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    亢战

    • 教授     博士生导师   硕士生导师
    • 主要任职:Deputy Dean, Faculty of Vehicle Engineering and Mechanics
    • 其他任职:Deputy Dean, Faculty of Vehicle Engineering and Mechanics
    • 性别:男
    • 毕业院校:stuttgart大学
    • 学位:博士
    • 所在单位:力学与航空航天学院
    • 学科:工程力学. 计算力学. 航空航天力学与工程. 固体力学
    • 办公地点:综合实验一号楼522房间
      https://orcid.org/0000-0001-6652-7831
      http://www.ideasdut.com
      https://scholar.google.com/citations?user=PwlauJAAAAAJ&hl=zh-CN&oi=ao
    • 联系方式:zhankang#dlut.edu.cn 84706067
    • 电子邮箱:zhankang@dlut.edu.cn

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    A method using successive iteration of analysis and design for large-scale topology optimization considering eigenfrequencies

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    论文类型:期刊论文

    发表时间:2020-04-15

    发表刊物:COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

    收录刊物:EI、SCIE

    卷号:362

    期号:,SI

    ISSN号:0045-7825

    关键字:Topology optimization; Large scale; Natural frequency; Eigenvalue; Successive iteration of analysis and design

    摘要:Repeatedly solving the generalized eigenvalue problems by far dominates the computational cost in large-scale topology optimization involving natural frequency constraints. This study proposes a method for dynamic topology optimization problems considering natural frequencies using successively executed iterations for the structural analysis and design. By using the Rayleigh quotients as approximations of the natural frequencies and achieving sequential approximation of the eigenpairs through inverse iteration-like procedures to improve the eigenvectors along with the topological evolution of the structure, the method avoids solving the time-consuming eigenvalue problem in each design iteration. This makes the method particularly suitable for large-scale frequency-constrained topology optimization problems. The convergence property of the method is analyzed under the assumption of sufficiently small design changes between two successive design iterations. Numerical examples regarding frequency and frequency gap constraints show that this method is able to realize concurrent convergence of the eigenvalue analysis and design optimization, and is more efficient than the conventional double-loop approach. (C) 2020 Elsevier B.V. All rights reserved.