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兵工学报 ›› 2019, Vol. 40 ›› Issue (2): 234-242.doi: 10.3969/j.issn.1000-1093.2019.02.002

• 论文 • 上一篇    下一篇

舰炮身管的抗振性能优化研究

富威, 吴琼, 崔运山, 刘琪, 周超   

  1. (哈尔滨工程大学 机电工程学院, 黑龙江 哈尔滨 150001)
  • 收稿日期:2018-05-18 修回日期:2018-05-18 上线日期:2019-04-08
  • 通讯作者: 富威(1975—),男,副教授,硕士生导师 E-mail:fuwei@hrbeu.edu.cn
  • 作者简介:吴琼(1994—),女,硕士研究生。E-mail:757539771@qq.com

Optimization of Anti-vibration Performance of Naval Gun Barrel

FU Wei, WU Qiong, CUI Yunshan, LIU Qi, ZHOU Chao   

  1. (College of Mechanical and Electrical Engineering, Harbin Engineering University, Harbin 150001, Heilongjiang,China)
  • Received:2018-05-18 Revised:2018-05-18 Online:2019-04-08

摘要: 以舰炮连续射击时的射击精度为背景,针对舰炮身管抗振性能优化问题进行了研究。基于舰炮身管的实际模型,建立了3段梁结构简化模型,进而建立并推导了舰炮身管刚度优化数学模型。通过灵敏度分析方法,得到了舰炮身管刚度与质量的灵敏度分析图谱,从而确定了优化参数。利用ANSYS软件平台完成了舰炮身管抗振性能的多目标优化,得到了舰炮身管优化的最优解。将优化后舰炮身管模型与实际舰炮身管模型进行对比,对比结果表明:改变舰炮身管的有效设计变量,可使其动态性能得到改善;舰炮身管经过抗振性能优化后,1阶固有频率提高17.99%,质量降低6.12%,达到了优化目的。

关键词: 舰炮身管, 抗振性能, 灵敏度, 多目标遗传算法, 优化

Abstract: The optimization of anti-vibration performance of barrel of naval gun is studied based on the firing accuracy of naval gun during continuous firing. According to the actual model of barrel, a simplified model of the three-section beam structure of barrel is put forward. And then a mathematical model of the barrel stiffness optimization is established, and the stiffness model of barrel is deduced. The sensitivity analysis method is used to obtain the sensitivity analysis map of barrel stiffness and mass, thus determining the optimized parameters. The ANSYS software is used for the multi-objective optimization of the anti-vibration performance of barrel. The optimal solution of barrel optimization was obtained and compared with the actual barrel model. The results show that the effective design variables of barrel could be changed to improve the dynamic performance of barrel. After the anti-vibration performance is optimized,the first order natural frequency is increased by 17.99%, and the mass of barrel is reduced by 6.12%. Key

Key words: navalgunbarrel, anti-vibrationperformance, sensitivity, multi-targetgeneticalgorithm, optimization

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