基于改进粒子群算法的火炮内弹道多参数符合计算

Multi Parameter Coincidence Calculation of Gun Interior Ballistics Based on Improved Particle Swarm Optimization Algorithm

为了研究火炮在发射过程中膛内火药燃烧规律以及弹丸运动规律,需要建立火炮内弹道数学模型并进行数值求解,在此过程中对部分内弹道参数进行符合计算是优化内弹道模型的重要途经之一。在经典内弹道方程组的基础上,阐述了经典内弹道计算原理,并对基本粒子群算法进行了改进,使改进后的粒子群算法在迭代初期有较大的惯性权重ω和学习因子c_(1)以及较小的学习因子c_(2),而在迭代后期有较小的惯性权重ω和学习因子c_(1)以及较大的学习因子c_(2),从而有效地避免粒子群陷入局部最优而导致收敛精度低的缺陷。将改进后的粒子群算法应用于火炮内弹道多参数符合计算,算例结果表明该方法完全满足工程实际要求,具有收敛速度快、符合精度高的特性,是火炮内弹道多参数符合计算的理想算法之一。

In order to study the combustion law of gunpowder and the motion law of projectiles in the gun bore during the launching process,it is necessary to establish the mathematical model of gun interior ballistics and to carry out numerical solution.In this process,it is one of the important ways to optimize the interior ballistic model to coincidentally calculate some interior ballistic parameters.Based on the classical interior ballistic equations,the classical calculation principle of interior ballistic parameters is elaborated,and the basic particle swarm optimization algorithm is improved,In the early stage of the iteration,the improved particle swarm optimization algorithm has a larger inertia weightω,a learning factor c_(1) and a smaller learning factor c_(2).In the later stage of the iteration,the improved particle swarm optimization algorithm has a smaller inertia weightω,a learning factor c_(1) and a larger learning factor c_(2),so as to effectively avoid the defect that the particle swarm is trapped in the local optimum and the convergence accuracy is low.The improved particle swarm optimization(PSO)algorithm is applied to the multi parameter coincidence calculation of the interior ballistic of the gun.The result of the example shows that the method can fully meet the requirements of the engineering practice,the algorithm has the characteristics of fast convergence speed and high coincidence accuracy,and is one of the ideal algorithms for the multi parameter coincidence calculation of the interior ballis tic of the gun.