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

2025/05/21 by Chang Liu, Liu, Chang
Engineering · Environmental Science · #Advanced Sensor and Control Systems #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Flow Measurement and Analysis #Hydrological Forecasting Using AI

paper · pdf · doi:10.48550/arxiv.2505.15533

openalex publication_date 2025/05/21 · openalex created_date 2025/10/19 · openalex updated_date 2026/07/28

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.

Citations

• Machine Learning on Graphs: A Model and Comprehensive Taxonomy
• DeepCFD: Efficient Steady-State Laminar Flow Approximation with Deep Convolutional Neural Networks
• Latent-space Physics: Towards Learning the Temporal Evolution of Fluid Flow
• Prediction of laminar vortex shedding over a cylinder using deep learning
• Semi-Supervised Classification with Graph Convolutional Networks
• Deep Residual Learning for Image Recognition

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