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APPLICATION OF SURROGATE BASED PARTICLE SWARM OPTIMIZATION TO THE RELIABILITY-BASED ROBUST DESIGN OF COMPOSITE PRESSURE VESSELS

ISSN号：0894-9166
期刊名称：《固体力学学报：英文版》
时间：0
分类：TP301.6[自动化与计算机技术—计算机系统结构;自动化与计算机技术—计算机科学与技术] TB33[一般工业技术—材料科学与工程]
作者机构：[1]Department of Mechanics, Hubei Key Laboratory of Engineering Structural Analysis and Safety Assessment, Huazhong University of Science and Technology, Wuhan 430074, China
相关基金：Project supported by the Natural Science Foundation of China （No. 10772070） and National Basic Research Program of China （No. 2011CB013800）.

作者： Jianqiao Chen Yuanfu Tang Xiaoxu Huang[1]

关键词：纤维缠绕复合材料, 粒子群优化算法, 可靠性要求, 压力容器, 稳健设计, 应用, 有限元法分析, 设计变量, structural optimization, reliability based robust design, composite pressure vessel,surrogate based particle swarm optimization, sequential algorithm

中文摘要：

一个代理人联合为技术和粒子群优化建模的代理人的基于的粒子群优化(SBPSO ) 算法被用于合成压力容器的基于可靠性的柔韧的设计(RBRD ) 。SBPSO 的算法和效率通过数字例子被显示。为细丝创伤的一个模型有金属性的班机的合成压力容器然后被网分析学习，它的回答被使用有限元素方法分析(由软件 ANSYS 表现了) 。为最大化性能因素的一个优化问题被在圆柱的部分，金属班机的厚度和 drop off 区域尺寸选择螺旋状的厚度的弯屈的取向为设计变量提出。为合成的层和金属班机的力量限制被分别地使用 Tsai-Wu 失败标准和协定失败标准构造。方法建议了的数字例子表演能有效地解决 RBRD 问题，和建议模型的最佳的结果能满足某些可靠性要求并且有坚韧性到设计变量的变化。

英文摘要：

A surrogate based particle swarm optimization （SBPSO） algorithm which combines the surrogate modeling technique and particle swarm optimization is applied to the reliability- based robust design （RBRD） of composite pressure vessels. The algorithm and efficiency of SBPSO are displayed through numerical examples. A model for filament-wound composite pressure vessels with metallic liner is then studied by netting analysis and its responses are analyzed by using Finite element method （performed by software ANSYS）. An optimization problem for maximizing the performance factor is formulated by choosing the winding orientation of the helical plies in the cylindrical portion, the thickness of metal liner and the drop off region size as the design variables. Strength constraints for composite layers and the metal liner are constructed by using Tsai-Wu failure criterion and Mises failure criterion respectively. Numerical examples show that the method proposed can effectively solve the RBRD problem, and the optimal results of the proposed model can satisfy certain reliability requirement and have the robustness to the fluctuation of design variables.

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