Research article

A novel quantity for identifying the repelling structures of continuous dynamical systems

  • Received: 11 August 2020 Accepted: 14 January 2021 Published: 19 January 2021
  • MSC : 37A25, 37M25, 76M27

  • We propose a new quantity to study complicated dynamical systems based on the repelling behaviors of particle trajectories throughout the whole time interval under consideration. Since this proposed quantity measures the averaged repelling rate along each particle trajectory against nearby trajectories, we name the quantity the Lagrangian Averaged Repelling Rate (LARR). The LARR is shown to be objective, i.e. unchanged under time-dependent rotations and translations of the coordinate frame. We also compare the proposed LARR with the commonly used concept called the finite time Lyapunov exponent (FTLE), the latter also measures the separation behaviors of particles but only cares about the initial and terminal states of them. An efficient Eulerian algorithm is also proposed to compute the LARR. Numerical examples illustrate the effectiveness of the LARR in measuring the repelling properties of particle trajectories and also the difference between the proposed LARR and the traditional FTLE.

    Citation: Guoqiao You. A novel quantity for identifying the repelling structures of continuous dynamical systems[J]. AIMS Mathematics, 2021, 6(4): 3378-3392. doi: 10.3934/math.2021202

    Related Papers:

  • We propose a new quantity to study complicated dynamical systems based on the repelling behaviors of particle trajectories throughout the whole time interval under consideration. Since this proposed quantity measures the averaged repelling rate along each particle trajectory against nearby trajectories, we name the quantity the Lagrangian Averaged Repelling Rate (LARR). The LARR is shown to be objective, i.e. unchanged under time-dependent rotations and translations of the coordinate frame. We also compare the proposed LARR with the commonly used concept called the finite time Lyapunov exponent (FTLE), the latter also measures the separation behaviors of particles but only cares about the initial and terminal states of them. An efficient Eulerian algorithm is also proposed to compute the LARR. Numerical examples illustrate the effectiveness of the LARR in measuring the repelling properties of particle trajectories and also the difference between the proposed LARR and the traditional FTLE.


    加载中


    [1] E. J. Candès, L. Ying, Fast geodesics computation with the phase flow method, J. Comput. Phys., 220 (2006), 6–18. doi: 10.1016/j.jcp.2006.07.032
    [2] R. Ding, J. Li, Nonlinear finite-time Lyapunov exponent and predictability, Physics Letters A, 364 (2007), 396–400. doi: 10.1016/j.physleta.2006.11.094
    [3] S. Gottlieb, C. W. Shu, Total variation diminishing Runge-Kutta schemes, Math. Comput., 67 (1998), 73–85. doi: 10.1090/S0025-5718-98-00913-2
    [4] M. A. Green, C. W. Rowley, A. J. Smiths, Using hyperbolic Lagrangian coherent structures to investigate vortices in biospired fluid flows, Chaos, 20 (2010), 017510. doi: 10.1063/1.3270045
    [5] G. Haller, Distinguished material surfaces and coherent structures in Three-Dimensional fluid flows, Physica D, 149 (2001), 248–277. doi: 10.1016/S0167-2789(00)00199-8
    [6] G. Haller, Lagrangian structures and the rate of Strain in a partition of Two-Dimensional turbulence, Phys. Fluids A, 13 (2001), 3368–3385.
    [7] G. Haller, Lagrangian coherent structures from approximate velocity data, Physics Fluid, 14 (2002), 1851–1861. doi: 10.1063/1.1477449
    [8] G. Haller, A variational theory of hyperbolic Lagrangian coherent structure, Physica D, 240 (2011), 574–598. doi: 10.1016/j.physd.2010.11.010
    [9] G. Haller, G. Yuan, Lagrangian coherent structures and mixing in Two-Dimensional turbulence, Physica D, 147 (2000), 352–370. doi: 10.1016/S0167-2789(00)00142-1
    [10] D. Karrasch, G. Haller, Do finite-size Lynapunov exponents detect coherent structures? Chaos, 23 (2013), 043126. doi: 10.1063/1.4837075
    [11] F. Lekien, N. Leonard, Dynamically consistent Lagrangian coherent structures, Experimental Chaos: 8-th Experimental Chaos Conference, 2004,132–139.
    [12] F. Lekien, S. D. Ross, The computation of finite-time Lyapunov exponents on unstructured meshes and for non-Euclidean manifolds, Chaos, 20 (2010), 017505. doi: 10.1063/1.3278516
    [13] F. Lekien, S. C. Shadden, J. E. Marsden, Lagrangian coherent structures in $n$-dimensional systems, J. Math. Phys., 48 (2007), 065404. doi: 10.1063/1.2740025
    [14] S. Leung, An Eulerian approach for computing the finite time Lyapunov exponent, J. Comput. Phys., 230 (2011), 3500–3524. doi: 10.1016/j.jcp.2011.01.046
    [15] S. Leung, A backward phase flow method for the finite time Lyapunov exponent, Chaos, 23 (2013), 043132. doi: 10.1063/1.4847175
    [16] S. Leung, J. Qian, R. Burridge, Eulerian Gaussian Beams for high frequency wave propagation, Geophysics, 72 (2007), SM61–SM76. doi: 10.1190/1.2752136
    [17] D. Lipinski, K. Mohseni, Flow structures and fluid transport for the hydromedusae Sarsia tubulosa and Aequorea victoria, J. Exp. Biology, 212 (2009), 2436–2447. doi: 10.1242/jeb.026740
    [18] X. D. Liu, S. J. Osher, T. Chan, Weighted essentially NonOscillatory schemes, J. Comput. Phys., 115 (1994), 200–212. doi: 10.1006/jcph.1994.1187
    [19] S. Lukens, X. Yang, L. Fauci, Using Lagrangian coherent structures to analyze fluid mixing by cillia, Chaos, 20 (2010), 017511. doi: 10.1063/1.3271340
    [20] T. Sapsis, G. Haller, Inertial particle dynamics in a hurricane, J. Atmos. Sci., 66 (2009), 2481–2492. doi: 10.1175/2009JAS2865.1
    [21] S. C. Shadden, F. Lekien, J. E. Marsden, Definition and properties of Lagrangian coherent structures from finite-time Lyapunov exponents in two-dimensional aperiodic flows, Physica D, 212 (2005), 271–304. doi: 10.1016/j.physd.2005.10.007
    [22] C. W. Shu, Essentially Non-Oscillatory and weighted essentially Non-Oscillatory schemes for hyperbolic conservation laws, NASA Langley Research Center, 1997.
    [23] W. Tang, T. Peacock, Lagrangian coherent structures and internal wave attractors, Chaos, 20 (2010), 017508. doi: 10.1063/1.3273054
    [24] F. Wang, D. Zhao, L. Deng, S. Li, An accurate vortex feature extraction method for Lagrangian vortex visualization on high-order flow field data, J. Visualization, 20 (2017), 729–742. doi: 10.1007/s12650-017-0421-y
    [25] G. You, T. Wong, S. Leung, Eulerian methods for visualizing continuous dynamical systems using Lyapunov exponents, SIAM J. Sci. Comput., 39 (2017), A415–A437. doi: 10.1137/16M1066890
  • Reader Comments
  • © 2021 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(1564) PDF downloads(81) Cited by(0)

Article outline

Figures and Tables

Figures(11)  /  Tables(1)

Other Articles By Authors

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog