底部爆炸冲击下装甲车辆薄壁底板的等效加载方法

IF 5.9 Q1 ENGINEERING, MULTIDISCIPLINARY
Weiwei Qin , Jiahao He , Shaoyan Zhang , Tuzao Yao , Jing Tang , Xianhui Wang , Xiaowang Sun , Tao Wang , Qiang Zhou
{"title":"底部爆炸冲击下装甲车辆薄壁底板的等效加载方法","authors":"Weiwei Qin ,&nbsp;Jiahao He ,&nbsp;Shaoyan Zhang ,&nbsp;Tuzao Yao ,&nbsp;Jing Tang ,&nbsp;Xianhui Wang ,&nbsp;Xiaowang Sun ,&nbsp;Tao Wang ,&nbsp;Qiang Zhou","doi":"10.1016/j.dt.2025.06.010","DOIUrl":null,"url":null,"abstract":"<div><div>The high cost and low efficiency of full-scale vehicle experiments and numerical simulations limit the efficient development of armored vehicle occupant protection systems. The floor-occupant-seat local simulation model provides an alternative solution for quickly evaluating the performance of occupant protection systems. However, the error and rationality of the loading of the thin-walled floor in the local model cannot be ignored. This study proposed an equivalent loading method for the local model, which includes two parts: the dimensionality reduction method for acceleration matrix and the joint optimization framework for equivalent node coordinates. In the dimensionality reduction method, the dimension of the acceleration matrix was reduced based on the improved kernel principal component analysis (KPCA), and a dynamic variable bandwidth was introduced to address the limitation of failing to effectively measure the similarity between acceleration data in conventional KPCA. In addition, a least squares problem with forced displacement constraints was constructed to solve the correction matrix, thereby achieving the scale restoration process of the principal component acceleration matrix. The joint optimization framework for coordinates consists of the error assessment of response time histories (EARTH) and Bayesian optimization. In this framework, the local loading error of the equivalent acceleration matrix is taken as the Bayesian optimization objective, which is quantified and scored by EARTH. The expected improvement acquisition function was used to select the new set of the equivalent acceleration node coordinates for the self-updating optimization of the observation dataset and Gaussian process surrogate model. We reduced the dimension of the acceleration matrix from 2256 to 7, while retaining 91% of the information features. The comprehensive error score of occupant's lower limb response in the local model increased from 58.5% to 80.4%. The proposed equivalent loading method provides a solution for the rapid and reliable development of occupant protection systems.</div></div>","PeriodicalId":58209,"journal":{"name":"Defence Technology(防务技术)","volume":"52 ","pages":"Pages 184-206"},"PeriodicalIF":5.9000,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Equivalent loading method for the thin-walled floors of armored vehicles with bottom explosion impacts\",\"authors\":\"Weiwei Qin ,&nbsp;Jiahao He ,&nbsp;Shaoyan Zhang ,&nbsp;Tuzao Yao ,&nbsp;Jing Tang ,&nbsp;Xianhui Wang ,&nbsp;Xiaowang Sun ,&nbsp;Tao Wang ,&nbsp;Qiang Zhou\",\"doi\":\"10.1016/j.dt.2025.06.010\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>The high cost and low efficiency of full-scale vehicle experiments and numerical simulations limit the efficient development of armored vehicle occupant protection systems. The floor-occupant-seat local simulation model provides an alternative solution for quickly evaluating the performance of occupant protection systems. However, the error and rationality of the loading of the thin-walled floor in the local model cannot be ignored. This study proposed an equivalent loading method for the local model, which includes two parts: the dimensionality reduction method for acceleration matrix and the joint optimization framework for equivalent node coordinates. In the dimensionality reduction method, the dimension of the acceleration matrix was reduced based on the improved kernel principal component analysis (KPCA), and a dynamic variable bandwidth was introduced to address the limitation of failing to effectively measure the similarity between acceleration data in conventional KPCA. In addition, a least squares problem with forced displacement constraints was constructed to solve the correction matrix, thereby achieving the scale restoration process of the principal component acceleration matrix. The joint optimization framework for coordinates consists of the error assessment of response time histories (EARTH) and Bayesian optimization. In this framework, the local loading error of the equivalent acceleration matrix is taken as the Bayesian optimization objective, which is quantified and scored by EARTH. The expected improvement acquisition function was used to select the new set of the equivalent acceleration node coordinates for the self-updating optimization of the observation dataset and Gaussian process surrogate model. We reduced the dimension of the acceleration matrix from 2256 to 7, while retaining 91% of the information features. The comprehensive error score of occupant's lower limb response in the local model increased from 58.5% to 80.4%. The proposed equivalent loading method provides a solution for the rapid and reliable development of occupant protection systems.</div></div>\",\"PeriodicalId\":58209,\"journal\":{\"name\":\"Defence Technology(防务技术)\",\"volume\":\"52 \",\"pages\":\"Pages 184-206\"},\"PeriodicalIF\":5.9000,\"publicationDate\":\"2025-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Defence Technology(防务技术)\",\"FirstCategoryId\":\"1087\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2214914725001941\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Defence Technology(防务技术)","FirstCategoryId":"1087","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2214914725001941","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0

