ChannelFlow-Tools: A Standardized Dataset Creation Pipeline for 3D Obstructed Channel Flows
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866914046838571008 |
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| author | Kavane, Shubham Kulkarni, Kajol Koestler, Harald |
| author_facet | Kavane, Shubham Kulkarni, Kajol Koestler, Harald |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2509_15236 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ChannelFlow-Tools: A Standardized Dataset Creation Pipeline for 3D Obstructed Channel Flows Kavane, Shubham Kulkarni, Kajol Koestler, Harald Graphics Artificial Intelligence 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. |
| title | ChannelFlow-Tools: A Standardized Dataset Creation Pipeline for 3D Obstructed Channel Flows |
| topic | Graphics Artificial Intelligence |
| url | https://arxiv.org/abs/2509.15236 |