# Fast Prediction and Optimization of Building Wind Environment Using CFD and Deep Learning Method
## 1 Introduction  
With increasing economic development and urbanization, cities have experienced significant changes in their natural environment, resulting in unique urban microclimates. Wind environment has a significant impact on urban air quality and comfort because a favorable wind environment can dilute and remove pollutants accumulated in the city [1; 2], providing good comfort [3]. As a renewable energy source, wind can provide necessary fresh air for buildings through natural ventilation, while also carrying away excess heat or cold indoors [4; 5]. Therefore, cities can reduce their reliance on non-renewable energy sources. Hence, integrating wind energy into urban design and planning is becoming increasingly important, especially in the development of sustainable and energy-efficient buildings [6]. Therefore, optimizing the microclimate in architectural design is an essential part of urban planning, as it has significant practical implications for accurately assessing the surrounding microclimate and improving residents' safety and comfort. Field measurements, atmospheric boundary layer wind tunnel tests, and computational fluid dynamics (CFD) simulations are commonly used methods of studying the wind environment in architecture. However, field measurement methods can only be applied to existing buildings and are not effective during the urban planning phase. Atmospheric boundary layer wind tunnel tests utilize scaled models and simulate real atmospheric boundary layer conditions. They are widely employed in the study of urban wind fields, such as individual building [7] and shape optimization [8; 9; 10; 11; 12; 13; 14], building arrays [15; 16],urban building clusters [17; 18], and urban street canyons [19]. Wind tunnel experiments can simulate the inflow conditions in urban wind fields and provide relatively accurate simulations of the building microclimate. However, data collection within the flow field relies on a limited arrangement of wind speed measurement points, which cannot capture the entire flow field. Wind tunnel experiments also involve high equipment costs, substantial manpower, and time requirements, making it challenging to optimize the complex microclimate of a real city. In recent years, CFD simulations have become an important method for studying the microclimate in architecture due to advancements in computer technology [20; 21; 22; 23; 24; 25; 26]. CFD methods allow for full-scale modeling of individual buildings or urban streets, with easily controllable boundary conditions. They can compute data for the entire flow field, enabling more detailed analysis of the flow field around buildings. This provides an effective means for studying the microclimate in architecture. Various modeling methods are used for simulating turbulence in urban wind fields, including Reynolds-averaged Navier-Stokes equations (RANS) [27; 28], large-eddy simulation (LES) [29; 30], and direct numerical simulation (DNS). DNS, due to its high computational cost, is not suitable for studying flow at the scale of buildings. LES, although having lower computational costs compared to DNS and higher accuracy than RANS [31], still presents challenges for engineering applications. However, for the optimization of urban microclimates, the high demand for data still poses a challenge in terms of computational costs for CFD methods. The integration of deep learning and CFD enables the rapid prediction of flow fields, making it possible to overcome computational challenges. [PERSON] et al. [32] proposed a model based on a convolutional neural network, which was used to predict steady flow through objects. [PERSON] et al. [33] proposed a deep learning model for velocity field prediction of fixed cylinders and found that the model has good generalization for systems with a larger number cylinders. [PERSON] and [PERSON] [34] proposed an efficient convolutional neural network (CNN) model to predict the coefficient of the cylindrical wake flow field under different two-dimensional geometry. [PERSON] et al.