Airflow Field Prediction for Quadrotor UAVs Based on Spatiotemporal Prediction Network

Abstract

To address the limitations of traditional computational fluid dynamics (CFD) simulations, such as high computational cost, long processing times, and limited scalability, this study identifies the inefficiencies of existing data-driven prediction methods, which often lack spatial–temporal coordination mechanisms and fail to capture fine-grained dynamic features of UAV airflow fields. We propose a novel deep learning model, VAN-ConvLSTM, for rapid and accurate prediction of UAV downwash airflow. Unlike conventional ConvLSTM-based frameworks, which struggle with modeling long-range dependencies and detailed spatial variations, our model introduces a visual attention unit (VAN) to enhance spatiotemporal sensitivity. The model architecture combines a convolutional encoder for spatial feature extraction, a VAN module for attention-guided temporal modeling, and a ConvLSTM decoder for sequence generation. This synergistic design improves both the accuracy and interpretability of airflow prediction. Experimental results show that the VAN-ConvLSTM model achieves an SSIM score of 0.96, demonstrating high consistency with CFD simulations. Compared to baseline methods, our model reduces error while improving stability and spatial fidelity. Ablation studies further validate the individual contributions of VAN and ConvLSTM modules. The results, verified through three representative case studies, confirm that VAN-ConvLSTM outperforms state-of-the-art approaches across multiple evaluation metrics, while offering significantly enhanced computational efficiency. This demonstrates its strong potential as a reliable and scalable alternative to traditional CFD methods in rotor airflow prediction scenarios.