.png){kind=icon}
返回
A hierarchical spline based isogeometric topology optimization using moving morphable components
.png){kind=icon}
PRE
AI
DOI：10.1016/j.cma.2019.112696.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This paper presents a hierarchical spline based isogeometric topology optimization using moving morphable components (HITO-MMC). In this work, the adaptive isogeometric analysis implemented by hierarchical B-spline is adopted to efficiently and accurately assess the structural performance. An ersatz material model is derived from the Gaussian points of the hierarchy computational mesh to relate the geometric design variables with the objective and constraint of TO. To determine the iterative steps performing local refinement and the elements to be locally refined, a mark strategy is put forward based on the relative error of objective function and the value of topological description function (TDF). The mathematic model of HITO-MMC is reformulated by mapping the global displacement vector into the local displacement vector of the active elements on each level of the hierarchy computational mesh. The proposed HITO-MMC approach has twofold merits: (a) the optimization can be started from a relative coarse computational mesh and the mesh is locally refined during the course of TO, which result into a highly computational efficiency; (b) more accurate results are obtained due to the use of high continuous hierarchical basis functions and denser mesh on structural boundary. Besides, continuous refinement is more effective to generate the optimal design than fixed NURBS mesh, and hierarchical local refinement is superior to continuous global refinement. The effectiveness of the proposed HITO-MMC is validated by a series of 2D and 3D numerical benchmarks. (C) 2019 Elsevier B.Y. All rights reserved.
Keyword:
Topology optimization
IGA
Moving morphable components
Hierarchical local refinement
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
7.3
论文数:
1.3W
被引数:
5.6W
机构
引用论文
.png){kind=icon}
分享收藏A subdivision-based implementation of the hierarchical b-spline finite element method基于细分的分层b样条有限元方法的实现
PREAI
分享收藏Eigenvalue topology optimization of structures using a parameterized level set method使用参数化水平集方法对结构进行特征值拓扑优化
PREAI
分享收藏SURF: A connectivity-based space filling curve construction algorithm in high genus 3D surface WSNsSURF: 一种基于连通性的高属三维曲面WSNs空间填充曲线构建算法
PREAI
分享收藏
查看更多内容