Crash energy management optimization of high-speed trains by machine learning methods

撞车 火车 汽车工程 能量(信号处理) 高能 工程类 计算机科学 数学 工程物理 地理 统计 地图学 程序设计语言
作者
Shaodong Zheng,Lin Jing,Kai Liu,Zhenhao Yu,Zhao Tang,Kaiyun Wang
出处
期刊:International Journal of Mechanical Sciences [Elsevier]
卷期号:270: 109108-109108 被引量:2
标识
DOI:10.1016/j.ijmecsci.2024.109108
摘要

With the increasing speed of railway vehicles, the intricacies inherent in train collision systems pose challenges in the rational allocation of energy during collision events. In this study, an efficient strategy for train crash energy management was proposed by integrating machine learning and the multi-objective optimization method. A 3-D finite element model of an eight-marshalling train was established and a train collision database was built by simulating train-to-train collisions. The machine learning methods were used to construct prediction models for the energy absorption in the head car's interface (Ea) and the standard deviation of the energy absorption in each intermediate car's interfaces (σ). The machine learning prediction model served as the fitness function for the multi-objective optimization algorithm, to achieve a maximum Ea and minimum σ based on the idea of collision energy management. The sample data of 340 groups were found to be sufficient to construct a machine learning model for energy absorption prediction, and the XGBoost was chosen to predict the collision energy absorption with R2 of 0.923 for Ea and 0.927 for σ, respectively. The optimal alternative of train crash energy management was obtained (i.e., F1 = 1787.69 kN, F2 = 2881.38 kN, F3 = 1596.43 kN, F4 = 1353.44 kN, F5 = 1765.68 kN, and F6 = 1200.64 kN), compared to the traditional configuration of the equivalent values (i.e., 1500 kN). The optimized Ea increased by 10.51% and σ decreased by 12.59%, and the main energy absorption interfaces of the intermediate cars changed from the original 6 to 8 interfaces. The optimized train displayed better crashworthiness performance in terms of instantaneous acceleration, living space, and peak interfacial forces. These findings demonstrated that the proposed approach was effective in optimizing train crash energy management.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
大幅提高文件上传限制,最高150M (2024-4-1)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
Dr发布了新的文献求助10
1秒前
2秒前
小蘑菇应助崔崔采纳,获得10
3秒前
3秒前
3秒前
Hhhhhhh发布了新的文献求助10
4秒前
。。。发布了新的文献求助20
4秒前
kp发布了新的文献求助10
5秒前
青衣发布了新的文献求助10
5秒前
5秒前
ding应助筱煜采纳,获得10
6秒前
6秒前
德鲁梦雨发布了新的文献求助10
6秒前
zhzhzh发布了新的文献求助10
6秒前
来一杯纯牛奶完成签到,获得积分10
7秒前
zfcvdavdf发布了新的文献求助10
7秒前
zzz发布了新的文献求助10
9秒前
9秒前
烟花应助玲玲采纳,获得10
9秒前
9秒前
阿文发布了新的文献求助10
9秒前
10秒前
12秒前
神勇的青旋应助zfcvdavdf采纳,获得10
14秒前
kk子发布了新的文献求助10
15秒前
雪子发布了新的文献求助10
16秒前
17秒前
笑点低小蚂蚁完成签到,获得积分10
17秒前
20秒前
英俊的芙蓉完成签到 ,获得积分10
20秒前
幻想Cloudy完成签到 ,获得积分0
21秒前
晏啊完成签到,获得积分10
22秒前
爆米花应助科研通管家采纳,获得10
23秒前
yhchow0204应助科研通管家采纳,获得10
24秒前
fifteen应助科研通管家采纳,获得10
24秒前
orixero应助科研通管家采纳,获得10
24秒前
24秒前
Akim应助科研通管家采纳,获得10
24秒前
yhchow0204应助科研通管家采纳,获得10
24秒前
酷波er应助科研通管家采纳,获得10
24秒前
高分求助中
歯科矯正学 第7版(或第5版) 1004
Smart but Scattered: The Revolutionary Executive Skills Approach to Helping Kids Reach Their Potential (第二版) 1000
Semiconductor Process Reliability in Practice 720
GROUP-THEORY AND POLARIZATION ALGEBRA 500
Mesopotamian divination texts : conversing with the gods : sources from the first millennium BCE 500
Days of Transition. The Parsi Death Rituals(2011) 500
The Heath Anthology of American Literature: Early Nineteenth Century 1800 - 1865 Vol. B 500
热门求助领域 (近24小时)
化学 医学 生物 材料科学 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 基因 遗传学 催化作用 物理化学 免疫学 量子力学 细胞生物学
热门帖子
关注 科研通微信公众号,转发送积分 3228715
求助须知:如何正确求助?哪些是违规求助? 2876473
关于积分的说明 8195167
捐赠科研通 2543670
什么是DOI,文献DOI怎么找? 1373912
科研通“疑难数据库(出版商)”最低求助积分说明 646868
邀请新用户注册赠送积分活动 621453