Fast and accurate prediction of flow fields around common high-rise buildings under various wind directions using a Fourier skip connection residual U-shaped convolutional neural network

Abstract

As the geometries of high-rise buildings become increasingly complex, accurately and efficiently predicting their wind field characteristics is essential for wind-resistant structural design. Traditional computational fluid dynamics (CFD) methods, although highly accurate, incur substantial computational costs and are thus impractical for rapid evaluation. This study proposes a CFD data-driven deep learning model for fast and accurate prediction of flow fields around common high-rise buildings under various wind directions. Based on the exposure category B wind field defined in the Load Standard Code of China, a flow field dataset was constructed using steady Reynolds-averaged Navier–Stokes for five building shapes under different wind directions. A residual U-shaped convolutional neural network with Fourier skip connection (FSC-ResUNet) was developed, and ablation experiments were conducted against U-shaped convolutional neural network (UNet), residual U-shaped convolutional neural network (ResUNet), and U-shaped convolutional neural network with Fourier skip connection (FSC-UNet). Results show that the predictions of FSC-ResUNet are highly consistent with CFD simulations, with coefficients of determination (R2) for velocity and pressure fields close to 0.99, minimal root mean squared error, and overall weighted absolute percentage errors generally below 10% and below 5% for most cases, significantly outperforming the comparison models. Flow field visualization and streamline characteristics analysis further confirm that the model successfully captures key flow features across different cross-sectional shapes, such as separated shear layers and recirculating vortices. Compared with the hour-level computation time of CFD, the trained model predicts new cases within seconds, achieving several orders of magnitude improvement in efficiency. This study demonstrates the significant potential of deep learning in wind engineering flow field prediction and provides an efficient and reliable data-driven tool for fast wind load evaluation and aerodynamic shape optimization of buildings.