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两相流相空间多元图重心轨迹动力学特征

赵俊英 金宁德

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两相流相空间多元图重心轨迹动力学特征

赵俊英, 金宁德

Dynamic characteristics of multivariate graph centrobaric trajectory in phase space of two-phase flow

Zhao Jun-Ying, Jin Ning-De
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  • 本文提出了一种新的混沌时间序列高维相空间多元图重心轨迹动力学特征提取方法. 在确定了最佳嵌入维数和延迟时间后, 将相空间中高维矢量点映射到二维平面的雷达图上, 相应地将相空间中高维矢量点变换为对应的几何多边形. 通过提取几何多边形的重心位置得到重心轨迹动力学演化特性, 并利用重心轨迹矩特征量区分不同性质的混沌时间序列. 在此基础上, 处理分析了气液两相流电导传感器动态信号, 发现高维相空间多元图重心轨迹矩特征量不仅可以辨识泡状流、段塞流和混状流, 而且为流型动力学演化机理提供了新的分析途径.
    We propose a multivariate graph centrobaric trajectory-based method for characterizing nonlinear dynamics from high-dimensional chaotic time series. After the optimal selecting of the embedding dimension and time delay, we map the high-dimensional vector point into the two-dimensional radial plane graph, i.e., the high-dimensional vector point is transformed correspondingly to a geometric polygon. By extracting the geometric location of the polygon barycenters, we can obtain the evolving feature of the barycenter dynamical trajectory. Then we use the moment quantity of the barycenter trajectory to distinguish different chaotic time series. Finally, we apply our method to the fluctuating signals measured from gas-liquid two-phase flow experiments. The results suggest that our method can be a powerful tool for not only distinguishing the different flow patterns but also investigating the dynamical evolving mechanism of flow patterns.
    • 基金项目: 国家自然科学基金(批准号:50974095, 41174109)和 国家科技重大专项(批准号:2011ZX05020-006)资助的课题.
    • Funds: Project supported by the National Natural Science Foundation of China (Grant Nos. 50974095, 41174109), and the National Science and Technology Major Projects (Grant No. 2011ZX05020-006).
    [1]

    Daw C S, Lawkins W F, Downing D J, Clapp N E 1990 Phys. Rev. A 41 1179

    [2]

    Lawkins W F, Daw C S, Downing D J, Clapp N E 1993 Phys Rev. E 47 2520

    [3]

    Daw C S, Finney C E A, Vasudevan M, vanGoor N A, Nguyen K, Bruns D D, Kostelich E J, Grebogi C, Ott E, Yorke J A 1995 Phys. Rev. Lett. 75 2308

    [4]

    Lahey R T 1991 Heat Mass Transfer. 26 351

    [5]

    Letzel H M, Schouten J C, Krishna R, van den Bleek C M 1997 Chem. Eng. Sci. 52 4447

    [6]

    Ji H, Ohara H, Kuramoto K, Tsutsumi A, Yoshida K, Hirama T 2000 Chem. Eng. Sci. 55 403

    [7]

    Llauro F X, Llop M F 2006 Int. J. Multiphase Flow 32 1397

    [8]

    Xiao N, Jin N D 2007 Acta Phys. Sin. 56 5149 (in Chinese) [肖楠, 金宁德 2007 物理学报 56 5149]

    [9]

    Zhao G B, Yang Y R 2003 AIChE J. 49 869

    [10]

    Li H W, Zhou Y L, Sun B, Yang Y 2010 Chin. J. Chem. Eng. 18 880

    [11]

    Yu H, Chen Y S 2009 Advance in Mechanics 39 154 (in Chinese) [于海, 陈予恕 2009 力学进展 39 154]

    [12]

    Zoldi S M, Greenside H S 1997 Phys. Rev. Lett. 78 1687

    [13]

    Meng D Y, Xu C, Xu Z B 2010 Chin. J. Comput. 33 545 (in Chinese) [孟德宇, 徐晨, 徐宗本 2010 计算机学报 33 545]

    [14]

    Liu Y, Liu Y, Keith C.C.Chan 2010 Signal Processing 90 2375

    [15]

    Auerbach D, Cvitanovic P, Eckmann J P, Gunaratne G, Procaccia I 1987 Phys. Rev. Lett. 58 2387

    [16]

    Saiki Y, Yamada M 2008 Nonlinear Processes in Geophysics 15 675

    [17]

    Cong R, Liu S L, Ma R 2008 Acta Phys. Sin. 57 7487 (in Chinese) [丛蕊, 刘树林, 马瑞 2008 物理学报 57 7487]

    [18]

    Zhang C T, Ma Q L, Peng H 2010 Acta Phys. Sin. 59 7623 (in Chinese) [张春涛, 马千里, 彭宏 2010 物理学报 59 7623]

    [19]

    You R Y, Huang X J 2011 Chin. Phys. B 20 020505

    [20]

    Hong W X, Li X, Xu Y H, Wang J J, Song J L 2008 Information fusion and pattern recognition based on the principle of multidimensional chart expression (Beijing: National Defense Industry Press) p193 (In Chinese) [洪文学, 李昕, 徐永红, 王金甲, 宋佳霖 2008 基于多元统计图表示原理的信息融合和模式识别技术 (北京:国防工业出版社)第193页]

    [21]

    Takens F, 1981 Dynamical Systems and Turbulence (Berlin: Spring Verlag) p366

    [22]

    Saary M J 2008 Journal of Clinical Epidemiology 60 311

    [23]

