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Coreline Criteria for Inertial Particle Motion

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Topological Methods in Data Analysis and Visualization VI

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Abstract

Dynamical systems, such as the second-order ODEs that govern the motion of finite-sized objects in fluids, describe the evolution of a state by a trajectory living in a high-dimensional phase space. The high dimensionality leads to visualization challenges and, for the case of inertial particles, multiple models exist that pose different assumptions. In this paper, we thoroughly address the extraction of a specific feature, namely the vortex corelines of inertial particles. Based on a general template model that comprises two of the most commonly used inertial particle ODEs, we first transform their high-dimensional tangent vector field into a Galilean reference frame in which the observed inertial particle flow becomes as steady as possible. In the optimal frame, we derive first-order and second-order vortex coreline criteria, allowing us to extract straight and bent inertial vortex corelines using 3D and 6D parallel vectors operators, respectively. With this, we generalize existing work in multiple ways: not only do we handle two inertial particle models at once, we extend the concept of second-order vortex corelines to the inertial case and make them Galilean-invariant by deriving the criteria from a steady reference frame, rather than from a geometric characterization.

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References

  1. Syal, M., Govindarajan, B., Leishman, J.G.: Mesoscale sediment tracking methodology to analyze brownout cloud developments. In: 66th Annual Forum of Proceedings of the American Helicopter Society (2010)

    Google Scholar 

  2. Sydney, A., Baharani, A., Leishman, J.G.: Understanding brownout using near-wall dual-phase flow measurements. In: 67th Annual Forum of Proceedings of the American Helicopter Society, Virginia Beach, VA, May 2011

    Google Scholar 

  3. Kutz, B.M., Gunther, T., Rumpf, A., Kuhn, A.: Numerical examination of a model rotor in brownout conditions. In: Proceedings of the American Helicopter Society, no. AHS2014-000343 (2014)

    Google Scholar 

  4. Karl, D.M.: A sea of change: biogeochemical variability in the North Pacific Subtropical Gyre. Ecosystems 2(3), 181–214 (1999)

    Article  Google Scholar 

  5. Bordas, R.: Optical measurements in disperse two-phase flows: application to rain formation in cumulus clouds. Ph.D. thesis, University of Magdeburg (2011)

    Google Scholar 

  6. Günther, T., Theisel, H.: Vortex cores of inertial particles. IEEE Trans. Vis. Comput. Graph. 20(12), 2535–2544 (2014). (Proceedings of the IEEE SciVis)

    Article  Google Scholar 

  7. Günther, T., Theisel, H.: Objective vortex corelines of finite-sized objects in fluid flows. IEEE Trans. Vis. Comput. Graph. 25(1), 956–966 (2019). (Proceedings of the IEEE Scientific Visualization 2018)

    Article  Google Scholar 

  8. Sujudi, D., Haimes, R.: Identification of swirling flow in 3D vector fields. Technical report, Department of Aeronautics and Astronautics, MIT (1995). AIAA paper: 95-1715

    Google Scholar 

  9. Roth, M., Peikert, R.: A higher-order method for finding vortex core lines. In: Proceedings of the IEEE Visualization, pp. 143–150 (1998)

    Google Scholar 

  10. Peikert, R., Roth, M.: The “parallel vectors” operator–a vector field visualization primitive. In: Proceedings of the IEEE Visualization, pp. 263–270 (1999)

    Google Scholar 

  11. Lugt, H.J.: The dilemma of defining a vortex. In: Recent Developments in Theoretical and Experimental Fluid Mechanics, pp. 309–321. Springer (1979)

