留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

使用BP神经网络模型研究逆向卸荷膜片式减压器的稳定性能

陈经禄 王拥军 陈阳

陈经禄, 王拥军, 陈阳. 使用BP神经网络模型研究逆向卸荷膜片式减压器的稳定性能[J]. 航空动力学报, 2013, 28(9): 2112-2120.
引用本文: 陈经禄, 王拥军, 陈阳. 使用BP神经网络模型研究逆向卸荷膜片式减压器的稳定性能[J]. 航空动力学报, 2013, 28(9): 2112-2120.
CHEN Jing-lu, WANG Yong-jun, CHEN Yang. Research on stability of reverse unloading diaphragm pressure reducing regulator using BP neural network model[J]. Journal of Aerospace Power, 2013, 28(9): 2112-2120.
Citation: CHEN Jing-lu, WANG Yong-jun, CHEN Yang. Research on stability of reverse unloading diaphragm pressure reducing regulator using BP neural network model[J]. Journal of Aerospace Power, 2013, 28(9): 2112-2120.

使用BP神经网络模型研究逆向卸荷膜片式减压器的稳定性能

基金项目: 国家高技术研究发展计划;国家自然科学基金(11101023)

Research on stability of reverse unloading diaphragm pressure reducing regulator using BP neural network model

  • 摘要: 采用数据挖掘中BP(back propagation)神经网络模型来研究逆向卸荷膜片式减压器的结构参数与稳定性能之间的依赖关系,得到结构参数变化,尤其是多结构参数耦合变化下减压器的稳定性结果.其中稳定性对阻尼孔直径、膜片刚度非常敏感,对弹性元件材料的阻尼系数、低压腔有效长度较为灵敏.由此提出减弱振荡的各种措施:增大阻尼孔直径、增大膜片刚度、在一定范围(标准值的6.5倍)内增大弹性元件材料的阻尼系数、增大低压腔有效长度、减小阀芯质量.数值实验误差分析表明:该模型不存在过拟合、局部最优的情况,其预测结果是可靠的,可为减压器的设计和系统分析提供决策支持.而且,该模型对不同类型的数据集具有通用性,可以用来研究其他部件的结构参数与性能指标的依赖关系.

     

  • [1] 戴梧叶,马彬,张国舟,等.减压器特性实验研究[J].北京航空航天大学学报,1999,25(6):711-713. DAI Wuye,MA Bin,ZHANG Guozhou,et al.The research of regulator feature by experiment[J].Journal of Beijing University of Aeronautics and Astronautics,1999,25(6):711-713.(in Chinese)
    [2] 陈阳,蔡国飙,张振鹏,等.双组元统一推进系统减压器稳性仿真[J].北京航空航天大学学报,2010,36(10):1135-1139. CHEN Yang,CAI Guobiao,ZHANG Zhenpeng,et al.Numerical smiulation on dynamic stability of pressure reducing regulator in integral bipropellant propulsion system[J].Journal of Beijing University of Aeronautics and Astronautics,2010,36(10):1135-1139.(in Chinese)
    [3] Zafer N,Luecke G R.Stability of gas pressure regulators[J].Applied Mathematical Modeling,2008,32(1):61-82.
    [4] 郑赟韬,池元成,陈阳.基于均匀设计的粒子群优化在减压器优化设计中的应用[J].航空动力学报,2012,27(4):882-229. ZHENG Yuntao,CHI Yuancheng,CHEN Yang.Particle swarm optimization o pressure reducing regulator based on uniform design[J].Journal of Aerospace Power,2012,27(4):882-229.(in Chinese)
    [5] 尤裕荣,曾维亮.逆向卸荷式气体减压阀的动态特性仿真[J].火箭推进,2006,32(3):24-30. YOU Yurong,ZENG Weiliang.Simulation on reverse balanced pneumatic pressure reducing valve dynamic characteristic[J].Journal of Rocket Propulsion,2006,32(3):24-30.(in Chinese)
    [6] 尤裕荣,曾维亮.气体减压阀的稳定性分析[J].火箭推进,2009,35(5):34-38. YOU Yurong,ZENG Weiliang.Analysis on pneumatic pressure reducing valve stability[J].Journal of Rocket Propulsion,2009,35(5):34-38.(in Chinese)
    [7] 陈晓琴.减压阀充填过程动态特性仿真[J].导弹与航天运载技术,2006(5):45-49. CHEN Xiaoqin.Dynamic simulation of the pressure reducing valve in filling conditions[J].Missile and Space Vehicle,2006(5):45-49.(in Chinese)
    [8] 赖林,李清廉,郑丽,等.大流量气体减压器振动问题研究[J].国防科技大学学报,2009,31(2):1-4. LAI Lin,LI Qinglian,ZHENG Li,et al.Research of the vibration failure of the large flux PRV[J].Journal of National University of Defense Technology,2009,31(2):1-4.(in Chinese)
    [9] 陈阳,高芳,张黎辉,等.减压器动态仿真的有限体积模型[J].推进技术,2006,27(1):9-14. CHEN Yang,GAO Fang,ZHANG Lihui,et al.Finite volumemodel for numerical simulation on dynamic process of pressure reducing regulator[J].Journal of Propulsion Technology,2006,27(1):9-14.(in Chinese)
    [10] 陈阳,高芳,张振鹏,等.气动薄膜调节阀控制系统工作过程的动态仿真[J].火箭推进,2006,32(6):28-34. CHEN Yang,GAO Fang,ZHANG Zhenpeng,et al.Dynamic simulation of working process for control system of a pneumatic diaphragm control valve[J].Journal of Rocket Propulsion,2006,32(6):28-34.(in Chinese)
    [11] 朱明.数据挖掘[M].2版.合肥:中国科学技术出版社,2008.
    [12] Han J,Kamber M.Data mining:concepts and techniques[M].2nd ed.San Francisco:Morgan Kaufmann,2011.
    [13] Haykin S.Neural networks and learning machines[M].3rd ed.New Jersey:Prentice Hall,2009.
    [14] Frasconi P,Gori M.Backpropagation for linearly separable patterns:a detailed analysis[C]//IEEE International Conference on Neural Networks.San Francisco,US:IEEE,1993:1818-1822.
    [15] Baldi P,Hornik K.Neural networks and principal component analysis:learning from examples without local minima[J].Neural Networks,1989,2(1):53-58.
  • 加载中
计量
  • 文章访问数:  1357
  • HTML浏览量:  4
  • PDF量:  859
  • 被引次数: 0
出版历程
  • 收稿日期:  2012-09-17
  • 刊出日期:  2013-09-28

目录

    /

    返回文章
    返回