用深度学习模型1秒完成城市风场预测，精度媲美耗时10小时的仿真。
From large-eddy simulations to deep learning: A U-net model for fast urban canopy flow predictions
- 基于U-Net架构，输入建筑布局与距离函数，预测多高度风速和湍流强度。
- 测试误差仅9.3%（风速）和5.2%（湍流），速度提升超3600倍。
- 适合城市规划、建筑设计等需快速风环境评估的场景。
准确预测城市街区风场对保障行人舒适性、安全性和可持续城市设计至关重要。传统风洞实验与大涡模拟（LES）方法受限于高成本、高算力和长耗时。本研究提出一种深度神经网络（DNN）模型，将单次LES计算耗时从32核CPU上约10小时缩短至单张GPU上约1秒。模型基于包含252种人工城市布局、7个风向（0°至90°，间隔15°）的LES数据训练，采用U-Net结构，输入为256×256×9的张量，包含2D建筑分布及符号距离函数与梯度信息。通过跳跃连接引入空间注意力模块，损失函数融合均方根误差、梯度幅值与L2正则化。在50组测试案例中，风速预测平均相对误差为9.3%，湍流强度为5.2%。结果表明，深度学习可实现快速精准的城市风环境评估。代码开源：https://github.com/tvarg/Urban-FlowUnet.git
原文摘要 · Abstract (English)
Accurate prediction of wind flow fields in urban canopies is crucial for ensuring pedestrian comfort, safety, and sustainable urban design. Traditional methods using wind tunnels and Computational Fluid Dynamics, such as Large-Eddy Simulations (LES), are limited by high costs, computational demands, and time requirements. This study presents a deep neural network (DNN) approach for fast and accurate predictions of urban wind flow fields, reducing computation time from an order of 10 hours on 32 CPUs for one LES evaluation to an order of 1 second on a single GPU using the DNN model. We employ a U-Net architecture trained on LES data including 252 synthetic urban configurations at seven wind directions ($0^{o}$ to $90^{o}$ in $15^{o}$ increments). The model predicts two key quantities of interest: mean velocity magnitude and streamwise turbulence intensity, at multiple heights within the urban canopy. The U-net uses 2D building representations augmented with signed distance functions and their gradients as inputs, forming a $256\times256\times9$ tensor. In addition, a Spatial Attention Module is used for feature transfer through skip connections. The loss function combines the root-mean-square error of predictions, their gradient magnitudes, and L2 regularization. Model evaluation on 50 test cases demonstrates high accuracy with an overall mean relative error of 9.3% for velocity magnitude and 5.2% for turbulence intensity. This research shows the potential of deep learning approaches to provide fast, accurate urban wind assessments essential for creating comfortable and safe urban environments. Code is available at https://github.com/tvarg/Urban-FlowUnet.git
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