Zhang Yongjie, Bajaj Chandrajit, Sohn Bong-Soo
Institute for Computational Engineering and Sciences, Department of Computer Sciences, The University of Texas at Austin.
Comput Methods Appl Mech Eng. 2005 Nov 15;194(48-49):5083-5106. doi: 10.1016/j.cma.2004.11.026.
This paper describes an algorithm to extract adaptive and quality 3D meshes directly from volumetric imaging data. The extracted tetrahedral and hexahedral meshes are extensively used in the Finite Element Method (FEM). A top-down octree subdivision coupled with the dual contouring method is used to rapidly extract adaptive 3D finite element meshes with correct topology from volumetric imaging data. The edge contraction and smoothing methods are used to improve the mesh quality. The main contribution is extending the dual contouring method to crack-free interval volume 3D meshing with feature sensitive adaptation. Compared to other tetrahedral extraction methods from imaging data, our method generates adaptive and quality 3D meshes without introducing any hanging nodes. The algorithm has been successfully applied to constructing the geometric model of a biomolecule in finite element calculations.
本文描述了一种直接从容积成像数据中提取自适应且高质量三维网格的算法。提取的四面体和六面体网格在有限元方法(FEM)中得到广泛应用。采用自上而下的八叉树细分与对偶轮廓法相结合,从容积成像数据中快速提取具有正确拓扑结构的自适应三维有限元网格。使用边收缩和平滑方法来提高网格质量。主要贡献在于将对偶轮廓法扩展到具有特征敏感自适应的无裂纹区间体积三维网格划分。与其他从成像数据中提取四面体的方法相比,我们的方法生成的自适应且高质量三维网格不会引入任何悬挂节点。该算法已成功应用于有限元计算中构建生物分子的几何模型。