Global urbanization has underscored the significance of urban microclimates for human comfort, health, and building/urban energy efficiency. They profoundly influence building design and urban planning as major environmental impacts. Understanding local microclimates is essential for cities to prepare for climate change and effectively implement resilience measures. However, analyzing urban microclimates requires considering a complex array of outdoor parameters within computational domains at the city scale over a longer period than indoors. As a result, numerical methods like Computational Fluid Dynamics (CFD) become computationally expensive when evaluating the impact of urban microclimates. The rise of deep learning techniques has opened new opportunities for accelerating the modeling of complex non-linear interactions and system dynamics. Recently, the Fourier Neural Operator (FNO) has been shown to be very promising in accelerating solving the Partial Differential Equations (PDEs) and modeling fluid dynamic systems. In this work, we apply the FNO network for real-time three-dimensional (3D) urban wind field simulation. The training and testing data are generated from CFD simulation of the urban area, based on the semi-Lagrangian approach and fractional stepping method to simulate urban microclimate features for modeling large-scale urban problems. Numerical experiments show that the FNO model can accurately reconstruct the instantaneous spatial velocity field. We further evaluate the trained FNO model on unseen data with different wind directions, and the results show that the FNO model can generalize well on different wind directions. More importantly, the FNO approach can make predictions within milliseconds on the graphics processing unit, making real-time simulation of 3D dynamic urban microclimate possible.

翻译：全球城市化进程凸显了城市微气候对人类舒适度、健康以及建筑/城市能效的重要性。作为主要的环境影响因素，城市微气候深刻影响着建筑设计及城市规划。理解局部微气候对于城市应对气候变化并有效实施韧性措施至关重要。然而，分析城市微气候需在城市尺度计算域内考虑复杂的室外参数，且时间跨度长于室内环境。因此，在评估城市微气候影响时，计算流体动力学等数值方法计算成本高昂。深度学习技术的兴起为解决复杂非线性交互与系统动力学的建模加速提供了新机遇。近期研究表明，傅里叶神经算子在加速求解偏微分方程和流体动力系统建模方面极具潜力。本文将FNO网络应用于实时三维城市风场模拟。训练与测试数据基于半拉格朗日方法和分步法对城市区域进行CFD仿真生成，以模拟城市微气候特征并建模大规模城市问题。数值实验表明，FNO模型能够精确重构瞬时空间速度场。我们进一步评估了训练后的FNO模型在具有不同风向的未见数据上的表现，结果显示该模型能较好地泛化到不同风向场景。更关键的是，FNO方法可在图形处理器上于毫秒级完成预测，使三维动态城市微气候的实时模拟成为可能。