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兵工学报 ›› 2016, Vol. 37 ›› Issue (10): 1795-1802.doi: 10.3969/j.issn.1000-1093.2016.10.005

• 论文 • 上一篇    下一篇

基于Kriging模型的武器站炮口扰动优化

邓威, 毛保全, 冯帅, 李程   

  1. (装甲兵工程学院 兵器工程系, 北京 100072)
  • 收稿日期:2016-02-01 修回日期:2016-02-01 上线日期:2016-12-08
  • 通讯作者: 邓威 E-mail:314829483@qq.com
  • 作者简介:邓威(1986—),男,博士研究生
  • 基金资助:
    武器装备预先研究项目(2011YY34)

Optimization of Muzzle Disturbance of Overhead Weapon Station Based on Kriging Model

DENG Wei, MAO Bao-quan, FENG Shuai, LI Cheng   

  1. (Department of Arms Engineering, Academy of Armored Force Engineering, Beijing 100072, China)
  • Received:2016-02-01 Revised:2016-02-01 Online:2016-12-08
  • Contact: DENG Wei E-mail:314829483@qq.com

摘要: 炮口扰动是影响顶置武器站射击精度的重要因素。为研究抑制炮口扰动的技术途径,基于有限元理论建立顶置武器站刚柔耦合动力学模型,并通过射击精度试验验证了该模型的准确性。以炮口扰动综合函数为优化目标,构建顶置武器站炮口扰动优化设计模型,应用Kriging序列优化算法实现优化问题的求解。仿真结果表明,考虑部件柔性变形的有限元模型符合真实系统的振动特性,Kriging序列优化算法能够快速有效地用于武器站炮口扰动优化问题的求解。寻优得到的全局最优解,能使顶置武器站炮口扰动得到有效抑制。

关键词: 兵器科学与技术, 顶置武器站, 炮口扰动, 有限元模型, Kriging序列优化

Abstract: Muzzle disturbance is an important factor to affect the firing accuracy of overhead weapon station (OWS). In order to study the approach to inhibite the muzzle disturbance, an OWS rigid-flexible coupling dynamic model is established based on finite element theory, of which accuracy is verified by the firing accuracy test. The synthetic function of muzzle disturbance is defined as optimization objective to construct an optimization design model of OWS muzzle disturbance, and the Kriging sequential optimization method is used to solve the optimization problem. The simulated results show that the finite element model, considering the flexible deformation of parts , is a good approximation of the vibration characteristics of real system, and Kriging sequential optimization method can be applied to solve the optimization problems of OWS muzzle disturbance. The global optimal solutions searched in optimization problems can be used to suppress OWS muzzle disturbance.

Key words: ordnance science and technology, overhead weapon station, muzzle disturbance, finite element model, Kriging sequential optimization

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