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.

翻译：评估用于三维湍流流动的神经算子需要具备物理基准的验证数据集。我们提出了一个可复现的流程，用于生成在雷诺数Re=1,000-10,000范围内、围绕生成几何结构的三维通道流动的训练数据。采用累积量碰撞算子的格子玻尔兹曼求解器，在1024x512x512分辨率下进行了全面的网格收敛性研究，并严格对照实验测量值（斯特劳哈尔数、阻力系数、湍流脉动）进行了验证。基于已有框架，该经过验证的流程能够实现标准化的代理模型比较。我们概述了计划中的系统性评估，包括傅里叶神经算子与U-Net变体在预测、超分辨率和误差修正任务上的表现，并使用基于物理的指标评估湍流能量级联表征。未来工作将比较数值求解器与神经代理模型之间的计算效率，探索实际应用。我们期待社区对我们的验证方法、计划中的基准测试方法论以及湍流流动中神经算子的评估优先级提出反馈。