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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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    Topology optimization of magnetorheological fluid layers in sandwich plates for semi-active vibration control

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    论文类型:期刊论文

    发表时间:2015-08-01

    发表刊物:SMART MATERIALS AND STRUCTURES

    收录刊物:SCIE、EI、Scopus

    卷号:24

    期号:8

    ISSN号:0964-1726

    关键字:topology optimization; magneto-rheological fluid; semi-active control; dynamic optimization; frequency-aggregated dynamic compliance

    摘要:This paper investigates topology optimization of the magnetorheological (MR) fluid layer in a sandwich plate for improving the semi-active vibration control performance. Therein, a uniform magnetic field is applied across the MR fluid layer to provide a semi-active damping control effect. In the optimization model, the pseudo-densities describing the MR fluid material distribution are taken as design variables, and an artificial magneto-rheological fluid model (AMRF) with penalization is proposed to suppress intermediate density values. For reducing the vibration level under harmonic excitations, the dynamic compliance under a specific excitation frequency, or the frequency-aggregated dynamic compliance in a given frequency band, is taken as the objective function to be minimized. In this context, the adjoint-variable sensitivity analysis scheme is derived. The effectiveness and efficiency of the proposed method are demonstrated by numerical examples, in which the structural dynamic performance can be remarkably improved through optimization. The influences of several key factors on the optimal designs are also explored. It is shown that the AMRF model is effective in yielding clear boundaries in the final optimal solutions without use of additional regularization techniques.