基于Pareto遗传算法的无后坐炮内弹道多目标优化设计  

Multi-objective Optimization Design for Interior Ballistics of Recoilless Rifle Based on Pareto Genetic Algorithm

作　　者：肖剑 王雨时[1] 张志彪[1] XIAO Jian;WANG Yushi;ZHANG Zhibiao(School of Mechanical Engineering,Nanjing University of Science and Technology,Nanjing 210094,China)

机构地区：[1]南京理工大学机械工程学院

出　　处：《弹道学报》2019年第3期53-57,共5页Journal of Ballistics

摘　　要：针对某中口径无后坐炮研制中的内弹道设计问题,将多目标优化的Pareto遗传算法与无后坐炮经典内弹道数学模型相结合,以最大膛压和初速作为优化设计的目标,对药室容积和发射药药量进行了多目标优化设计,提出了一种基于Pareto遗传算法的无后坐炮内弹道多目标优化方法。优化结果表明:所采用的Pareto遗传算法与内弹道相结合是可行的;利用Pareto遗传算法,所得数个内弹道目标优化解以Pareto前沿面的形式给出,设计人员可根据需要从中选择无后坐炮内弹道的最优设计方案。Aiming at the design problem of interior ballistics for a medium-caliber recoilless rifle,the Pareto genetic algorithm based on multi-objective optimization was combined with the classical interior ballistic mathematical model of recoilless rifle. Taking the maximum bore pressure and initial velocity as the objective of optimization design,the volume of chamber and the quality of propellant were optimized by multi-objective optimization. A multi-objective optimization method of interior ballistics of recoilless rifle based on Pareto genetic algorithm was proposed. The optimization results show that the Pareto genetic algorithm is feasible for interior ballistics,and several optimized-results of interior ballistic targets can be given in the form of Pareto frontier by using Pareto genetic algorithm. Designers can choose the optimal design scheme of interior ballistics for recoilless rifle according to their needs.

关 键 词：Pareto遗传算法 无后坐炮 内弹道优化 

分 类 号：TJ32[兵器科学与技术—火炮、自动武器与弹药工程]