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 em-ploys 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.
基金项目
NSFC Basic Science Center Program(11988102)
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)
维普
万方数据