Convolutional Long Short-Term Memory Neural Networks Based Numerical Simulation of Flow Field
This work addresses flow field simulation for computational fluid dynamics applications, representing an incremental improvement over existing deep learning methods.
The authors tackled the problem of flow field prediction in computational fluid dynamics by proposing an improved Convolutional Long Short-Term Memory neural network, which achieved better feature extraction with fewer parameters and shorter training time compared to a standard ConvLSTM model.
Computational Fluid Dynamics (CFD) is the main approach to analyzing flow field. However, the convergence and accuracy depend largely on mathematical models of flow, numerical methods, and time consumption. Deep learning-based analysis of flow filed provides an alternative. For the task of flow field prediction, an improved Convolutional Long Short-Term Memory (Con-vLSTM) Neural Network is proposed as the baseline network in consideration of the temporal and spatial characteristics of flow field. Combining dynamic mesh technology and User-Defined Function (UDF), numerical simulations of flow around a circular cylinder were conducted. Flow field snapshots were used to sample data from the cylinder's wake region at different time instants, constructing a flow field dataset with sufficient volume and rich flow state var-iations. Residual networks and attention mechanisms are combined with the standard ConvLSTM model. Compared with the standard ConvLSTM model, the results demonstrate that the improved ConvLSTM model can extract more temporal and spatial features while having fewer parameters and shorter train-ing time.