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Detection of evolving Lagrangian coherent structures: A multiple object tracking approach

Theodore MacMillan

Nicholas T. Ouellette

David H. Richter*

  • Department of Aerospace and Mechanical Engineering, University of Notre Dame, Notre Dame, Indiana 46656, USA

  • Department of Civil and Environmental Engineering, Stanford University, Stanford, California 94305, USA

  • Department of Civil and Environmental Engineering and Earth Sciences, University of Notre Dame, Notre Dame, Indiana 46656, USA

  • *drichte2@nd.edu
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Phys. Rev. Fluids 5, 124401 – Published 21 December, 2020

DOI: https://doi.org/10.1103/PhysRevFluids.5.124401

Abstract

Recently, advances in techniques for the detection of Lagrangian coherent structures (LCSs) have driven forward understanding of kinematics and particle transport in a variety of flows. One major shortcoming of these techniques, however, is the lack of an objective procedure for identifying time scales of interest, or an ability to characterize the lives, deaths, or age of coherent structures, especially when relevant flow time scales are larger than the time scales associated with coherence. Here, we address these issues by proposing a multiple object tracking framework frequently used in video analysis to extend the use of existing LCS detection algorithms, allowing coherence time scales to be considered independently of the overall flow evolution time scale and revealing dynamics of the LCSs. Our approach enables the detection of evolving Lagrangian coherent structures, which are created and destroyed throughout the larger time scales of analysis of complex flows.

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References (21)

  1. G. Haller, Lagrangian coherent structures, Annu. Rev. Fluid Mech. 47, 127 (2015).
  2. F. J. Beron-Vera, A. Hadjighasem, Q. Xia, M. J. Olascoaga, and G. Haller, Coherent Lagrangian swirls among submesoscale motions, Proc. Natl. Acad. Sci. 116, 18251 (2018).
  3. M. Serra, P. Sathe, I. Rypina, A. Kirincich, S. D. Ross, P. Lermusiaux, A. Allen, T. Peacock, and G. Haller, Search and rescue at sea aided by hidden flow structures, Nat. Commun. 11, 2525 (2020).
  4. A. Hadjighasem, D. Karrasch, H. Teramoto, and G. Haller, Spectral-clustering approach to Lagrangian vortex detection, Phys. Rev. E 93, 063107 (2016).
  5. U. von Luxburg, A tutorial on spectral clustering, Stat. Comput. 17, 395 (2007).
  6. K. L. Schlueter-Kuck and J. O. Dabiri, Identification of individual coherent sets associated with flow trajectories using coherent structure coloring, Chaos 27, 091101 (2017).
  7. G. Haller, A. Hadjighasem, M. Farazmand, and F. Huhn, Defining coherent vortices objectively from the vorticity, J. Fluid Mech. 795, 136 (2016).
  8. G. Froyland and K. Padberg-Gehle, A rough-and-ready cluster-based approach for extracting finite-time coherent sets from sparse and incomplete trajectory data, Chaos 25, 087406 (2015).
  9. M. R. Allshouse and T. Peacock, Lagrangian based methods for coherent structure detection, Chaos 25, 097617 (2015).
  10. A. Hadjighasem, M. Farazmand, D. Blazevski, G. Froyland, and G. Haller, A critical comparison of Lagrangian methods for coherent structure detection, Chaos 27, 53104 (2017).
  11. C. Schneide, A. Pandey, K. Padberg-Gehle, and J. Schumacher, Probing turbulent superstructures in Rayleigh-Benard convection by Lagrangian trajectory clusters, Phys. Rev. Fluids 3, 113501 (2018).
  12. W. Luo, J. Xing, A. Milan, X. Zhang, W. Liu, X. Zhao, and T.-K. Kim, Multiple object tracking: A literature review, arXiv:1409.7618.
  13. N. T. Ouellette, H. Xu, and E. Bodenschatz, A quantitative study of three-dimensional Lagrangian particle tracking algorithms, Exp. Fluids 40, 301 (2006).
  14. B. Sahbani and W. Adiprawita, Kalman filter and iterative-Hungarian algorithm implementation for low complexity point tracking as part of fast multiple object tracking system, 2016 6th International Conference on System Engineering and Technology (ICSET) (IEEE, Piscataway, NJ, 2016), pp. 109–115.
  15. X. Weng, J. Wang, D. Held, and K. Kitani, 3D multi-object tracking: A baseline and new evaluation metrics, arXiv:1907.03961.
  16. L. Zelnik-Manor and P. Perona, Self-tuning spectral clustering, Adv. Neural Inf. Process. Syst. 17, 1601 (2005).
  17. H. W. Kuhn, The Hungarian method for the assignment problem, Nav. Res. Logistics Q. 2, 83 (1955).
  18. L. M. Beal, W. P. De Ruijter, A. Biastoch, R. Zahn, M. Cronin, J. Hermes, J. Lutjeharms, G. Quartly, T. Tozuka, S. Baker-Yeboah, T. Bornman, P. Cipollini, H. Dijkstra, I. Hall, W. Park, F. Peeters, P. Penven, H. Ridderinkhof, and J. Zinke, On the role of the Agulhas system in ocean circulation and climate, Nature 472, 429 (2011).
  19. D. H. Kelley and N. T. Ouellette, Onset of three-dimensionality in electromagnetically driven thin-layer flows, Phys. Fluids 23, 45103 (2011).
  20. K. L. Schlueter-Kuck and J. O. Dabiri, Coherent structure colouring: Identification of coherent structures from sparse data using graph theory, J. Fluid Mech. 811, 468 (2019).
  21. L. Fang, S. Balasuriya, and N. T. Ouellette, Local linearity, coherent structures, and scale-to-scale coupling in turbulent flow, Phys. Rev. Fluids 4, 014501 (2019).

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Phys. Rev. Fluids 5, 124401– Published 21 December, 2020
  • Received 19 August 2020
  • Accepted 30 November 2020
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