基于混沌量子粒子群的舰炮身管多目标优化研究

舰炮的炮口扰动是影响舰炮射击精度的重要因素。为了减少舰炮的炮口扰动，优化身管结构，建立了柔性身管的有限元模型并将该模型的模态计算结果与模态试验测试值进行对比,证明建立的柔性身管有限元模型是有效的。提出了混沌量子粒子群算法与动力学联合优化的方法，进行身管和炮口的多目标优化，优化结果表明：优化后的炮口中心的线速度、角速度和角位移与优化前相比显著减小，身管质量有所降低，结构更加合理，该优化方法有效可行，为下一步全炮总体优化设计提供了一定的借鉴。

Multi-objective Optimization of Naval Gun Barrel Based on New Quantum Chaos Particle Swarm Optimization Algorithm

Muzzle disturbance is an important factor affecting the accuracy of naval gun firing. In order to reduce the muzzle disturbance and optimize the barrel structure, finite element model of flexible barrel is established and compares the modal calculation results of finite element model with the modal test values, proved that the established flexible body finite element model is effective. A method of new quantum chaos particle swarm optimization algorithm and dynamic joint optimization is proposed to carry out multi-objective optimization of the artillery recoil mechanism. Optimization results indicate that the line speed, angular velocity and angular displacement of optimized muzzle center is significantly reduced compared with that before optimization, the barrel quality is reduced, and the structure is more reasonable. The optimization method is effective and feasible, which provides a reference for the overall design of the whole naval gun.