发表机构
Concordia University; McGill University（康考迪亚大学; 麦吉尔大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出基于条件流匹配的生成框架，以建筑几何和平均流为引导，快速生成三维瞬时城市风温场，精度高且计算成本低，支持韧性城市设计。
AI 中文摘要
快速准确地预测城市风场和温度场对于城市微气候设计和气候适应至关重要。大涡模拟（LES）能够有效解析这些瞬时场，但由于计算成本高昂，其在城市微气候应用的迭代设计中受到限制。现有的回归型数据驱动模型能够提供快速输出，但仅产生确定性的点预测，本质上无法表征湍流的随机性。本文采用了一种新颖的条件流匹配（CFM）生成框架，以建筑几何和平均流作为引导，在数秒内生成合理的城市微气候三维瞬时速度和温度场。为克服像素空间三维生成的GPU内存瓶颈，该模型通过共享噪声初始化在重叠像素空间上并行运行，从而在整个域内保持流动结构的高空间连续性。与参考LES数据相比，CFM替代模型能够快速准确地恢复一阶统计量，风速和温度的归一化均方根误差（NRMSE）分别为2.99%和1.77%；二阶湍流统计量，风速和温度的NRMSE分别为7.17%和8.84%；湍流动能的NRMSE为7%；以及代表性位置的概率密度函数和垂直剖面。局部阵风预测的风工程应用表明，CFM的速度和准确性支持使用生成式AI使具有湍流感知能力的韧性城市设计和气候适应在计算上更加可行。
英文摘要
Rapid and accurate prediction of urban wind and temperature fields is important for urban microclimate design and climate adaptation. Large-eddy simulation (LES) effectively resolves these instantaneous fields, but its application is limited in iterative design of urban microclimate applications due to high computational cost. Existing regressive data-driven models offers quick outputs, but they produce only deterministic point predictions that inherently fail to represent turbulent stochasticity. This paper adopts a novel generative framework of Conditional Flow Matching (CFM) that uses building geometry and mean flow as guidance to generate plausible three-dimensional instantaneous velocity and temperature fields for urban microclimate in seconds. To overcome the GPU memory bottleneck of pixel space 3D generation, the model operates in parallel on overlapping pixel space through a shared-noise initialization that preserves high spatial continuity of flow structure across the entire domain. Against reference LES data, the CFM surrogate can rapidly and accurately restore the first-order statistics with Normalized Root Mean Square Error (NRMSE) of 2.99% for wind and 1.77% for temperature, second-order turbulence metrics with NRMSE of 7.17% for wind and 8.84% for temperature, turbulent kinetic energy with NRMSE of 7%, probability density function and vertical profiles in representative locations. Wind engineering application of local gust prediction demonstrate that the speed and accuracy of CFM, supporting the use of generative AI for making turbulence-aware resilient urban design and climate adaptation more computationally feasible.
