Machine learning optimization strategy of shaped charge liner structure based on jet penetration efficiency  

作　　者：Ziqi Zhao Tong Li Donglin Sheng Jian Chen Amin Yan Yan Chen Haiying Wang Xiaowei Chen Lanhong Dai 

机构地区：[1]State Key Laboratory of Nonlinear Mechanics,Institute of Mechanics,Chinese Academy of Sciences,Beijing 100190,China [2]School of Future Technology,University of Chinese Academy of Sciences,Beijing 100049 China [3]School of Engineering Science,University of Chinese Academy of Sciences,Beijing 100049,China [4]State Key Laboratory of Explosion Science and Technology,Beijing Institute of Technology,Beijing 100081,China

出　　处：《Defence Technology（防务技术）》2024年第9期23-41,共19页Defence Technology

基　　金：supported by the NSFC Basic Science Center Program for"Multi-scale Problems in Nonlinear Mechanics" (Grant No.11988102);the NSFC (Grant Nos.U2141204,12172367);the Key Research Program of the Chinese Academy of Sciences (Grant No.ZDRW-CN-2021-2-3);the National Key Research and Development Program of China (Grant No.2022YFC3320504-02);the opening project of State Key Laboratory of Explosion Science and Technology (Grant No.KFJJ21-01 and No.KFJJ18-14 M)。

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.

关 键 词：Jet penetration efficiency Shaped charge liner FEM-ML XGBOOST MFO High-entropy alloy 

分 类 号：TJ410.34[兵器科学与技术—火炮、自动武器与弹药工程]