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| 基于Python-Abaqus的自适应网格重划分算法实现及其应用 |
| Implementation and its application of adaptive remeshing algorithm based on Python-Abaqus |
| 投稿时间：2022-03-21 修订日期：2022-04-26 |
| DOI： |
| 中文关键词: Python脚本 二次开发 自适应网格重划 应力集中 数值模拟 |
| 英文关键词:Python script Secondary development Adaptive remeshing Stress concentration Numericial simulation |
| 基金项目:国家自然科学(10972191)和湖南省教育厅资助科研项目(19A502) |
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| 中文摘要: |
| 在有限元分析中,当选取了合适单元类型后,若所采用的网格尺寸太大则达不到计算精度要求、尺寸太小则往往需要非常庞大的单元数而导致求解自由度的迅速增长,利用自适应网格可以减轻计算精度与计算量的矛盾。本文采用基于后验误差估计的自适应网格重划算法,并结合Abaqus二次开发,编写了相应的自适应有限元Python脚本,从数值上分析了误差控制标准对计算结果的影响,实现了自适应求解全过程。通过将Python脚本应用于几类典型问题的有限元分析,数值验证了基于Abaqus网格重划技术的自适应方法对求解带奇性解问题的有效性。Python二次开发自适应计算与模拟,可绕过Abaqus/CAE的图形用户界面(GUI)直接对Abaqus内核进行操作,实现从几何建模、网格剖分到自适应求解的自动化处理,进而可方便多次修改模型和参数,提高建模效率。 |
| 英文摘要: |
| In the finite element analysis, if the mesh size is too large, it can’t satisfy the calculation accuracy once the appropriate element has been chosen, and if the mesh size is too small, it often needs a very large number of elements, which leads to the rapid growth of the degree of freedom. Using adaptive meshes can reduce the contradiction between the accuracy and the amount of calculation. In this paper, the resulting Python script of the adaptive finite element program is written combining with Abaqus software and the adaptive remeshing algorithm based on a posteriori error estimation. The influence of the selection of error control standards on the results is numerically analyzed, and the whole process of adaptive solution is realized. By applying the Python script to the finite element analysis of several typical problems, it is numerically verified the efficiency of the resulting adaptive method based on the Abaqus remeshing techniques for solving those problems with singular solutions. Secondary development of adaptive computation and simulation with Python script can bypass GUI of Abaqus/CAE and directly operate the Abaqus kernel. Thus, we can realize the automatic processing from geometric modeling, mesh generation to adaptive solution. Furthermore, it is convenient to repeatedly modify the models considered and the corresponding parameters, and the resulting method can significantly improve the efficiency of modeling in practice. |
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