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Local Trajectory Variation Exponent (LTVE) for Visualizing Dynamical Systems (2401.09222v1)

Published 17 Jan 2024 in math.DS and physics.flu-dyn

Abstract: The identification and visualization of Lagrangian structures in flows plays a crucial role in the study of dynamic systems and fluid dynamics. The Finite Time Lyapunov Exponent (FTLE) has been widely used for this purpose; however, it only approximates the flow by considering the positions of particles at the initial and final times, ignoring the actual trajectory of the particle. To overcome this limitation, we propose a novel quantity that extends and generalizes the FTLE by incorporating trajectory metrics as a measure of similarity between trajectories. Our proposed method utilizes trajectory metrics to quantify the distance between trajectories, providing a more robust and accurate measure of the LCS. By incorporating trajectory metrics, we can capture the actual path of the particle and account for its behavior over time, resulting in a more comprehensive analysis of the flow. Our approach extends the traditional FTLE approach to include trajectory metrics as a means of capturing the complexity of the flow.

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References (47)
  1. Computing the Discrete Fréchet Distance in Subquadratic Time. CoRR, abs/1204.5333, 2012.
  2. D. Blazevski and G. Haller. Hyperbolic And Elliptic Transport Barriers In Three-Dimensional Unsteady Flows. Physica D: Nonlinear Phenomena, 273-274:46–62, 2014.
  3. K. Bringmann. Why Walking The Dog Takes Time: Frechet Distance Has No Strongly Subquadratic Algorithms Unless SETH Fails. CoRR, abs/1404.1448, 2014.
  4. B.M. Cardwell and K. Mohseni. Vortex shedding over two-dimensional airfoil: Where do the particles come from? AIAA J., 46:545–547, 2008.
  5. T.M. Chan and Z. Rahmati. An Improved Approximation Algorithm for the Discrete Fréchet Distance. Information Processing Letters, 138:72–74, 2018.
  6. W.M. Chau and S. Leung. Within-Cluster Variability Exponent for Identifying Coherent Structures in Dynamical Systems. Commun. Comput. Phys., 33(3):824–848, 2023.
  7. T. Eiter and H. Mannila. Computing Discrete Fréchet Distance. Technical report, Technical University of Vienna, 1994.
  8. Vector Field k-Means: Clustering Trajectories by Fitting Multiple Vector Fields. Eurographics Conference on Visualization (EuroVis), 32(3):201–210, 2013.
  9. Probabilistic clustering of extratropical cyclones using regression mixture models. Clim. Dyn., 29:423–440, 2007.
  10. S. Gaffney and P. Smyth. Trajectory Clustering with Mixtures of Regression Models. Proc. 5th ACM SIGKDD Int’l Conf. on Knowledge Discovery and Data Mining, pages 63–72, 1999.
  11. Using hyperbolic Lagrangian coherent structures to investigate vortices in biospired fluid flows. Chaos, 20:017510, 2010.
  12. G. Haller. Distinguished material surfaces and coherent structures in three-dimensional fluid flows. Physica D, 149:248–277, 2001.
  13. G. Haller. Lagrangian structures and the rate of strain in a partition of two-dimensional turbulence. Phys. Fluids A, 13:3368–3385, 2001.
  14. G. Haller. A variational theory of hyperbolic Lagrangian Coherent Structure. Physica D, 240:574–598, 2011.
  15. G. Haller. Lagrangian coherent structures. Annu. Rev. Fluid Mech., 47:137–162, 2015.
  16. G. Haller and G. Yuan. Lagrangian coherent structures and mixing in two-dimensional turbulence. Physica D, 147:352–370, 2000.
  17. Trajectory clustering: a partition-and-group framework. ACM SIGMOD International Conference on Management of Data, pages 593–604, 2007.
  18. F. Lekien and N. Leonard. Dynamically consistent Lagrangian coherent structures. Experimental Chaos: 8-th Experimental Chaos Conference, pages 132–139, 2004.
  19. Lagrangian coherent structures in n𝑛nitalic_n-dimensional systems. Journal of Mathematical Physics, 48:065404, 2007.
  20. S. Leung. An Eulerian approach for computing the finite time Lyapunov exponent. J. Comput. Phys., 230:3500–3524, 2011.
