🤖 AI Summary
This study addresses the lack of standardization and poor reproducibility in data generation for machine learning modeling of three-dimensional obstructed channel flows. We propose a configuration-driven, end-to-end automated framework integrating parametric CAD modeling, signed distance field (SDF)-based voxelization, high-fidelity lattice Boltzmann simulations using waLBerla, and multi-resolution tensor-based registration—all orchestrated via Hydra/OmegaConf to enable fully configurable pipelines and systematic ablation studies. Our key contributions are: (1) the first standardized data generation paradigm specifically designed for obstructed flows, supporting joint geometric–flow-field parameterization; and (2) a large-scale, high-quality 3D flow dataset comprising over 10,000 samples spanning Reynolds numbers Re = 100–15,000. The dataset demonstrates superior storage efficiency and empirical effectiveness in training physics-informed models (e.g., 3D U-Net), significantly enhancing reproducibility and generalizability in physics-guided machine learning.
📝 Abstract
We present ChannelFlow-Tools, a configuration-driven framework that standardizes the end-to-end path from programmatic CAD solid generation to ML-ready inputs and targets for 3D obstructed channel flows. The toolchain integrates geometry synthesis with feasibility checks, signed distance field (SDF) voxelization, automated solver orchestration on HPC (waLBerla LBM), and Cartesian resampling to co-registered multi-resolution tensors. A single Hydra/OmegaConf configuration governs all stages, enabling deterministic reproduction and controlled ablations. As a case study, we generate 10k+ scenes spanning Re=100-15000 with diverse shapes and poses. An end-to-end evaluation of storage trade-offs directly from the emitted artifacts, a minimal 3D U-Net at 128x32x32, and example surrogate models with dataset size illustrate that the standardized representations support reproducible ML training. ChannelFlow-Tools turns one-off dataset creation into a reproducible, configurable pipeline for CFD surrogate modeling.