Convolutional Long Short-Term Memory Neural Networks Based Numerical Simulation of Flow Field

TL;DR

This paper introduces an improved Convolutional LSTM neural network for flow field prediction, demonstrating enhanced feature extraction with fewer parameters and reduced training time compared to standard models.

Contribution

The paper proposes an enhanced ConvLSTM model incorporating residual networks and attention mechanisms for more efficient flow field prediction.

Findings

Improved ConvLSTM extracts more features with fewer parameters.

The model achieves shorter training times.

It effectively predicts flow around a circular cylinder.

Abstract

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.