.png){kind=icon}
返回
Design optimization of a runflat structure based on multi-objective genetic algorithm
.png){kind=icon}
PRE
AI
DOI：10.1007/s00158-014-1217-5.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Runflat structure plays an important role in determining the sustainable mileage after the tire is shot. Lightweight, stiffness and strength are highly relevant to the overall performance of the structure. A parameterized model was built based on the full study of the structure, and a new adaptive meshing method is proposed to ensure the quality of the model. The accuracy of the new model was verified by comparing to the traditional finite element model. The parameter study was carried out to investigate the response of the performance and mass. Multi-objective optimization model was established by applying optimal Latin square design method and response surface model approach. Non-dominated sorting genetic algorithm-II (NSGA-II) was applied to obtain the optimization design. The results indicate that the combination of parameterized model and multi-objective genetic algorithms successfully achieve the goal of multi-objective optimization for mass and displacement while ensuring the stress. Meanwhile, the optimal topology, shape and thickness optimization for the runflat structure have been achieved at the same time.
Keyword:
Parameterized model
Multi-objective optimization
Topology optimization
Design of experiment
Response surface model
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
4
论文数:
4.9K
被引数:
1.7W
机构
查看更多机构
引用论文
Fungal gold and firewood on the Tibetan plateau: examining access to diverse ecosystem provisioning services within a rural community.png)
Oryx
OAAI
OryxIF0
OAAI分享收藏NSGA-II algorithm for multi-objective generation expansion planning problem多目标发电扩展规划问题的nsga-ii算法
PREAI
分享收藏
A survey of structural and multidisciplinary continuum topology optimization: post 2000结构和多学科连续体拓扑优化的调查: 后2000
PREAI
分享收藏分享收藏分享收藏 没有更多内容