Skip to main content
Log in

An Efficient Lagrangian Interpolation Scheme for Computing Flow Maps and Line Integrals using Discrete Velocity Data

  • Published:
Journal of Scientific Computing Aims and scope Submit manuscript

Abstract

We propose a new class of Lagrangian approaches for constructing the flow maps of given dynamical systems. In the case when only discrete velocity data at mesh points is available, an interpolation step will be required. However, in our proposed approaches all particle trajectories share a common global interpolation at each time step and therefore interpolation operations will not increase the overall computational complexity. The old Lagrangian approaches propose to solve the corresponding ordinary differential equations (ODEs) backward in time to obtain the backward flow map. It is inconvenient and not natural, especially when incorporated with certain computational fluid dynamic solvers, because the velocity field needs to be loaded from the terminal time backward to the initial time. In contrast, our proposed approaches for computing the backward flow map propose to solve the corresponding ODEs forward in time which is more practical. We will also extend the proposed approaches to compute line integrals along any particle trajectory. This paper gives a detailed analysis on the computational complexity and error estimate of the proposed Lagrangian approach. Finally, a wide range of applications of our approaches will be given, including the so-called coherent ergodic partition and the high frequency wave propagations based on geometric optic.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Allshouse, M.R., Peacock, T.: Lagrangian based methods for coherent structure detection. Chaos 25(9), 097617 (2015)

    Article  Google Scholar 

  2. Artale, V., Boffetta, G., Celani, A., Cencini, M., Vulpiani, A.: Dispersion of passive tracers in closed basins: Beyond the diffusion coefficient. Phys. Fluids 9(11), 3162–3171 (1997)

    Article  MathSciNet  MATH  Google Scholar 

  3. Aurell, E., Boffetta, G., Crisanti, A., Paladin, G., Vulpiani, A.: Predictability in the large: an extension of the concept of Lyapunov exponent. J. Phys. A: Math. Gen. 30, 1–26 (1997)

    Article  MathSciNet  MATH  Google Scholar 

  4. Besse, N., Mehrenberger, M.: Convergence of classes of high-order semi-Lagrangian schemes for the Vlasov–Poisson system. Math. Comput. 77(261), 93–123 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  5. Brunton, S.L., Rowley, C.W.: Fast computation of finite-time Lyapunov exponent fields for unsteady flows. Chaos 20, 017503 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  6. Candès, E.J., Ying, L.: Fast geodesics computation with the phase flow method. J. Comput. Phys. 220, 6–18 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  7. Cencini, M., Vulpiani, A.: Finite size Lyapunov exponent: review on applications. J. Phys. A: Math. Theor. 46, 254019 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  8. Cerveny, V., Molotkov, I.A., Psencik, I.: Ray Method in Seismology. Univerzita Karlova Press, Karlova (1977)

    Google Scholar 

  9. Enright, D., Losasso, F., Fedkiw, R.: A fast and accurate semi-Lagrangian particle level set method. Compt. Struct. 83, 479–490 (2005)

    Article  MathSciNet  Google Scholar 

  10. Farazmand, M., Haller, G.: Computing Lagrangian coherent structures from their variational theory. Chaos 22, 1–12 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  11. Guo, H., He, W., Peterka, T., Shen, H.-W., Collis, S.M., Helmus, J.J.: Finite-time Lyapunov exponents and Lagrangian coherent structures in uncertain unsteady flows. IEEE Trans. Vis. Comput. Graph. 22(6), 1672–2016 (2016)

    Article  Google Scholar 

  12. Haller, G.: Distinguished material surfaces and coherent structures in three-dimensional fluid flows. Phys. D 149, 248–277 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  13. Haller, G.: Lagrangian structures and the rate of strain in a partition of two-dimensional turbulence. Phys. Fluids A 13, 3368–3385 (2001)

    MathSciNet  Google Scholar 

  14. Haller, G., Yuan, G.: Lagrangian coherent structures and mixing in two-dimensional turbulence. Phys. D 147, 352–370 (2000)

    Article  MathSciNet  MATH  Google Scholar 

  15. Hermandez-Carrasco, I., Lopex, C., Hernansez-Garcia, E., Turiel, A.: How reliable are finite-size Lyapunov exponents for the assessment of ocean dynamics? Ocean Model. 36(3–4), 208–218 (2011)

