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和城市规划。理解局地微气候对于城市应对气候变化和有效实施韧性措施至关重要。然而，分析城市微气候需在比室内更长的时期内考虑城市尺度计算域内的一系列复杂室外参数，因此，计算流体力学（CFD）等数值方法在评估城市微气候影响时计算成本高昂。深度学习技术的兴起为加速复杂非线性相互作用及系统动力学建模提供了新机遇。近期，傅立叶神经算子（FNO）在加速求解偏微分方程（PDE）和建模流体动力学系统方面展现出巨大潜力。本研究将FNO网络应用于三维（3D）城市风场的实时模拟。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模拟生成，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模型可准确重构瞬态空间速度场。我们进一步评估了训练后的FNO模型对不同风向未知数据的泛化能力，结果显示该模型能有效适应不同风向。更重要的是，FNO方法可在图形处理器上于毫秒级内完成预测，使得三维动态城市微气候的实时模拟成为可能。