    Hubert M, Rousseeuw P J, Verboven S 2002 Chemometrics and Intelligent Laboratory Systems 60 100

    [24]

    Kim H S, Eykholt R, Salas J D 1999 Physica D 127 48

    [25]

    Zheng G B, Jin N D 2009 Acta Phys. Sin. 58 4485 (in Chinese) [郑桂波, 金宁德 2009 物理学报 58 4485]

  • [1]

    Daw C S, Lawkins W F, Downing D J, Clapp N E 1990 Phys. Rev. A 41 1179

    [2]

    Lawkins W F, Daw C S, Downing D J, Clapp N E 1993 Phys Rev. E 47 2520

    [3]

    Daw C S, Finney C E A, Vasudevan M, vanGoor N A, Nguyen K, Bruns D D, Kostelich E J, Grebogi C, Ott E, Yorke J A 1995 Phys. Rev. Lett. 75 2308

    [4]

    Lahey R T 1991 Heat Mass Transfer. 26 351

    [5]

    Letzel H M, Schouten J C, Krishna R, van den Bleek C M 1997 Chem. Eng. Sci. 52 4447

    [6]

    Ji H, Ohara H, Kuramoto K, Tsutsumi A, Yoshida K, Hirama T 2000 Chem. Eng. Sci. 55 403

    [7]

    Llauro F X, Llop M F 2006 Int. J. Multiphase Flow 32 1397

    [8]

    Xiao N, Jin N D 2007 Acta Phys. Sin. 56 5149 (in Chinese) [肖楠, 金宁德 2007 物理学报 56 5149]

    [9]

    Zhao G B, Yang Y R 2003 AIChE J. 49 869

    [10]

    Li H W, Zhou Y L, Sun B, Yang Y 2010 Chin. J. Chem. Eng. 18 880

    [11]

    Yu H, Chen Y S 2009 Advance in Mechanics 39 154 (in Chinese) [于海, 陈予恕 2009 力学进展 39 154]

    [12]

    Zoldi S M, Greenside H S 1997 Phys. Rev. Lett. 78 1687

    [13]

    Meng D Y, Xu C, Xu Z B 2010 Chin. J. Comput. 33 545 (in Chinese) [孟德宇, 徐晨, 徐宗本 2010 计算机学报 33 545]

    [14]

    Liu Y, Liu Y, Keith C.C.Chan 2010 Signal Processing 90 2375

    [15]

    Auerbach D, Cvitanovic P, Eckmann J P, Gunaratne G, Procaccia I 1987 Phys. Rev. Lett. 58 2387

    [16]

    Saiki Y, Yamada M 2008 Nonlinear Processes in Geophysics 15 675

    [17]

    Cong R, Liu S L, Ma R 2008 Acta Phys. Sin. 57 7487 (in Chinese) [丛蕊, 刘树林, 马瑞 2008 物理学报 57 7487]

    [18]

    Zhang C T, Ma Q L, Peng H 2010 Acta Phys. Sin. 59 7623 (in Chinese) [张春涛, 马千里, 彭宏 2010 物理学报 59 7623]

    [19]

    You R Y, Huang X J 2011 Chin. Phys. B 20 020505

    [20]

    Hong W X, Li X, Xu Y H, Wang J J, Song J L 2008 Information fusion and pattern recognition based on the principle of multidimensional chart expression (Beijing: National Defense Industry Press) p193 (In Chinese) [洪文学, 李昕, 徐永红, 王金甲, 宋佳霖 2008 基于多元统计图表示原理的信息融合和模式识别技术 (北京:国防工业出版社)第193页]

    [21]

    Takens F, 1981 Dynamical Systems and Turbulence (Berlin: Spring Verlag) p366

    [22]

    Saary M J 2008 Journal of Clinical Epidemiology 60 311

    [23]

    Hubert M, Rousseeuw P J, Verboven S 2002 Chemometrics and Intelligent Laboratory Systems 60 100

    [24]

    Kim H S, Eykholt R, Salas J D 1999 Physica D 127 48

    [25]

    Zheng G B, Jin N D 2009 Acta Phys. Sin. 58 4485 (in Chinese) [郑桂波, 金宁德 2009 物理学报 58 4485]

计量
  • 文章访问数:  3537
  • PDF下载量:  701
  • 被引次数: 0
出版历程
  • 收稿日期:  2010-12-28
  • 修回日期:  2012-05-10
  • 刊出日期:  2012-05-05

两相流相空间多元图重心轨迹动力学特征

  • 1. 天津大学电气与自动化工程学院, 天津 300072;
  • 2. 天津电子信息职业技术学院电子技术系, 天津 300350
    基金项目: 

    国家自然科学基金(批准号:50974095, 41174109)和 国家科技重大专项(批准号:2011ZX05020-006)资助的课题.

摘要: 本文提出了一种新的混沌时间序列高维相空间多元图重心轨迹动力学特征提取方法. 在确定了最佳嵌入维数和延迟时间后, 将相空间中高维矢量点映射到二维平面的雷达图上, 相应地将相空间中高维矢量点变换为对应的几何多边形. 通过提取几何多边形的重心位置得到重心轨迹动力学演化特性, 并利用重心轨迹矩特征量区分不同性质的混沌时间序列. 在此基础上, 处理分析了气液两相流电导传感器动态信号, 发现高维相空间多元图重心轨迹矩特征量不仅可以辨识泡状流、段塞流和混状流, 而且为流型动力学演化机理提供了新的分析途径.

English Abstract

参考文献 (25)

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