# ChannelFlow-Tools: A Configuration-Driven Pipeline for Generating Machine-Learning-Ready Datasets of 3D Obstructed Channel Flows

arXiv: https://arxiv.org/abs/2509.15236 · HTML: https://mlsanity.com/paper/2509.15236/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 48 references that are in the map.
- First posted on arXiv 17 Sep 2025.

## Builds on (most cited first, 19 of 19)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Fourier Neural Operator for Parametric Partial Differential Equations](https://mlsanity.com/paper/2010.08895/) (2020)
- [DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation](https://mlsanity.com/paper/1901.05103/) (2019)
- [DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operators](https://mlsanity.com/paper/1910.03193/) (2019)
- [Machine learning accelerated computational fluid dynamics](https://mlsanity.com/paper/2102.01010/) (2021)
- [PDEBENCH: An Extensive Benchmark for Scientific Machine Learning](https://mlsanity.com/paper/2210.07182/) (2022)
- [Geometry-Informed Neural Operator for Large-Scale 3D PDEs](https://mlsanity.com/paper/2309.00583/) (2023)
- [U-FNO -- An enhanced Fourier neural operator-based deep-learning model for multiphase flow](https://mlsanity.com/paper/2109.03697/) (2021)
- [The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning](https://mlsanity.com/paper/2412.00568/) (2024)
- [AirfRANS: High Fidelity Computational Fluid Dynamics Dataset for Approximating Reynolds-Averaged Navier-Stokes Solutions](https://mlsanity.com/paper/2212.07564/) (2022)
- [DrivAerNet++: A Large-Scale Multimodal Car Dataset with Computational Fluid Dynamics Simulations and Deep Learning Benchmarks](https://mlsanity.com/paper/2406.09624/) (2024)
- [APEBench: A Benchmark for Autoregressive Neural Emulators of PDEs](https://mlsanity.com/paper/2411.00180/) (2024)
- [CFDBench: A Large-Scale Benchmark for Machine Learning Methods in Fluid Dynamics](https://mlsanity.com/paper/2310.05963/) (2023)
- [AiiDA 1.0, a scalable computational infrastructure for automated reproducible workflows and data provenance](https://mlsanity.com/paper/2003.12476/) (2020)
- [DeepCFD: Efficient Steady-State Laminar Flow Approximation with Deep Convolutional Neural Networks](https://mlsanity.com/paper/2004.08826/) (2020)
- [AhmedML: High-Fidelity Computational Fluid Dynamics Dataset for Incompressible, Low-Speed Bluff Body Aerodynamics](https://mlsanity.com/paper/2407.20801/) (2024)
- [WindsorML: High-Fidelity Computational Fluid Dynamics Dataset For Automotive Aerodynamics](https://mlsanity.com/paper/2407.19320/) (2024)
- [Shape-informed surrogate models based on signed distance function domain encoding](https://mlsanity.com/paper/2409.12400/) (2024)
- [waLBerla: A block-structured high-performance framework for multiphysics simulations](https://mlsanity.com/paper/1909.13772/) (2019)

## Abstract (arXiv)

Data-driven surrogate models are increasingly used in computational fluid dynamics, and their reliability depends on the quality of the training data. These models are typically trained on fixed, pre-generated datasets. Systematic surrogate studies require controlled data generation, in which datasets can be regenerated, adapted, or extended to match specific research requirements. We introduce ChannelFlow-Tools, an open-source, configuration-driven pipeline for generating ML-ready datasets of three-dimensional obstructed channel flows. The pipeline integrates procedural obstacle geometry generation across six shape families, signed-distance-field (SDF) voxelisation, lattice-Boltzmann simulation, and packaging into ML-ready tensors. The workflow is driven by configuration files, with byte-identical reproducibility verified for the geometry-generation stage. The pipeline is evaluated through a full-corpus mesh-integrity audit, analytical and corpus-level validation of the SDF representation, canonical sphere-flow benchmarks for the solver, and a per-scene data-integrity audit. To demonstrate that the pipeline produces physically consistent and directly usable training data, three surrogate models (3D U-Net, FNO, and U-FNO) are trained on a sample dataset of 450 simulations spanning Rec ≈ 1000-10,000, generated entirely through the pipeline. The models learn the geometry-to-flow mapping and show physically interpretable behaviour on shape-family and Reynolds-number out-of-distribution splits, confirming direct downstream usability. ChannelFlow-Tools thus provides shared, auditable infrastructure for controlled benchmarking of geometry-aware CFD surrogates.