Simulations of turbulent flows in 3D are one of the most expensive simulations in computational fluid dynamics (CFD). Many works have been written on surrogate models to replace numerical solvers for fluid flows with faster, learned, autoregressive models. However, the intricacies of turbulence in three dimensions necessitate training these models with very small time steps, while generating realistic flow states requires either long roll-outs with many steps and significant error accumulation or starting from a known, realistic flow state - something we aimed to avoid in the first place. Instead, we propose to approach turbulent flow simulation as a generative task directly learning the manifold of all possible turbulent flow states without relying on any initial flow state. For our experiments, we introduce a challenging 3D turbulence dataset of high-resolution flows and detailed vortex structures caused by various objects and derive two novel sample evaluation metrics for turbulent flows. On this dataset, we show that our generative model captures the distribution of turbulent flows caused by unseen objects and generates high-quality, realistic samples amenable for downstream applications without access to any initial state.

翻译：三维湍流模拟是计算流体力学（CFD）中最为昂贵的计算任务之一。已有大量研究采用替代模型——通过学习型自回归模型取代数值求解器，以实现更快速的流体模拟。然而，三维湍流的复杂性要求模型采用极小时间步长进行训练，而生成真实流场状态要么需要执行包含大量步长、误差显著累积的长程滚动预测，要么需从已知的真实流场状态出发——这恰恰是我们希望避免的。为此，我们提出将湍流模拟视为一项生成任务，直接学习所有可能湍流流场状态构成的流形，无需依赖任何初始流场状态。实验中，我们引入了一个具有挑战性的三维湍流数据集，该数据集包含高分辨率流场及由多种物体引发的精细涡旋结构，并针对湍流专门推导出两项新型样本评估指标。基于该数据集，我们的生成模型能够捕获由未见物体引发的湍流分布，无需任何初始状态即可生成质量上乘、真实感强的样本，适用于下游应用。