| Machine learning optimization strategy of shaped charge liner structure based on jet penetration efficiency | |
Zhao ZQ(赵子棋)1,2; Li T(李统)1; Sheng DL(盛冬林)1,3; Chen J(陈健)1 ; Yan AM(鄢阿敏)1; Chen Y(陈艳)1,3; Wang HY(汪海英)1,3; Chen, Xiaowei4; Dai LH(戴兰宏)1,2,3,4
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| Corresponding Author | Dai, Lanhong([email protected]) |
| Source Publication | DEFENCE TECHNOLOGY
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| 2024-09-01 | |
| Volume | 39Pages:23-41 |
| ISSN | 2096-3459 |
| Abstract | 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. |
| Keyword | Jet penetration efficiency Shaped charge liner FEM-ML XGBOOST MFO High-entropy alloy |
| DOI | 10.1016/j.dt.2024.04.006 |
| Indexed By | SCI ; EI |
| Language | 英语 |
| WOS ID | WOS:001318232700001 |
| WOS Keyword | LONG ROD ; PERFORMANCE ; COMPRESSIBILITY ; EXTENSION ; SEARCH ; DESIGN ; MODEL |
| WOS Research Area | Engineering |
| WOS Subject | Engineering, Multidisciplinary |
| Funding Project | 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] |
| Funding Organization | 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 |
| Classification | 二类/Q1 |
| Ranking | 1 |
| Contributor | Dai, Lanhong |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://dspace.imech.ac.cn/handle/311007/96761 |
| Collection | 非线性力学国家重点实验室 |
| Affiliation | 1.Chinese Acad Sci, Inst Mech, State Key Lab Nonlinear Mech, Beijing 100190, Peoples R China; 2.Univ Chinese Acad Sci, Sch Future Technol, Beijing 100049, Peoples R China; 3.Univ Chinese Acad Sci, Sch Engn Sci, Beijing 100049, Peoples R China; 4.Beijing Inst Technol, State Key Lab Explos Sci & Technol, Beijing 100081, Peoples R China |
| Recommended Citation 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.Rp_Au:Dai, Lanhong |
| 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. |
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