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

作　　者：Qingqing Chen Xinyu Zhang Zhiyong Wang Jie Zhang Zhihua Wang 

机构地区：[1]Institute of Applied Mechanics,College of Mechanical and Vehicle Engineering,Taiyuan University of Technology,Taiyuan 030024,China [2]Shanxi Key Lab.of Material Strength&structural Impact,College of Mechanical and Vehicle Engineering,Taiyuan 030024,China [3]Department of Civil and Environmental Engineering,National University of Singapore,1 Engineering Drive 2,117576,Singapore

出　　处：《Defence Technology（防务技术）》2024年第10期105-124,共20页Defence Technology

基　　金：supported by the National Natural Science Foundation of China(Grant Nos.12272257,12102292,12032006);the special fund for Science and Technology Innovation Teams of Shanxi Province(Nos.202204051002006).

摘　　要：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.

关 键 词：Data-driven dimensional analysis PENETRATION Semi-infinite metal target Dimensionless numbers Feature selection 

分 类 号：TJ410[兵器科学与技术—火炮、自动武器与弹药工程] O38[理学—流体力学]