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Prediction of penetration depth of earth penetrator based on neural network
Zhuo, Chen1; Huixiang, Sun1; Yingwu, Wang2; Huan, Niu1
2019-06-10
会议名称2019 3rd International Workshop on Renewable Energy and Development, IWRED 2019
会议录名称2019 3rd International Workshop on Renewable Energy and Development, IWRED 2019 - Energy Efficient Systems and Optimization Theory
卷号267
期号3
会议日期2019-03-08
会议地点Guangzhou, China
出版者Institute of Physics Publishing
产权排序2
摘要

An artificial intelligence neural network model is established in this essay to seek a more general method for predicting penetration depth of earth penetrator, to comprehensively analyze the effect of various parameters on penetration depth as well as to predict the penetration depth of earth penetrator.This paper, by means of numerical simulation, and determined the ordnance penetrator warhead curvature radius, the length of the projectile, the density of the projectile,the density of the target protective layer, the elastic modulus of the target protective layer and the hit velocity of the earth penetrator.This six key parameters as the input data of neural network model, and by using numerical simulation to obtain the data needed for training the neural network model samples. According to the characteristics of six input data and one output data of the neural network model, the possible structure of the neural network model is set, and the optimal model structure is selected through training. We built neural network model to forecast the ordnance penetrator penetration depth, analyzes the six key parameter's influence on the depth of penetration, the results show that reducing the warhead curvature radius, increasing the length and density of the projectile, properly increasing the impact velocity of the projectile can improve the penetration ability of the earth penetrating projectile, and increasing the density and elastic modulus of the target protective layer can improve the anti-penetration ability of the protective layer. © Published under licence by IOP Publishing Ltd.

作者部门条件保障处
DOI10.1088/1755-1315/267/3/032004
收录类别EI ; CPCI
语种英语
ISSN号17551307;17551315
WOS记录号WOS:000495369900049
EI入藏号20192607097436
引用统计
文献类型会议论文
条目标识符http://ir.opt.ac.cn/handle/181661/31567
专题条件保障处
作者单位1.College of Aeronautics Engineering, Air Force Engineering University, Shanxi, Xi'an; 710038, China;
2.Xi'An Institute of Optics and Precision Mechanics of Cas, Shanxi, Xi'an; 710068, China
推荐引用方式
GB/T 7714
Zhuo, Chen,Huixiang, Sun,Yingwu, Wang,et al. Prediction of penetration depth of earth penetrator based on neural network[C]:Institute of Physics Publishing,2019.
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