A Validated LBM Dataset and Pipeline for Surrogate Modeling of Turbulent 3D Obstructed Channel Flows
arXiv:2606.16765v1 Announce Type: new 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 en
The increasing sophistication of neural operators for complex physical systems necessitates high-quality, validated datasets for training, which this research provides. Advancements in computational fluid dynamics and AI are converging to enable more accurate and efficient simulation and modeling.
This development significantly enhances the ability to create robust AI models for turbulent fluid dynamics, critical for engineering, design, and scientific discovery across numerous industries. It accelerates the deployment of AI-driven simulation tools, reducing reliance on costly and time-consuming physical experiments.
The availability of a rigorously validated dataset and pipeline for 3D turbulent flows will improve the accuracy and reliability of AI surrogate models, leading to faster and more efficient design cycles and predictions in fields like aerospace, energy, and materials science. It moves AI from conceptual application to practical, verifiable engineering solutions.
- · Aerospace Industry
- · Energy Sector
- · AI/ML Researchers
- · Computational Fluid Dynamics Engineers
- · Traditional CFD Software Manufacturers (without AI integration)
- · Expensive Physical Experimentation Setups
More accurate and faster AI-driven simulations for complex fluid dynamics become feasible for industrial applications.
Reduced design and testing cycles lead to accelerated innovation and cost savings in sectors heavily reliant on fluid dynamics.
The methodology could be extended to other complex physical systems (e.g., thermodynamics, material science), broadly accelerating scientific discovery and engineering R&D.
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