A Validated LBM Dataset and Pipeline for Surrogate Modeling of Turbulent 3D Obstructed Channel Flows
This work provides a reproducible benchmark for evaluating neural operators in turbulent flow, addressing the need for validated datasets in computational fluid dynamics.
The paper presents a validated pipeline for generating training data for 3D turbulent channel flows at Re=1,000-10,000, using a lattice Boltzmann solver verified against experimental measurements. The pipeline enables standardized comparison of neural operators like Fourier Neural Operator and U-Net on forecasting, super-resolution, and error correction tasks.
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.