pyALDIC：具有GUI、自适应网格划分和掩码感知子集分割的增强拉格朗日数字图像相关的Python实现
pyALDIC: A Python Implementation of Augmented Lagrangian Digital Image Correlation with a GUI, Adaptive Meshing, and Mask-Aware Subset Splitting
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中文总结 AI 辅助
pyALDIC是用于全场位移和应变测量的增强拉格朗日数字图像相关的Python实现，结合图形界面与Python API，支持多种功能及加速分析，经多案例验证，以BSD-3-Clause许可分发，方便在多系统上可靠使用。
中文摘要 AI 辅助
pyALDIC是用于全场位移和应变测量的增强拉格朗日数字图像相关（AL-DIC）的开源Python实现。该软件将图形用户界面与可脚本化的Python API相结合，支持自适应四叉树网格划分、裂纹和孔洞附近的掩码感知子集分割以及可选的局部DIC和AL-DIC求解器模式。Numba加速实现高效分析，自动化测试、文档和可重现示例支持在Windows、macOS和Linux上可靠使用。验证案例包括合成位移场、刚体运动、I型裂纹、自适应细化和实验单轴拉伸。pyALDIC通过PyPI、GitHub和Zenodo以BSD-3-Clause许可分发以实现可重复性。pyALDIC可通过此https URL公开获取。
英文摘要
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.
发表机构
- The University of Texas at Austin（德克萨斯大学奥斯汀分校）
- Texas Materials Institute（德克萨斯材料研究所）
机构由 AI 辅助整理，请以论文原文为准。
