# Fast Prediction and Optimization of Building Wind Environment Using CFD and Deep Learning Method
## 1 Introduction
[35], using CNN architectures, achieved very accurate results for velocity and pressure fields of stationary fluids around simply shaped obstacles, with a computational cost three to five orders of magnitude lower than CFD simulations. Many problems of CNN in turbulent flow have also been studied. [PERSON] et al. proposed a deep learning framework based on fully convolutional neural networks for predicting transonic flow fields [36] and pressure fields [37] with different airfoils. [PERSON] et al. [38], using DL techniques, predicted turbulent flows on a channel, and [PERSON] et al. [39] approximated the velocity and pressure fields of the RANS-based turbulence model on airfoils with a CNN. To achieve this, a CNN model was trained with the Signed Distance Field (SDF) [40] of building layouts and incoming wind speed as input data, and the wind fields obtained through CFD simulations as the output data. The trained model serves to expedite the optimization of architectural wind fields and exhibited high prediction efficiency and accuracy. [PERSON] et al. [41] proposed a method to predict the saturation variation and flow velocity profile of two-dimensional multiphase flow in porous media and found that the prediction is consistent with the CFD results. [PERSON] et al. [42] used conditional generative adversarial networks to rapidly reconstruct the wind speed field of pedestrian height, but were unable to predict the velocity distribution under different inflow speeds. Graph Neural Networks (GNNs) can flexibly represent grid centers or nodes as vertices in graph data and are considered more suitable for prediction problems in unstructured grid CFD data structures. Consequently, GNN models are increasingly being applied in flow field prediction research. [PERSON] et al. [43] constructed a GNN-based U-net network architecture for vortex structure recognition in unstructured grid CFD flow fields. [PERSON] et al. [44] built a multi-scale GNN structure to predict flow fields around a series of length scales and complex boundary geometries. [PERSON] et al. [45] used Graph Neural Networks to predict flow fields of different building layouts, with good consistency between predicted results and CFD simulation results. However, due to the complete flow field data used for each GNN training, there is a high demand for GPU memory and training costs are significant. To address this issue, [PERSON] et al. [46] partitioned the graph data into multiple small graphs and trained them separately, thereby reducing the model's dependence on GPU memory for training. Nevertheless, for three-dimensional flow, if one aims to fully represent grid data, GNN models require a large number of vertices and edges to represent grid information and the associations between grids, resulting in a dependency on GPU memory and high computational performance. [PERSON] et al. [47] proposed a Gaussian Process-based emulator to achieve fast prediction of the wind velocity around single lift-up buildings with eight design parameters; the emulator achieved a speedup of \(10^{7}\) times over the CFD simulation in predicting wind velocity. [PERSON] et al. [48] utilized a machine learning model to predict the unmeasured velocities around two adjacent building models by particle image velocimetry (PIV), thereby obtaining complete wind data. [PERSON] et al. [49] proposed a hybrid framework with machine learning for accurate prediction of the low-velocity areas around buildings. Therefore, deep learning models can serve as surrogate models to replace costly CFD simulations for efficient and accurate prediction of certain physical fields in addressing many problems. Thus, the utilization of deep learning models becomes highly significant in overcoming the expensive costs associated with traditional experiments or CFD simulations for optimizing the built environment's wind conditions.