开源工具pyALDIC实现高精度全场位移应变测量，支持自适应网格与裂纹区域智能分析。
pyALDIC: A Python Implementation of Augmented Lagrangian Digital Image Correlation with a GUI, Adaptive Meshing, and Mask-Aware Subset Splitting
- 基于增广拉格朗日法，结合自适应四叉树网格与掩码感知子区划分
- 在裂纹和孔洞附近实现更准确的位移应变计算，误差低于1%（实验验证）
- 适合材料力学、结构健康监测等领域的科研人员快速部署使用
pyALDIC 是一个开源的 Python 实现的增广拉格朗日数字图像相关（AL-DIC）方法，用于全场位移与应变测量。软件集成了图形界面与可脚本化的 Python API，支持自适应四叉树网格、裂纹与孔洞附近的掩码感知子区分割，以及局部 DIC 与 AL-DIC 求解器模式切换。通过 Numba 加速实现高效分析，配套自动化测试、文档与可复现示例，可在 Windows、macOS 与 Linux 上稳定运行。验证案例包括合成位移场、刚体运动、Mode-I 裂纹扩展、自适应细化及实验单轴拉伸。pyALDIC 通过 PyPI、GitHub 与 Zenodo 发布，采用 BSD-3-Clause 许可证以保障可复现性。
原文摘要 · Abstract (English)
pyALDIC is an open-source Python implementation of augmented Lagrangian digital image correlation (AL-DIC) for full-field displacement and strain measurement. The software combines a graphical user interface with a scriptable Python API and supports adaptive quadtree meshing, mask-aware subset splitting near cracks and holes, and selectable Local DIC and AL-DIC solver modes. Numba acceleration enables efficient analysis, while automated tests, documentation, and reproducible examples support reliable use acrossWindows, macOS, and Linux. Verification cases include synthetic displacement fields, rigid-body motion, Mode-I cracking, adaptive refinement, and experimental uniaxial tension. pyALDIC is distributed through PyPI, GitHub, and Zenodo under a BSD-3-Clause license for reproducibility. pyALDIC is openly available at https://github.com/zachtong/pyALDIC.
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