张晓鹏Xiaopeng Zhang

(副教授)

 博士生导师  硕士生导师
学位:博士
性别:男
毕业院校:大连理工大学
所在单位:力学与航空航天学院
电子邮箱:zhangxiaopeng@dlut.edu.cn

论文成果

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Robust topology optimization for dynamic compliance minimization under uncertain harmonic excitations with inhomogeneous eigenvalue analysis

发表时间:2019-03-12 点击次数:

论文名称:Robust topology optimization for dynamic compliance minimization under uncertain harmonic excitations with inhomogeneous eigenvalue analysis
论文类型:期刊论文
发表刊物:STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION
收录刊物:SCIE、EI、Scopus
卷号:54
期号:6,SI
页面范围:1469-1484
ISSN号:1615-147X
关键字:Robust topology optimization; Vibration; Load uncertainty; Dynamic compliance; Convex model; Inhomogeneous eigenvalue
摘要:Variability of load magnitude/direction is a most significant source of uncertainties in practical engineering. This paper investigates robust topology optimization of structures subjected to uncertain dynamic excitations. The unknown-but-bounded dynamic loads/accelerations are described with the non-probabilistic ellipsoid convex model. The aim of the optimization problem is to minimize the absolute dynamic compliance for the worst-case loading condition. For this purpose, a generalized compliance matrix is defined to construct the objective function. To find the optimal structural layout under uncertain dynamic excitations, we first formulate the robust topology optimization problem into a nested double-loop one. Here, the inner-loop aims to seek the worst-case combination of the excitations (which depends on the current design, and is usually to be found by a global optimization algorithm), and the outer-loop optimizes the structural topology under the found worst-case excitation. To tackle the inherent difficulties associated with such an originally nested formulation, we convert the inner-loop into an inhomogeneous eigenvalue problem using the optimality condition. Thus the double-loop problem is reformulated into an equivalent single-loop one. This formulation ensures that the strictsense worst-case combination of the uncertain excitations for each intermediate design be located without resorting to a time-consuming global search algorithm. The sensitivity analysis of the worst-case objective function value is derived with the adjoint variable method, and then the optimization problem is solved by a gradient-based mathematical programming method. Numerical examples are presented to illustrate the effectiveness and efficiency of the proposed framework.
发表时间:2016-12-01