摘要

整车实验和数值模拟的高成本和低效率限制了装甲车辆乘员保护系统的高效开发。地板-乘员-座位局部仿真模型为快速评估乘员保护系统的性能提供了另一种解决方案。然而,薄壁楼盖局部模型加载的误差和合理性也不容忽视。本文提出了一种局部模型的等效加载方法,该方法包括加速度矩阵降维方法和等效节点坐标的联合优化框架两部分。在降维方法中,基于改进的核主成分分析(KPCA)对加速度矩阵进行降维,并引入动态可变带宽,解决了传统核主成分分析无法有效度量加速度数据之间相似度的局限性。此外,构造带强迫位移约束的最小二乘问题求解修正矩阵,从而实现主分量加速度矩阵的尺度恢复过程。该联合优化框架由响应时间历史误差评估(EARTH)和贝叶斯优化组成。该框架以等效加速度矩阵的局部加载误差为贝叶斯优化目标,利用EARTH对其进行量化和评分。利用期望改进获取函数选择新的等效加速节点坐标集,对观测数据集和高斯过程代理模型进行自更新优化。我们将加速度矩阵的维数从2256降到了7,同时保留了91%的信息特征。局部模型中乘员下肢反应综合误差评分由58.5%提高到80.4%。提出的等效加载方法为乘员保护系统的快速、可靠发展提供了一种解决方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Equivalent loading method for the thin-walled floors of armored vehicles with bottom explosion impacts
The high cost and low efficiency of full-scale vehicle experiments and numerical simulations limit the efficient development of armored vehicle occupant protection systems. The floor-occupant-seat local simulation model provides an alternative solution for quickly evaluating the performance of occupant protection systems. However, the error and rationality of the loading of the thin-walled floor in the local model cannot be ignored. This study proposed an equivalent loading method for the local model, which includes two parts: the dimensionality reduction method for acceleration matrix and the joint optimization framework for equivalent node coordinates. In the dimensionality reduction method, the dimension of the acceleration matrix was reduced based on the improved kernel principal component analysis (KPCA), and a dynamic variable bandwidth was introduced to address the limitation of failing to effectively measure the similarity between acceleration data in conventional KPCA. In addition, a least squares problem with forced displacement constraints was constructed to solve the correction matrix, thereby achieving the scale restoration process of the principal component acceleration matrix. The joint optimization framework for coordinates consists of the error assessment of response time histories (EARTH) and Bayesian optimization. In this framework, the local loading error of the equivalent acceleration matrix is taken as the Bayesian optimization objective, which is quantified and scored by EARTH. The expected improvement acquisition function was used to select the new set of the equivalent acceleration node coordinates for the self-updating optimization of the observation dataset and Gaussian process surrogate model. We reduced the dimension of the acceleration matrix from 2256 to 7, while retaining 91% of the information features. The comprehensive error score of occupant's lower limb response in the local model increased from 58.5% to 80.4%. The proposed equivalent loading method provides a solution for the rapid and reliable development of occupant protection systems.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Defence Technology(防务技术)
Defence Technology(防务技术) Mechanical Engineering, Control and Systems Engineering, Industrial and Manufacturing Engineering
CiteScore
8.70
自引率
0.00%
发文量
728
审稿时长
25 days
期刊介绍: Defence Technology, a peer reviewed journal, is published monthly and aims to become the best international academic exchange platform for the research related to defence technology. It publishes original research papers having direct bearing on defence, with a balanced coverage on analytical, experimental, numerical simulation and applied investigations. It covers various disciplines of science, technology and engineering.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信