    Article  Google Scholar 

  16. Lekien, F., Shadden, S.C., Marsden, J.E.: Lagrangian coherent structures in \(n\)-dimensional systems. J. Math. Phys. 48, 065404 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  17. Letz, T., Kantz, H.: Characterization of sensitivity to finite perturbations. Phys. Rev. E. 61, 2533 (2000)

    Article  Google Scholar 

  18. Leung, S.: An Eulerian approach for computing the finite time Lyapunov exponent. J. Comput. Phys. 230, 3500–3524 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  19. Leung, S.: The backward phase flow method for the finite time Lyapunov exponent computations. Chaos 23, 043132 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  20. Leung, S., Qian, J.: Transmission traveltime tomography based on paraxial liouville equations and level set formulations. Inverse Probl. 23, 799–821 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  21. Leung, S., Qian, J.: Eulerian Gaussian beams for Schrödinger equations in the semi-classical regime. J. Comput. Phys. 228, 2951–2977 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  22. Leung, S., Qian, J.: The backward phase flow and FBI-transform-based Eulerian Gaussian beams for the Schrödinger equation. J. Comput. Phys. 229, 8888–8917 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  23. Leung, S., Qian, J., Burridge, R.: Eulerian Gaussian beams for high frequency wave propagation. Geophysics 72, SM61–SM76 (2007)

    Article  Google Scholar 

  24. Mills, P.: Following the Vapour Trail: A Study of Chaotic Mixing of Water Vapour in the Upper Troposphere. Thesis, University of Breman, Germany (2004)

  25. Mills, P.: Isoline retrieval: an optimal sounding method for validation of advected contours. Comput. Geosci. 35, 2020–2031 (2009)

    Article  Google Scholar 

  26. Osher, S.J., Fedkiw, R.P.: Level Set Methods and Dynamic Implicit Surfaces. Springer, New York (2003)

    Book  MATH  Google Scholar 

  27. Osher, S.J., Sethian, J.A.: Fronts propagating with curvature dependent speed: algorithms based on Hamilton–Jacobi formulations. J. Comput. Phys. 79, 12–49 (1988)

    Article  MathSciNet  MATH  Google Scholar 

  28. Osher, S.J., Shu, C.W.: High-order essentially nonoscillatory schemes for Hamilton–Jacobi equations. SIAM J. Num. Anal. 28, 907–922 (1991)

    Article  MathSciNet  MATH  Google Scholar 

  29. Runge, C.: Uber empirische funktionen und die interpolation zwischen aquidistanten ordinaten. Z. Math. Phys. 46, 224–243 (1901)

    MATH  Google Scholar 

  30. Sethian, J.A.: Level Set Methods. Cambridge University Press, Cambridge (1996)

    MATH  Google Scholar 

  31. Shadden, S.C., Lekien, F., Marsden, J.E.: Definition and properties of Lagrangian coherent structures from finite-time Lyapunov exponents in two-dimensional aperiodic flows. Phys. D 212, 271–304 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  32. Staniforth, A., Cote, J.: Semi-Lagrangian integration schemes for atmospheric model—a review. Mon. Weather Rev. 119, 2206–2223 (1991)

    Article  Google Scholar 

  33. Ying, L., Candès, E.J.: The phase flow method. J. Comput. Phys. 220, 184–215 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  34. You, G., Leung, S.: An Eulerian method for computing the coherent ergodic partition of continuous dynamical systems. J. Comput. Phys. 264, 112–132 (2014)

    Article  MathSciNet  MATH  Google Scholar 

Download references

Acknowledgements

The work of You was supported by the National Natural Science Foundation of China (Grant No. 11701287) and the Natural Science Foundation of Jiangsu Province (Grant No. BK20171071). The work of Shi was supported by the Natural Science Foundation of Jiangsu Province (Grant No. BK20160856). The work of Xu was supported by the National Natural Science Foundation of China (Grant No. 61673221).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Guoqiao You.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

You, G., Shi, R. & Xu, Y. An Efficient Lagrangian Interpolation Scheme for Computing Flow Maps and Line Integrals using Discrete Velocity Data. J Sci Comput 76, 120–144 (2018). https://doi.org/10.1007/s10915-017-0620-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10915-017-0620-7

Keywords

Navigation