Fast Prediction and Optimization of Building Wind Environment Using CFD and Deep Learning Method

Abstract

CFD offers advantages over wind tunnel experiments in the prediction and optimization of building wind environment; however, the computational costs associated with optimizing architectural wind environment remain a challenge. In this study, an approach that combines deep learning techniques with CFD simulations is proposed for the prediction and optimization of the architectural wind environment efficiently. A dataset of wind field is constructed using CFD simulation, considering various wind directions, wind speeds, and building spacing. Subsequently, a U-net deep learning model is trained as a surrogate model to rapidly predict the architectural wind field under different conditions. The results indicate that the model can accurately predict the wind field in buildings. The prediction time of building wind field is only 1/900 of that of CFD simulations, making it a viable surrogate model for wind environment optimization. Furthermore, considering all the building layouts and inflow conditions examined in this study, the maximum and minimum uniform wind speed area ratios Auni are 0.84 and 0.13, respectively. Under a single inflow speed, the maximum improvement in the Auni is 0.4, with an improvement rate of 48%. The results demonstrate the effectiveness of the proposed method as an efficient approach for optimizing architectural wind environment.