Accurate and efficient urban wind prediction at city-scale with memory-scalable graph neural network

Abstract

The interaction between buildings and wind significantly impacts the comfort and safety of pedestrians, thereby influencing the sustainability of cities. Computational fluid dynamics (CFD) simulation of wind velocity in urban environments provides valuable insights into building aerodynamics. Traditional CFD solvers are limited by high computational costs, hindering practical engineering applications. Graph neural networks (GNNs) have emerged as a promising approach to accelerate CFD simulations on unstructured meshes. However, their inability to handle large-scale urban wind prediction due to high GPU memory requirements poses a challenge, as GNNs rely on GPUs for fast training and inference. To overcome this limitation, we propose SGMS-GNN, a novel GNN model that accurately and efficiently predicts wind velocity fields in urban environments while maintaining consistent GPU memory usage as the simulation domain increases. We employed a validated CFD model to generate a dataset of wind velocity fields in various urban topologies by simulating wind flow through randomly generated building layouts. Our well-generalized SGMS-GNN demonstrates accurate urban wind field predictions at city-scale, achieving a 70 % reduction in GPU memory usage compared to other GNN models. Furthermore, the proposed model outperforms the CFD model on which it is trained by running 1–2 orders of magnitude faster.