Novel distance-based masking and adaptive alpha-shape methods for CNN-ready reconstruction of arbitrary 2D CFD flow domains
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866917277932191744 |
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| author | Sharifi, Mehran Larraona, Gorka S. Rivas, Alejandro |
| author_facet | Sharifi, Mehran Larraona, Gorka S. Rivas, Alejandro |
| contents | Interpolating scattered CFD datasets onto a uniform Cartesian grid can distort the true geometry, producing a convex-hull type envelope and activating nonphysical regions. This work presents a reconstruction framework that recovers physically consistent masks before exporting CNN-ready fields. It introduces two novel strategies, distance-based masking and an adaptive alpha-shape formulation that normalizes alpha using local data resolution, and evaluates them against classical alpha-shape boundary recovery. A quantitative, topology-aware metric suite is introduced to assess retention, suppression of unsupported regions, overlap consistency, and connectivity. The novel distance-based method is robust across the geometries considered under the same threshold rule, with tau set to the minimum CFD grid spacing, and achieves 500-800 times speedups over classical alpha-shapes. The adaptive alpha-shape remains stable when its control parameter is set to 1 and is 1.7-2.6 times faster than the classical variant, which requires geometry-specific alpha tuning. A lightweight boundary inflation post-process using a minimal dilation further improves retention by up to 2.96% with negligible unsupported activation (less than 0.08%). Overall, the distance-based method is recommended as the default due to its accuracy, stability, minimal tuning, and low cost, while the adaptive alpha-shape is a strong alternative when grid-spacing information for threshold selection is unavailable. A companion web application operationalizes the workflow end to end, enabling 2D ASCII dataset upload, parameter tuning, mask and boundary generation, and export of CNN-ready outputs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_15536 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Novel distance-based masking and adaptive alpha-shape methods for CNN-ready reconstruction of arbitrary 2D CFD flow domains Sharifi, Mehran Larraona, Gorka S. Rivas, Alejandro Fluid Dynamics Metric Geometry Computational Physics Interpolating scattered CFD datasets onto a uniform Cartesian grid can distort the true geometry, producing a convex-hull type envelope and activating nonphysical regions. This work presents a reconstruction framework that recovers physically consistent masks before exporting CNN-ready fields. It introduces two novel strategies, distance-based masking and an adaptive alpha-shape formulation that normalizes alpha using local data resolution, and evaluates them against classical alpha-shape boundary recovery. A quantitative, topology-aware metric suite is introduced to assess retention, suppression of unsupported regions, overlap consistency, and connectivity. The novel distance-based method is robust across the geometries considered under the same threshold rule, with tau set to the minimum CFD grid spacing, and achieves 500-800 times speedups over classical alpha-shapes. The adaptive alpha-shape remains stable when its control parameter is set to 1 and is 1.7-2.6 times faster than the classical variant, which requires geometry-specific alpha tuning. A lightweight boundary inflation post-process using a minimal dilation further improves retention by up to 2.96% with negligible unsupported activation (less than 0.08%). Overall, the distance-based method is recommended as the default due to its accuracy, stability, minimal tuning, and low cost, while the adaptive alpha-shape is a strong alternative when grid-spacing information for threshold selection is unavailable. A companion web application operationalizes the workflow end to end, enabling 2D ASCII dataset upload, parameter tuning, mask and boundary generation, and export of CNN-ready outputs. |
| title | Novel distance-based masking and adaptive alpha-shape methods for CNN-ready reconstruction of arbitrary 2D CFD flow domains |
| topic | Fluid Dynamics Metric Geometry Computational Physics |
| url | https://arxiv.org/abs/2602.15536 |