构建可复现的湍流模拟数据集，助力神经网络预测三维障碍通道流。
A Validated LBM Dataset and Pipeline for Surrogate Modeling of Turbulent 3D Obstructed Channel Flows
- 用格子玻尔兹曼方法生成高精度流场数据，验证过实验结果。
- 在雷诺数1000至10000间，实现1024x512x512网格收敛性验证。
- 适合研究神经算子、湍流建模与高效仿真方案的学者参考。
评估三维湍流流动中的神经算子需要经过物理验证的数据集。本文提出一个可复现的生成管道，用于在雷诺数1,000至10,000范围内生成三维障碍通道流的训练数据。采用带有累积量碰撞算子的格子玻尔兹曼求解器，并通过实验测量（斯特劳哈尔数、阻力系数、湍流脉动）进行严格验证，结合全面的网格收敛性研究，分辨率达1024x512x512。基于已有框架，该验证过的管道支持标准化的代理模型对比。我们计划系统评估傅里叶神经算子与U-Net变体在预报、超分辨率及误差修正任务上的表现，使用物理信息指标评估湍流能量级联的表征能力。未来工作将比较数值求解器与神经代理的计算效率，探索实际应用潜力。诚邀社区就验证方法、基准设计与神经算子评估优先级提供反馈。
原文摘要 · Abstract (English)
Evaluating neural operators for 3D turbulent flow requires validated datasets with physical benchmarks. We present a reproducible pipeline generating training data for 3D channel flows around generated geometries at Re=1,000-10,000. Our lattice Boltzmann solver with cumulant collision operators is rigorously verified against experimental measurements (Strouhal number, drag coefficients, turbulent fluctuations) with comprehensive grid convergence studies at resolution 1024x512x512. Building upon an established framework, this validated pipeline enables standardized surrogate model comparison. We outline planned systematic evaluation of Fourier Neural Operator and U-Net variants on forecasting, super-resolution, and error correction tasks, using physics-informed metrics to assess turbulent energy cascade representation. Future work will compare computational efficiency between numerical solvers and neural surrogates, exploring practical application. We seek community feedback on our validation approach, planned benchmark methodology, and evaluation priorities for neural operators in turbulent flows.
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