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个人信息Personal Information
副教授
硕士生导师
性别:男
毕业院校:大连理工大学
学位:博士
所在单位:船舶工程学院
学科:船舶与海洋结构物设计制造
电子邮箱:likai@dlut.edu.cn
Structural Optimization of Hatch Cover Based on Bi-directional Evolutionary Structure Optimization and Surrogate Model Method
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论文类型:期刊论文
发表时间:2018-08-01
发表刊物:Journal of Shanghai Jiaotong University (Science)
卷号:23
期号:4
页面范围:538-549
ISSN号:10071172
摘要:Weight reduction has attracted much attention among ship designers and ship owners. In the present work, based on an improved bi-directional evolutionary structural optimization (BESO) method and surrogate model method, we propose a hybrid optimization method for the structural design optimization of beam-plate structures, which covers three optimization levels: dimension optimization, topology optimization and section optimization. The objective of the proposed optimization method is to minimize the weight of design object under a group of constraints. The kernel optimization procedure (KOP) uses BESO to obtain the optimal topology from a ground structure. To deal with beam-plate structures, the traditional BESO method is improved by using cubic box as the unit cell instead of solid unit to construct periodic lattice structure. In the first optimization level, a series of ground structures are generated based on different dimensional parameter combinations, the KOP is performed to all the ground structures, the response surface model of optimal objective values and dimension parameters is created, and then the optimal dimension parameters can be obtained. In the second optimization level, the optimal topology is obtained by using the KOP according to the optimal dimension parameters. In the third optimization level, response surface method (RSM) is used to determine the section parameters. The proposed method is applied to a hatch cover structure design. The locations and shapes of all the structural members are determined from an oversized ground structure. The results show that the proposed method leads to a greater weight saving, compared with the original design and genetic algorithm (GA) based optimization results. © 2018, Shanghai Jiaotong University and Springer-Verlag GmbH Germany, part of Springer Nature.