中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 力学研究所
  3. 中国科学院力学研究所
  4. 非线性力学国家重点实验室
Machine learning optimization strategy of shaped charge liner structure based on jet penetration efficiency

文献类型：期刊论文

作者Zhao ZQ(赵子棋)3,4; Li T(李统)4; Sheng DL(盛冬林)2,4; Chen J(陈健)4; Yan AM(鄢阿敏)4; Chen Y(陈艳)2,4; Wang HY(汪海英)2,4; Chen, Xiaowei1; Dai LH(戴兰宏)1,2,3,4
刊名DEFENCE TECHNOLOGY
出版日期2024-09-01
卷号39页码:23-41
关键词Jet penetration efficiency Shaped charge liner FEM-ML XGBOOST MFO High-entropy alloy
ISSN号2096-3459
DOI10.1016/j.dt.2024.04.006
通讯作者Dai, Lanhong(lhdai@lnm.imech.ac.cn)
英文摘要Shaped charge liner (SCL) has been extensively applied in oil recovery and defense industries. Achieving superior penetration capability through optimizing SCL structures presents a substantial challenge due to intricate rate-dependent processes involving detonation-driven liner collapse, high-speed jet stretching, and penetration. This study introduces an innovative optimization strategy for SCL structures that employs jet penetration efficiency as the primary objective function. The strategy combines experimentally validated finite element method with machine learning (FEM-ML). We propose a novel jet penetration efficiency index derived from enhanced cutoff velocity and shape characteristics of the jet via machine learning. This index effectively evaluates the jet penetration performance. Furthermore, a multi-model fusion based on a machine learning optimization method, called XGBOOST-MFO, is put forward to optimize SCL structure over a large input space. The strategy's feasibility is demonstrated through the optimization of copper SCL implemented via the FEM-ML strategy. Finally, this strategy is extended to optimize the structure of the recently emerging CrMnFeCoNi high-entropy alloy conical liners and hemispherical copper liners. Therefore, the strategy can provide helpful guidance for the engineering design of SCL. (c) 2024 China Ordnance Society. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd. This is an open access article under the CC BY-NC-ND license.
分类号二类/Q1
WOS关键词LONG ROD ; PERFORMANCE ; COMPRESSIBILITY ; EXTENSION ; SEARCH ; DESIGN ; MODEL
资助项目NSFC[U2141204] ; NSFC[12172367] ; Key Research Program of the Chinese Academy of Sciences[ZDRW-CN-2021-2-3] ; National Key Research and Development Program of China[2022YFC3320504-02] ; Opening project of State Key Laboratory of Explosion Science and Technology[KFJJ21-01] ; Opening project of State Key Laboratory of Explosion Science and Technology[KFJJ18-14 M]
WOS研究方向Engineering
语种英语
WOS记录号WOS:001318232700001
资助机构NSFC ; Key Research Program of the Chinese Academy of Sciences ; National Key Research and Development Program of China ; Opening project of State Key Laboratory of Explosion Science and Technology
其他责任者Dai, Lanhong
源URL[http://dspace.imech.ac.cn/handle/311007/96760]  
专题力学研究所_非线性力学国家重点实验室
作者单位1.Beijing Inst Technol, State Key Lab Explos Sci & Technol, Beijing 100081, Peoples R China
2.Univ Chinese Acad Sci, Sch Engn Sci, Beijing 100049, Peoples R China;
3.Univ Chinese Acad Sci, Sch Future Technol, Beijing 100049, Peoples R China;
4.Chinese Acad Sci, Inst Mech, State Key Lab Nonlinear Mech, Beijing 100190, Peoples R China;
推荐引用方式
GB/T 7714		 Zhao ZQ,Li T,Sheng DL,et al. Machine learning optimization strategy of shaped charge liner structure based on jet penetration efficiency[J]. DEFENCE TECHNOLOGY,2024,39:23-41.
APA 赵子棋.,李统.,盛冬林.,陈健.,鄢阿敏.,...&戴兰宏.(2024).Machine learning optimization strategy of shaped charge liner structure based on jet penetration efficiency.DEFENCE TECHNOLOGY,39,23-41.
MLA 赵子棋,et al."Machine learning optimization strategy of shaped charge liner structure based on jet penetration efficiency".DEFENCE TECHNOLOGY 39(2024):23-41.

入库方式： OAI收割

来源：力学研究所

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。