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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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    Continuum topology optimization with non-probabilistic reliability constraints based on multi-ellipsoid convex model

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    论文类型:期刊论文

    发表时间:2009-09-01

    发表刊物:STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION

    收录刊物:SCIE、EI、Scopus

    卷号:39

    期号:3

    页面范围:297-310

    ISSN号:1615-147X

    关键字:Topology optimization; Uncertainty; Non-probabilistic reliability; Convex model; Concerned performance approach

    摘要:Using a quantified measure for non-probab ilistic reliability based on the multi-ellipsoid convex model, the topology optimization of continuum structures in presence of uncertain-but-bounded parameters is investigated. The problem is formulated as a double-loop optimization one. The inner loop handles evaluation of the non-probabilistic reliability index, and the outer loop treats the optimum material distribution using the results from the inner loop for checking feasibility of the reliability constraints. For circumventing the numerical difficulties arising from its nested nature, the topology optimization problem with reliability constraints is reformulated into an equivalent one with constraints on the concerned performance. In this context, the adjoint variable schemes for sensitivity analysis with respect to uncertain variables as well as design variables are discussed. The structural optimization problem is then solved by a gradient-based algorithm using the obtained sensitivity. In the present formulation, the uncertain-but bounded uncertain variations of material properties, geometrical dimensions and loading conditions can be realistically accounted for. Numerical investigations illustrate the applicability and the validity of the present problem statement as well as the proposed numerical techniques. The computational results also reveal that non-probabilistic reliability-based topology optimization may yield more reasonable material layouts than conventional deterministic approaches. The proposed method can be regarded as an attractive supplement to the stochastic reliability-based topology optimization.