发表机构
School of Computing and Information Technology, Great Bay University（大湾区大学计算机与信息技术学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对城市风预测中速度误差不保证质量守恒与壁面不可渗透的问题，提出嵌套势框架Sculpt，通过离散旋度参数化实现无散度且不可渗透的速度场，并引入UrbanWindFlow数据集验证精度与运动学可容许性。
AI 中文摘要
预测瞬态城市风对于理解城市微气候和设计气候韧性城市至关重要。解析建筑物的大涡模拟（LES）能够以高昂的计算成本为每种布局生成详细的不可压缩城市风场。神经代理模型通过学习预测速度场的演化，提供了一种更快的替代方案。然而，最小化速度预测误差并不能保证局部质量守恒和壁面不可渗透性，这两者共同定义了运动学可容许性。这一局限性源于无约束的输出表示：几何条件引导预测，但并未将其限制在可容许的速度场范围内。修正这些输出中的边界违规会改变相邻流体单元的通量平衡，并可能因此损害局部质量守恒。为解决这一挑战，我们提出了Sculpt，一个嵌套势函数框架，将耦合的、依赖于几何的约束直接构建到其参数化中。这种嵌套参数化通过原生三维交错网格上体积矢量势的离散旋度生成无散度速度更新。一个共享的标量势约束矢量势的边界值，使得同一算子无需逐步压力投影即可强制执行不可渗透性。由于通过该旋度的反向传播会衰减大尺度梯度信号，我们在多个分辨率下参数化体积势，以更好地捕捉大尺度流动结构。我们引入了UrbanWindFlow，一个涵盖城市形态和入流条件的LES数据集，以同时评估精度和运动学可容许性。
英文摘要
Predicting transient urban winds is fundamental to understanding urban microclimates and designing climate-resilient cities. Building-resolving large-eddy simulation produces detailed incompressible urban wind fields at substantial computational cost for each layout. Neural surrogates offer a faster alternative by learning to predict the evolution of velocity fields. However, minimizing velocity prediction error does not guarantee local mass conservation and wall impermeability, which together define kinematic admissibility. This limitation stems from an unconstrained output representation: geometry conditioning guides predictions but does not restrict them to admissible velocity fields. Correcting boundary violations in these outputs changes the flux balance in adjacent fluid cells and may consequently compromise local mass conservation. To address the challenge, we propose Sculpt, a nested potential framework that builds the coupled, geometry-dependent constraints directly into its parameterization. This nested parameterization generates divergence-free velocity updates through the discrete curl of a volume vector potential on the native three-dimensional staggered grid. A shared scalar potential constrains the vector potential's boundary values so that the same operator also enforces impermeability, without a per-step pressure projection. Because backpropagation through this curl attenuates large-scale gradient signals, we parameterize the volume potential at multiple resolutions to better capture large-scale flow structures. We introduce UrbanWindFlow, an LES dataset spanning urban morphologies and inflow conditions, to evaluate accuracy and kinematic admissibility together.