  21. S. Leung. The backward phase flow method for the finite time Lyapunov exponent. Chaos, 23(043132), 2013.
  22. Recent developments in Eulerian approaches for visualizing continuous dynamical systems. Proceedings of the Seventh International Congress of Chinese Mathematicians, (2):579–622, 2019.
  23. D. Lipinski and K. Mohseni. Flow structures and fluid transport for the hydromedusae Sarsia tubulosa and Aequorea victoria. J. Exp. Biology, 212:2436–2447, 2009.
  24. Using Lagrangian coherent structures to analyze fluid mixing by cillia. Chaos, 20:017511, 2010.
  25. Detecting Lagrangian Coherent Structures from Sparse and Noisy Trajectory Data. Journal of Fluid Mechanics, 948, sep 2022.
  26. A. Nath and E. Taylor. k-Median Clustering under Discrete Fréchet and Hausdorff Distances. CoRR, abs/2004.00722, 2020.
  27. Y.K. Ng and S. Leung. Estimating the Finite Time Lyapunov Exponent from Sparse Lagrangian Trajectories. Commun. Comput. Phys., 26(4):1143–1177, 2019.
  28. Lagrangian Coherent Structure Identification Using A Voronoi Tessellation-Based Networking Algorithm. Experiments in Fluids, 56:1–14, 2015.
  29. T. Sapsis and G. Haller. Inertial particle dynamics in a hurricane. Journal of the Atmospheric Sciences, 66:2481–2492, 2009.
  30. Coherent Structure Colouring: Identification Of Coherent Structures From Sparse Data Using Graph Theory. Journal of Fluid Mechanics, 811:468 – 486, 2016.
  31. Definition and properties of Lagrangian coherent structures from finite-time Lyapunov exponents in two-dimensional aperiodic flows. Physica D, 212:271–304, 2005.
  32. A Survey of Trajectory Distance Measures and Performance Evaluation. The VLDB Journal, 29(1):3–32, oct 2019.
  33. Accurate extraction of Lagrangian coherent structures over finite domains with application to flight data analysis over Hong Kong international airport. Chaos, 20:017502, 2010.
  34. Accurate Extraction Of Lagrangian Coherent Structures Over Finite Domains With Application To Flight Data Analysis Over Hong Kong International Airport. Chaos, 20(1):017502, 2010.
  35. W. Tang and T. Peacock. Lagrangian coherent structures and internal wave attractors. Chaos, 20:017508, 2010.
  36. K. Toohey and M. Duckham. Trajectory Similarity Measures. SIGSPATIAL Special, 7(1):43–50, may 2015.
  37. J. Tyler and A. Wittig. An Improved Numerical Method For Hyperbolic Lagrangian Coherent Structures Using Differential Algebra. Journal of Computational Science, 65:101883, 2022.
  38. Discovering Similar Multidimensional Trajectories. Proc. 18th Int’l Conf. on Data Engineering, pages 673–684, 2002.
  39. Parallel clustering for visualizing large scientific line data. IEEE Symposium on Large Data Analysis and Visualization, 1:47–55, 2011.
  40. Discovering Similar Multidimensional Trajectories. 2017 International Joint Conference on Neural Networks (IJCNN), pages 3880–3887, 2017.
  41. G. You and S. Leung. An Eulerian method for computing the coherent ergodic partition of continuous dynamical systems. J. Comp. Phys., 264:112–132, 2014.
  42. G. You and S. Leung. Eulerian based interpolation schemes for flow map construction and line integral computation with applications to coherent structures extraction. J. Sci. Comput., 74(1):70–96, 2018.
  43. G. You and S. Leung. An improved eulerian approach for the finite time lyapunov exponent. J. Sci. Comput., 76(3):1407–1435, 2018.
  44. G. You and S. Leung. Fast Construction of Forward Flow Maps using Eulerian Based Interpolation Schemes. J. Sci. Comput., 82(32), 2020.
  45. G. You and S. Leung. Computing the finite time lyapunov exponent for flows with uncertainties. J. Comput. Phys., 425(109905), 2021.
  46. G. You and S. Leung. Eulerian algorithms for computing some lagrangian flow network quantities. J. Comput. Phys., 445(11020), 2021.
  47. Eulerian methods for visualizating continuous dynamical systems using Lyapunov exponents. SIAM J. Sci. Comp., 39(2):A415–A437, 2017.
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