陶威^a, 刘钊^b, 许灿^a, 朱平^a()

a.上海交通大学 机械系统与振动国家重点实验室, 上海 200240
b.上海交通大学 设计学院, 上海 200240

收稿日期:2019-10-09出版日期:2021-05-28发布日期:2021-06-01
通讯作者:朱平E-mail:pzhu@sjtu.edu.cn
作者简介:陶 威(1990-),男,浙江省绍兴市人,博士生,主要研究方向为复合材料结构多尺度可靠性优化设计.
基金资助:国家自然科学基金项目(11772191);国家自然科学基金重点项目(U1864211)

Multi-Scale Reliability-Based Design Optimization of Three-Dimensional Orthogonal Woven Composite Fender

TAO Wei^a, LIU Zhao^b, XU Can^a, ZHU Ping^a()

a. State Key Laboratory of Mechanical System and Vibration, Shanghai 200240, China
b. School of Design, Shanghai Jiao Tong University, Shanghai 200240, China

Received:2019-10-09Online:2021-05-28Published:2021-06-01
Contact:ZHU Ping E-mail:pzhu@sjtu.edu.cn

摘要/Abstract

摘要： 三维正交机织复合材料具有优异的力学性能和抗分层能力,在汽车轻量化应用方面前景广阔. 以三维正交机织复合材料汽车翼子板为研究对象,基于多尺度仿真预测方法,建立复合材料弹性性能解析预测模型和翼子板宏观有限元模型. 同时针对材料和结构设计变量的不确定性,结合蒙特卡洛可靠性分析方法、Kriging代理模型和粒子群优化算法,实现复合材料翼子板多尺度可靠性优化设计. 结果表明:优化后的翼子板在满足结构刚度和可靠性要求的同时,达到了21.93%的轻量化效果.
关键词: 三维正交机织复合材料, 汽车翼子板, Kriging代理模型, 多尺度仿真, 可靠性优化设计
Abstract: Three-dimensional orthogonal woven composites have excellent mechanical properties and delamination resistance, which have a bright future in the application of automotive lightweight. A prediction model of elastic properties for three-dimensional orthogonal woven composites was established based on the analytical micromechanical method. The macro-scale performances of the fender were analyzed by using the finite element method. The Monte Carlo reliability analysis method, the Kriging surrogate mode, and the particle swarm optimization algorithm were adopted to conduct multi-scale reliability-based design optimization of a composite structure, which involves the uncertainties of material and structural design variables. The results show that the optimized fender meets the requirments of structural stiffness and reliability, and it also achieves a weight reduction of 21.93%.
Key words: three-dimensional orthogonal woven composite (3DOWC), automobile fender, Kriging surrogate model, multi-scale simulation, reliability-based design optimization (RBDO)


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