Novel distance-based masking and adaptive alpha-shape methods for CNN-ready reconstruction of arbitrary 2D CFD flow domains

TL;DR

This paper introduces two novel methods for reconstructing physically consistent masks of 2D CFD flow domains, significantly improving accuracy, stability, and computational efficiency over classical approaches, with practical tools for end-to-end workflow.

Contribution

It presents distance-based masking and adaptive alpha-shape strategies for better CFD domain reconstruction, including a new evaluation metric suite and a web application for practical use.

Findings

Distance-based method achieves 500-800x speedup over classical alpha-shapes.

Adaptive alpha-shape is stable with minimal tuning and faster than classical variants.

Post-processing improves mask retention with negligible unsupported regions.

Abstract

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.