适当正交分解的快速放气共轭梯度拓扑优化方法

IF 0.4 Q4 ENGINEERING, MULTIDISCIPLINARY
Kota Watanabe, Kaito Oshima
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引用次数: 0

摘要

. 讨论了一种利用适当正交分解(POD)的放气技术求解拓扑优化问题的快速线性方法。基于进化算法的拓扑优化方法需要大量的计算量。为此,为了降低有限元分析的成本,提出了一种放气预条件共轭梯度法(PCG)。通货紧缩技术将解决方案分解为快速和缓慢收敛的组件。慢速元件由于尺寸小,可以直接求解,计算成本低。因此,放气PCC方法可以提高PCG的收敛性。然而,放气的PCG需要找到慢速元件。本文介绍了一种带快照的POD方法。在优化过程中,对快照使用父节点对应的解向量。由快照构造出通货紧缩的正交向量。数值结果表明,该方法可以降低计算量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Fast deflated conjugate gradient method with proper orthogonal decomposition for topology optimization
. A fast linear solver for topology optimization using a deflation technique with Proper Orthogonal Decomposition (POD) is discussed. The topology optimization method based on evolutionary algorithms requires huge computational cost. In this reason, a deflated Preconditioned Conjugate Gradient (PCG) method is introduced so as to reduce the cost of finite element analysis. The deflation technique decomposes the solution into fast and slowly converging components. The slow components can be solved by direct methods with low computational cost due to small dimensions. Therefore, the deflated PCC method can improve the convergence of PCG. However, the deflated PCG requires to find the slow components. In this study, a POD method with snapshots is introduced. In the optimization process, solution vectors corresponding to parents are used for the snapshots. Orthogonal vectors for the deflation are constructed from the snapshots. Numerical results show that the present method can reduce the computational cost.
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