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，可以在简化的城市环境中快速预测风，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，可能有助于提高城市设计流程的效率。