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中南大学学报(自然科学版)

Journal of Central South University

第43卷    第5期    总第213期    2012年5月

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文章编号:1672-7207(2012)05-1990-09
基于Kriging法的铁道车辆客室结构优化
谢素超1,周辉2

(1. 中南大学 交通运输工程学院 轨道交通安全教育部重点实验室,湖南 长沙,410075;
2. 中南林业科技大学 物流学院,湖南 长沙,410004
)

摘 要: 为解决铁道车辆客室空间尺寸和内部部件接触刚度优化配置问题,实现客室结构多目标多参数优化设计,以座椅-桌子结构和座椅-座椅结构2种模型为例,基于Kriging算法构造了目标参数(乘员头部性能指标CHI15,CHI36和胸部性能指标CT3ms)关于设计变量(小桌高度h、小桌与座椅距离l1、座椅间距l2、桌子接触刚度k1和座椅接触刚度k2)的代理模型,分别得到目标参数随设计参数变化的响应曲面,并采用遗传算法对建立的目标参数代理模型进行整体寻优,得到2种结构模型的最优参数配置。分析结果表明:各代理模型的遗传算法寻优结果与数值计算的模拟结果吻合较好,误差范围为-5.94%~2.23%,说明基于Kriging代理模型和遗传算法的优化结果是可靠的。研究结果可用于指导铁道车辆客室结构的布置。

 

关键字: 铁道车辆;客室结构;优化;Kriging法;遗传算法

Optimization on passenger compartment structure of railway vehicle based on Kriging method
XIE Su-chao1, ZHOU Hui2

1. Key Laboratory of Traffic Safety on Track of Ministry of Education,
School of Traffic & Transportation Engineering, Central South University, Changsha 410075, China;
2. School of Logistics, Central South University of Forestry and Technology, Changsha 410004, China

Abstract:In order to solve the optimization problem of compartment space dimensions and contact stiffness of inner parts of railway vehicle, and to achieve the multi-objective and multi-parameter compartment structure optimization design, the two models of seat-table structure and seat-seat structure were taken for example to set up the surrogate models between objective parameters (head injury criterion CHI15, CHI36 and thoracic cumulative-3ms injury criterion CT3ms) and design variables (table height h, distance l1 of table and chair, distance l2 of two chairs, table contact stiffness k1 and chair contact stiffness k2). Then the response surfaces between target parameters and design parameters were obtained respectively, and the optimal parameters of two structure models were obtained through the overall optimization of surrogate models by genetic algorithm (GA). The results indicate that the optimization results of surrogate models accord well with the numerical simulation ones, and their difference range is -5.94%-2.23%, which shows that the optimization results obtained by surrogate models and GA are reliable. The results might be helpful to guiding the structure design of railway vehicle compartment.

 

Key words: railway vehicle; passenger compartment; optimization; Kriging method (KM); genetic algorithm (GA)

中南大学学报(自然科学版)
  ISSN 1672-7207
CN 43-1426/N
ZDXZAC
中南大学学报(英文版)
  ISSN 2095-2899
CN 43-1516/TB
JCSTFT
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