interaction between the kinetic module and the original structure of the vanilla ConvLSTM. Furthermore, we construct a spatiotemporal prediction architecture consisting of the proposed neural networks for unsteady flow prediction. In addition, we utilize two representative unsteady cases, the flow around the oscillating airfoil and the oscillating circular cylinder, to evaluate the predictive accuracy and generalization ability of the proposed models. The numerical results show that K-ConvLSTM is effective when the input length is less than eight. Moreover, compared to the baseline ConvLSTM method, the EK-ConvLSTM model significantly reduces predictive and generalized errors, regardless of the input length.