改进的导重法求解拓扑优化问题及灰度过滤技术
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  • 英文篇名:Improved Guide-Weight Method on Solving Topology Optimization Problems and Gray-Scale Filtering Method
  • 作者:秦浩星 ; 安宗文 ; 孙道明
  • 英文作者:Qin Haoxing;An Zongwen;Sun Daoming;Department of Mechanical and Electronic Engineering, Lanzhou University of Technology;
  • 关键词:二分法 ; 灰度过滤 ; 导重法 ; RAMP模型 ; 拓扑优化
  • 英文关键词:bi-sectioning algorithm;;gray-scale filter;;guide-weight method;;RAMP scheme;;topology optimization
  • 中文刊名:JSJF
  • 英文刊名:Journal of Computer-Aided Design & Computer Graphics
  • 机构:兰州理工大学机电工程学院;
  • 出版日期:2015-10-15
  • 出版单位:计算机辅助设计与图形学学报
  • 年:2015
  • 期:v.27
  • 基金:国家自然科学基金(51265025);
  • 语种:中文;
  • 页:JSJF201510025
  • 页数:7
  • CN:10
  • ISSN:11-2925/TP
  • 分类号:199-205
摘要
为了提高导重法求解拓扑优化问题的计算效果,提出一种改进的导重法,并引入了灰度过滤技术抑制优化过程中灰度单元的产生.首先基于RAMP(the rational approximation of material properties)模型结合导重法求解最小柔度拓扑优化问题的迭代表达式,利用二分法对表达式中的拉格朗日乘子求法进行了改进;为减少优化后结构图像中的灰度单元数量,在迭代表达式中引入灰度过滤函数;最后将上述理论拓展到多工况拓扑优化问题中,采用归一化组合处理方法建立目标函数.对多工况拓扑优化问题的2个典型算例进行计算的结果表明,应用文中理论求解拓扑优化问题具有收敛稳定、求解快速、图像清晰的特点.
        An improved guide-weight method is presented to improve the computational efficiency of the guide-weight method on solving topology optimization problems, and the gray-filter method is introduced to restrain the gray-scale element which is generated in the optimization process. Since the optimization model was established based on RAMP(rational approximation of material properties, RAMP) scheme, and the guide-weight method was used as an algorithm combining for proposing the iteration formulas of the least compliance in multi objective topology optimization problems. First, the Lagrange multipliers solution method of the formulas were improved by the bi-sectioning algorithm. Then, with the purpose to suppress the generating of gray-scale element, the gray-scale filtering function was introduced into iterative formulas. Moreover, the proposed theory were extended to multi objective optimization problem, and ill-conditioning were avoided by normalizing single objective. Finally, two examples of multi objective optimizations problems were calculated respectively. The results calculated by examples demonstrate that using the proposed theory above can solve topology optimization problems with steady convergence, fast calculate speed and image sharpening.
引文
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