A Validated LBM Dataset and Pipeline for Surrogate Modeling of Turbulent 3D Obstructed Channel Flows

2026-06-15

AI Summary

This work addresses the scarcity of physically validated, high-quality three-dimensional turbulent flow data for training and evaluating neural operators. We develop a reproducible lattice Boltzmann method (LBM) data generation pipeline based on cumulant collision operators to simulate obstacle-induced turbulent channel flows across Reynolds numbers from 1,000 to 10,000, producing a high-fidelity dataset at a resolution of 1024×512×512. To our knowledge, this dataset is the first to include experimental validation and mesh convergence analysis, accompanied by a standardized benchmarking framework tailored for neural operators. Experimental comparisons demonstrate excellent agreement between LBM predictions and physical measurements in key quantities such as Strouhal number, drag coefficient, and turbulent fluctuations, enabling systematic evaluation of Fourier neural operators and U-Net variants in tasks including flow prediction, super-resolution, and error correction.

Abstract

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.

Problem

• turbulent flow
• neural operators
• surrogate modeling
• validated dataset
• 3D obstructed channel

Innovation

• Lattice Boltzmann Method
• Neural Operators
• Turbulent Flow
• Surrogate Modeling
• Physics-Informed Metrics