基于改进近似模型的水下鱼雷打靶快速预报方法

Fast Prediction Method of Underwater Torpedo Targeting Based on Improved Approximate Model

摘要:

鱼雷打靶试验对评价武器系统对目标的打击性能具有重要意义。水动力参数是模拟鱼雷水下运动轨迹、预报鱼雷打靶落点的关键参数。随着现代 CFD 技术的发展，采用数值方法获取水动力参数，提高了轨迹预报精度，但计算效率不高，不利于多工况下鱼雷打靶预报。首先，基于刚体动量和动量矩定理建立了鱼雷的水下运动方程组，运用 4 阶龙格–库塔(Runge-Kuta)法对运动方程组进行数值求解，对鱼雷水下轨迹进行模拟，从而获取鱼雷打靶性能。其次，提出了基于遗传算法的近似模型(GA-BP)和自适应遗传算法优化的近似模型(AGA-BP)模拟鱼雷水下打靶落点，对鱼雷打靶性能进行快速预报。通过仿真和数据对比，结果表明：AGA-BP 预测模型相对于 BP 预测模型更稳定、GA-BP 预测模型收敛速度更快，实现对鱼雷水下打靶快速预报。

Abstract:

Torpedo targeting experiment is of great significance to evaluate the striking performance of weapon systems against targets，in which hydrodynamic parameters are the key link to accurately predict the trajectory. With the development of modern CFD technology，numerical methods are used to obtain hydrodynamic parameters，which can improve the forecast accuracy，but the computational efficiency is not high，which is not conducive to the prediction of torpedo targeting under multiple working conditions. In this paper，we firstly establish a set of underwater equations of motion for torpedoes based on rigid body momentum and momentum moment theorems，and also numerically solve the established set of equations of motion by using the 4th order Runge-kuta method. Then an approximate model based on genetic algorithm(GA-BP)is proposed on the basis of the BP model，after which the model is optimized and an approximate model optimized by adaptive genetic algorithm(AGA-BP)is proposed. Finally，simulations and data comparison of the three models are conducted， and the results show that the AGA-BP prediction model is more stable and the GA-BP prediction model converges faster compared to the BP prediction model.