A generative design method for structural topology optimization via transformable triangular mesh (TTM) algorithm
A generative design method for structural topology optimization via transformable triangular mesh (TTM) algorithm
复制标题
通过可变换三角网格（TTM）算法进行结构拓扑优化的生成设计方法
DOI：
10.1007/s00158-020-02544-0
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发表时间：
2020-03
影响因子：
3.9
通讯作者：
Jun Hong
中科院分区：
文献类型：
--
作者：
Baotong Li;Wenhao Tang;Senmao Ding;Jun Hong
本文提出了一种利用可变换三角形网格（TTM）算法对发热结构进行导热拓扑优化的方法。与传统的优化方法不同，该方法利用了一种特殊的变形算法，从亏格零曲面生成最优拓扑。该方法首先将初始几何体转化为三角形网格，并以半边数据结构存储。然后，网格操作（即，细分、分裂和细化）被用来激活几何形状以在底层有限元网格上移动、分裂和变形，使得可以通过优化三角形网格的位置和取向来实现传导拓扑。网格操作的独特之处在于分割，这使得几何体具有与初始几何体不同的面数、边数、顶点数，从而使得这些几何体之间具有不同的亏格数。该方法使得优化过程更灵活。最后，通过算例验证了TTM算法的有效性，并与常用的密度法进行了比较。
This article presents a way of optimizing the conduction topology for heat-generating structures by means of transformable triangular mesh (TTM) algorithm which is implemented in an explicit and geometrical way. Unlike the traditional optimization approaches, the proposed method capitalizes on the use of a special morphing algorithm to generate optimal topologies from a genus zero surface. In this method, the initial geometry is firstly converted into triangular mesh and stored as a half-edge data structure. Then, the mesh operations (i.e., subdivision, split, and refinement) are employed to activate the geometry to move, split, and deform upon the underlying finite element mesh so that the conduction topology can be achieved by optimizing the positions and orientations of the triangular grids. The unique feature of the mesh operation is the split, which makes the geometries have different number of faces, edges, vertices as the initial one, and therefore different genus number between these geometries. This method renders the optimization process more flexibility. Finally, some examples with verification results are presented to demonstrate that TTM algorithm is capable of proposing solutions having almost the same cooling effectiveness with less computing resources compared with the commonly used density approaches.
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DOI：
--
发表时间：
2011-09
期刊：
--
影响因子：
--
作者：
M. Bendsøe;O. Sigmund
通讯作者：
M. Bendsøe;O. Sigmund
基于移动可变形组件的框架下机器学习驱动的实时拓扑优化
影响因子：
2.6
作者：
Xin Lei;Chang Liu;Zongliang Du;Weisheng Zhang;Xu Guo
通讯作者：
Xu Guo
DOI：
10.1145/3596711.3596767
发表时间：
1992-07
期刊：
Seminal Graphics Papers: Pushing the Boundaries, Volume 2
影响因子：
--
作者：
Thaddeus Beier;Shawn Neely
通讯作者：
Thaddeus Beier;Shawn Neely
DOI：
10.1016/j.cma.2016.06.027
发表时间：
2016-10
影响因子：
7.2
作者：
Ying Zhou;Weihong Zhang;Jihong Zhu;Zhao Xu
通讯作者：
Ying Zhou;Weihong Zhang;Jihong Zhu;Zhao Xu
影响因子：
14.3
作者：
G. Rozvany;M. Bendsøe;U. Kirsch
通讯作者：
G. Rozvany;M. Bendsøe;U. Kirsch