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    王博

    • 教授     博士生导师   硕士生导师
    • 主要任职:Deputy director of State Key Lab of Structural Analysis for Industrial Equipment
    • 其他任职:工业装备结构分析国家重点实验室副主任
    • 性别:男
    • 毕业院校:大连理工大学
    • 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
    • 办公地点:工程力学系系楼304房间
    • 联系方式:办公电话: 0411-84706608; 手机: 壹叁玖肆贰捌伍玖捌伍伍
    • 电子邮箱:wangbo@dlut.edu.cn

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    An integrated framework of exact modeling, isogeometric analysis and optimization for variable-stiffness composite panels

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    论文类型:期刊论文

    发表时间:2018-09-01

    发表刊物:COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

    收录刊物:SCIE

    卷号:339

    页面范围:205-238

    ISSN号:0045-7825

    关键字:Variable-stiffness panels; Buckling; Exact modeling; Isogeometric analysis; Analytical sensitivity; Optimization

    摘要:Isogeometric analysis (IGA) is particularly suitable for the prediction of buckling load and design optimization of variable-stiffness composite panels, since curvilinear fiber path can be described exactly to improve the analysis efficiency, moreover, analytical sensitivity can be derived to improve the optimization efficiency. In this study, an integrated framework of exact modeling, isogeometric analysis and optimization for variable-stiffness panels is developed for the global optimum. Due to the inherent feature of multiple local optima for this type of problems, a novel multi-start gradient-based strategy is developed to enhance the global optimization capacity, and multiple initial designs for gradient-based optimization are determined by space tailoring method, which can guarantee the convergence rate and efficiency. Once the constraint aggregation and parallel computing methods are employed, the computational efficiency will be further improved. For typical illustrative example, it can be demonstrated that the proposed method is able to provide a more efficient optimum design with significant less computational cost compared to other traditional methods, including FEA-based optimization, direction optimization using genetic algorithm, gradient-based optimization without K-S function, gradient-based optimization based on difference method. (C) 2018 Elsevier B.V. All rights reserved.