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 |
DOI | 10.1088/1755-1315/267/3/032004 |
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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文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
Prediction of penetr(727KB) | 会议论文 | 限制开放 | CC BY-NC-SA | 请求全文 |
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