改进的ConvLSTM模型更高效地预测流场，适合工程仿真加速。
Convolutional Long Short-Term Memory Neural Networks Based Numerical Simulation of Flow Field
- 用改进ConvLSTM融合残差与注意力机制，捕捉流场时空特征。
- 相比标准模型，参数更少、训练更快，且能提取更多特征。
- 结合动态网格与自定义函数生成真实流场数据，适用于物理仿真加速。
计算流体力学（CFD）是分析流场的主要方法，但其收敛性与精度高度依赖数学模型、数值方法及计算耗时。深度学习为流场分析提供了替代方案。针对流场预测任务，本文提出一种改进的卷积长短期记忆（ConvLSTM）神经网络，以兼顾流场的时空特性。通过动态网格技术与用户自定义函数（UDF），对圆柱绕流进行数值模拟，并在尾迹区域不同时刻采样流场快照，构建了具有足够规模和丰富状态变化的流场数据集。将残差网络与注意力机制融入标准ConvLSTM模型。实验表明，相较于标准模型，改进后的模型在参数更少、训练时间更短的前提下，能够提取更丰富的时空特征。
原文摘要 · Abstract (English)
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.
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