Traditional computational fluid dynamics (CFD) methods used for wind field prediction can be time‐consuming, limiting architectural creativity in the early‐stage design process. Deep learning models have the potential to significantly speed up wind field prediction. This work introduces a convolutional neural network (CNN) approach based on the U‐Net architecture, to rapidly predict wind in simplified urban environments, representative of early‐stage design. The process of generating a wind field prediction at pedestrian level is reformulated from a 3D CFD simulation into a 2D image‐to‐image translation task, using the projected building heights as input. Testing on standard consumer hardware shows that our model can efficiently predict wind velocities in urban settings in less than 1 ms. Further tests on different configurations of the model, combined with a Pareto front analysis, helped identify the trade‐off between accuracy and computational efficiency. The fastest configuration is close to seven times faster, while having a relative loss, which is 1.8 times higher than the most accurate configuration. This CNN‐based approach provides a fast and efficient method for pedestrian wind comfort (PWC) analysis, potentially aiding in more efficient urban design processes.

中文翻译：

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使用帕累托优化卷积神经网络进行早期设计的快速行人级风场预测

用于风场预测的传统计算流体动力学（CFD）方法可能非常耗时，限制了早期设计过程中的建筑创造力。深度学习模型有可能显着加快风场预测速度。这项工作引入了一种基于 U-Net 架构的卷积神经网络 (CNN) 方法，可以快速预测简化的城市环境中的风，这是早期设计的代表。使用预计的建筑物高度作为输入，将行人层面的风场预测生成过程从 3D CFD 模拟重新表述为 2D 图像到图像转换任务。对标准消费级硬件的测试表明，我们的模型可以在不到 1 毫秒的时间内有效预测城市环境中的风速。对模型不同配置的进一步测试，结合帕累托前沿分析，有助于确定准确性和计算效率之间的权衡。最快的配置速度接近七倍，同时相对损耗比最精确的配置高 1.8 倍。这种基于 CNN 的方法为行人风舒适度 (PWC) 分析提供了一种快速有效的方法，可能有助于更高效的城市设计过程。