Data-driven prediction of dimensionless quantities for semi-infinite target penetration by integrating machine-learning and feature selection methods

IF 5 Q1 ENGINEERING, MULTIDISCIPLINARY Defence Technology(防务技术) Pub Date : 2024-10-01 DOI:10.1016/j.dt.2024.04.012
{"title":"Data-driven prediction of dimensionless quantities for semi-infinite target penetration by integrating machine-learning and feature selection methods","authors":"","doi":"10.1016/j.dt.2024.04.012","DOIUrl":null,"url":null,"abstract":"<div><div>This study employs a data-driven methodology that embeds the principle of dimensional invariance into an artificial neural network to automatically identify dominant dimensionless quantities in the penetration of rod projectiles into semi-infinite metal targets from experimental measurements. The derived mathematical expressions of dimensionless quantities are simplified by the examination of the exponent matrix and coupling relationships between feature variables. As a physics-based dimension reduction methodology, this way reduces high-dimensional parameter spaces to descriptions involving only a few physically interpretable dimensionless quantities in penetrating cases. Then the relative importance of various dimensionless feature variables on the penetration efficiencies for four impacting conditions is evaluated through feature selection engineering. The results indicate that the selected critical dimensionless feature variables by this synergistic method, without referring to the complex theoretical equations and aiding in the detailed knowledge of penetration mechanics, are in accordance with those reported in the reference. Lastly, the determined dimensionless quantities can be efficiently applied to conduct semi-empirical analysis for the specific penetrating case, and the reliability of regression functions is validated.</div></div>","PeriodicalId":58209,"journal":{"name":"Defence Technology(防务技术)","volume":null,"pages":null},"PeriodicalIF":5.0000,"publicationDate":"2024-10-01","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/S2214914724000898","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

Abstract

This study employs a data-driven methodology that embeds the principle of dimensional invariance into an artificial neural network to automatically identify dominant dimensionless quantities in the penetration of rod projectiles into semi-infinite metal targets from experimental measurements. The derived mathematical expressions of dimensionless quantities are simplified by the examination of the exponent matrix and coupling relationships between feature variables. As a physics-based dimension reduction methodology, this way reduces high-dimensional parameter spaces to descriptions involving only a few physically interpretable dimensionless quantities in penetrating cases. Then the relative importance of various dimensionless feature variables on the penetration efficiencies for four impacting conditions is evaluated through feature selection engineering. The results indicate that the selected critical dimensionless feature variables by this synergistic method, without referring to the complex theoretical equations and aiding in the detailed knowledge of penetration mechanics, are in accordance with those reported in the reference. Lastly, the determined dimensionless quantities can be efficiently applied to conduct semi-empirical analysis for the specific penetrating case, and the reliability of regression functions is validated.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
通过整合机器学习和特征选择方法,以数据驱动预测半无限目标穿透的无量纲数量
本研究采用数据驱动方法,将尺寸不变性原理嵌入人工神经网络,从实验测量结果中自动识别棒状射弹穿透半无限金属目标时的主要无量纲量。通过检查指数矩阵和特征变量之间的耦合关系,简化了无量纲量的数学表达式。作为一种基于物理学的维度缩减方法,这种方法将高维参数空间缩减为在穿透情况下只涉及几个物理上可解释的无量纲量的描述。然后,通过特征选择工程评估了四种撞击条件下各种无量纲特征变量对穿透效率的相对重要性。结果表明,通过这种协同方法,无需参考复杂的理论方程,也无需借助详细的穿透力学知识,所选择的关键无量纲特征变量与参考文献中的变量一致。最后,确定的无量纲量可以有效地应用于对特定穿透情况进行半经验分析,并验证了回归函数的可靠性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 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.
期刊最新文献
IFC - Editorial Board Blast injury risks to humans within a military trench Properties of high cis-1,4 content hydroxyl-terminated polybutadiene and its application in composite solid propellants Design and evaluation of a kind of polymer-bonded explosives with improved mechanical sensitivity and thermal properties Comparative impact behaviours of ultra high performance concrete columns reinforced with polypropylene vs steel fibres
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1