摘要
为在不增加计算时耗的前提下提升多枚杀爆弹对面目标打击毁伤效能,建立融入动爆威力计算的多瞄准点规划方法.对面目标采用结构化网格划分方法实现多枚杀爆弹对目标毁伤区域的精确计算,并进行计算结果验证,基于多次计算结果采用神经网络方法建立单枚弹药对面目标毁伤区域的计算代理模型,在同样计算条件下,比非代理模型计算时间缩短1000倍;据此,通过差分进化算法实现多枚杀爆弹对面目标打击瞄准点及末端弹道参数的规划.通过实例对比分析表明:该瞄准点规划方法形成的打击方案比传统以毁伤半径为输入的方法毁伤效果大幅提升,最低提升25.5%,且单次规划时间不超过3 s,解决了瞄准点规划中毁伤效能模型复杂度与计算耗时之间的矛盾.
In order to improve the damage effectiveness of multiple anti-explosive shells against targets without increasing computational time consumption,a multiple aiming points planning method incorporating dynamic explosive power calculation was established.Accurate calculation of target damage area caused by multiple blast-fragmentation warheads was made using structured grid generation method for area targets,and computational results were verified.Based on simulation calculation data of multiple dynamic explosion damage effects,neural network method was used to establish a computational agent model of single ammunition for area target dam-aged area.Under the same calculation conditions,its calculation time was 1000 times shorter than that of non-agent model.Based on this,the differential evolution algorithm was used to plan the aiming point and terminal ballistic parameters of multiple blast-fragmentation warheads.Case analysis shows that this aiming point plan-ning method has greatly improved the damage efficiency compared with the traditional method with damage ra-dius as the input,and the minimum increase is 25.5%,and the single planning time does not exceed 3 seconds,solving the contradiction between the complexity of the damage efficiency model and the computational time in aiming point planning.
作者
徐豫新
贾志远
杨晓红
索非
张益荣
XU Yuxin;JIA Zhiyuan;YANG Xiaohong;SUO Fei;ZHANG Yirong(State Key Laboratory of Explosion Science and Technology,Beijing Institute of Technology,Beijing 100081,China;Chongqing Innovation Center,Beijing Institute of Technology,Chongqing 401120,China;63961 Troop of PLA,Beijing 100012,China)
出处
《北京理工大学学报》
EI
CAS
CSCD
北大核心
2024年第2期146-155,共10页
Transactions of Beijing Institute of Technology
基金
基础加强计划技术领域基金项目(2022-JCJQ-JJ-0334)。
关键词
杀爆弹
动爆威力
瞄准点规划
毁伤幅员
神经网络
差分进化算法
blast-fragmentation warhead
dynamic explosive power
aiming point planning
dimensionality of damage
neural network
differential evolution algorithm
