Introducing the sequential linear programming level-set method for topology optimization
Introducing the sequential linear programming level-set method for topology optimization
复制标题
DOI：
10.1007/s00158-014-1174-z
复制
发表时间：
2015-03
影响因子：
3.9
通讯作者：
Peter D. Dunning;H. A. Kim
中科院分区：
文献类型：
--
作者：
Peter D. Dunning;H. A. Kim
本文介绍了一种水平集拓扑优化方法，该方法可以处理多个约束条件，同时优化非水平集设计变量。该方法的主要特点是将边界积分离散化以估计函数的变化，并提出优化子问题以求得速度函数。该子问题采用顺序线性规划（SLP）求解，新方法称为顺序线性规划水平集法。新方法是在Hamilton-Jacobi型水平集方法的背景下发展起来的，其中形状导数被用来优化由隐式水平集函数表示的结构。这种方法有时被称为传统的水平集方法。SLP水平集方法通过包括体积、顺应性、特征值和位移约束以及非水平集设计变量的同时优化在内的一系列问题进行了演示。
This paper introduces an approach to level-set topology optimization that can handle multiple constraints and simultaneously optimize non-level-set design variables. The key features of the new method are discretized boundary integrals to estimate function changes and the formulation of an optimization sub-problem to attain the velocity function. The sub-problem is solved using sequential linear programming (SLP) and the new method is called the SLP level-set method. The new approach is developed in the context of the Hamilton-Jacobi type level-set method, where shape derivatives are employed to optimize a structure represented by an implicit level-set function. This approach is sometimes referred to as the conventional level-set method. The SLP level-set method is demonstrated via a range of problems that include volume, compliance, eigenvalue and displacement constraints and simultaneous optimization of non-level-set design variables.