# Fast Prediction and Optimization of Building Wind Environment Using CFD and Deep Learning Method
## 4 Conclusions  
In response to the high costs associated with CFD methods for studying building wind environments, this study focuses on the rapid optimization of building wind environments, achieving fast prediction and optimization of building wind environments using the CFD and CNN models. This study proposes a fast prediction and optimization method for building wind environments based on the U-Net deep learning surrogate model. The method is used to optimize the building wind environment for different building layouts, incoming wind speeds, and wind direction angles. The approach accurately learns the characteristics of the building wind field under various building layouts and incoming flow conditions, providing high prediction accuracy and low computational costs. The results demonstrate that the deep learning surrogate model accurately learns the building wind environment characteristics under different building layouts and incoming flow conditions compared to the results of CFD simulations. Compared to traditional CFD methods, the prediction of building wind environments using the CNN model can be accelerated by approximately 900, greatly improving the efficiency of predicting building wind environments and thus holding significant importance for building wind environment optimization; the optimized results are consistent with those obtained from CFD simulations, accurately predicting the building wind field for various building layouts and incoming flow conditions. This study also evaluates the building wind field using performance indicators. The optimal building layout and incoming flow conditions for the building wind environment in this study are a wind direction angle of \(\theta=90^{\circ}\), incoming wind speed \(V=4\) m/s, and a building spacing of 45 m, with a uniformity coefficient \(A_{uni}\) of 0.84, The worst building layout for the wind environment is a wind direction angle of \(\theta=90^{\circ}\), incoming wind speed \(V=7\) m/s, and a building spacing of 45 m, with a uniformity coefficient \(A_{uni}\) is 0.13.  
The essence of deep learning lies in being data-driven, where the generalization and accuracy of the model depend on high-quality datasets. With the advancement of computer technology, more diverse and abundant building wind environment datasets can be constructed through CFD numerical simulations, laying a solid foundation for intelligent computation in the field of building wind environment engineering. Future works may include the study of wind environment characteristics for groups of buildings, enabling fast prediction and optimization of wind environments for different scales of building clusters, thus providing a cost-effective and powerful tool for urban building design and planning.  
Y.Y.: Conceptualization, Methodology, Software, Investigation, Data curation, Writing--Original draft. F.Y.: Writing-Reviewing and Editing. N.M.: Methodology, Writing--Reviewing and Editing, Supervision. All authors have read and agreed to the published version of the manuscript.  
Figure 13: Comparison of computational efficiency of CFD and CNN models.  
Funding: The study was supported by National Natural Science Foundation of China (NSFC Grant No. 52276092), and Shandong Provincial Natural Science Foundation, China (No.: ZR2020 ME170). Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: The raw data supporting the conclusions of this article will be made available by the authors on request. Conflicts of Interest: The authors declare no conflicts of interest. The funders had no role in the design of the study, in the collection, analyses, or interpretation of data, in the writing of the manuscript, or in the decision to publish the results. References