    Google Scholar 

  12. Robinson, S.K.: Coherent motions in the turbulent boundary layer. Ann. Rev. Fluid Mech. 23(1), 601–639 (1991)

    Article  Google Scholar 

  13. Crowe, C., Sommerfield, M., Tsuji, Y.: Multiphase Flows with Droplets and Particles. CRC Press, Boca Raton (1998)

    Google Scholar 

  14. Haller, G., Sapsis, T.: Where do inertial particles go in fluid flows? Physica D 237, 573–583 (2008)

    Article  MathSciNet  Google Scholar 

  15. Sudharsan, M., Brunton, S.L., Riley, J.J.: Lagrangian coherent structures and inertial particle dynamics. ArXiv e-prints 1512.05733 (2015)

  16. Wan, Z.Y., Sapsis, T.P.: Machine learning the kinematics of spherical particles in fluid flows. J. Fluid Mech. 857, R2 (2018). https://doi.org/10.1017/jfm.2018.797

  17. Benzi, R., Biferale, L., Calzavarini, E., Lohse, D., Toschi, F.: Velocity-gradient statistics along particle trajectories in turbulent flows: the refined similarity hypothesis in the Lagrangian frame. Phys. Rev. E 80, 066318 (2009)

    Article  Google Scholar 

  18. Cartwright, J.H.E., Feudel, U., Karolyi, G., Moura, A., Piro, O., Tel, T.: Dynamics of finite-size particles in chaotic fluid flows. In: Thiel, M., Kurths, J., Romano, M.C., Károlyi, G., Moura, A. (eds.) Nonlinear Dynamics and Chaos: Advance and Perspectives. Understanding Complex Systems, pp. 51–87. Springer, Heidelberg (2010)

    Google Scholar 

  19. Bec, J., Biferale, L., Cencini, M., Lanotte, A.S., Toschi, F.: Spatial and velocity statistics of inertial particles in turbulent flows. J. Phys: Conf. Ser. 333(1), 012003 (2011)

    Google Scholar 

  20. Benczik, I.J., Toroczkai, Z., Tel, T.: Selective sensitivity of open chaotic flows on inertial tracer advection: catching particles with a stick. Phys. Rev. Lett. 89, 164501 (2002)

    Article  Google Scholar 

  21. Babiano, A., Cartwright, J.H.E., Piro, O., Provenzale, A.: Dynamics of a small neutrally buoyant sphere in a fluid and targeting in Hamiltonian systems. Phys. Rev. Lett. 84, 5764–5767 (2000)

    Article  Google Scholar 

  22. Vilela, R.D., de Moura, A.P.S., Grebogi, C.: Finite-size effects on open chaotic advection. Phys. Rev. E 73, 026302 (2006)

    Article  Google Scholar 

  23. Günther, T., Theisel, H.: The state of the art in vortex extraction. Comput. Graph. Forum 37(6), 149–173 (2018)

    Article  Google Scholar 

  24. Globus, A., Levit, C., Lasinski, T.: A tool for visualizing the topology of three dimensional vector fields. In: Proceedings of the IEEE Visualization, pp. 33–40 (1991)

    Google Scholar 

  25. Sahner, J., Weinkauf, T., Hege, H.-C.: Galilean invariant extraction and iconic representation of vortex core lines. In: Proceedings of the Eurographics/IEEE VGTC Symposium on Visualization (EuroVis), pp. 151–160 (2005)

    Google Scholar 

  26. Fuchs, R., Peikert, R., Hauser, H., Sadlo, F., Muigg, P.: Parallel vectors criteria for unsteady flow vortices. IEEE Trans. Vis. Comput. Graph. 14(3), 615–626 (2008)

    Article  Google Scholar 

  27. Weinkauf, T., Sahner, J., Theisel, H., Hege, H.-C.: Cores of swirling particle motion in unsteady flows. IEEE Trans. Vis. Comput. Graph. 13(6), 1759–1766 (2007). (Proceedings of the Visualization)

    Article  Google Scholar 

  28. Theisel, H., Seidel, H.-P.: Feature flow fields. In: Proceedings of the Symposium on Data Visualisation, pp. 141–148 (2003)

    Google Scholar 

  29. Günther, T., Schulze, M., Theisel, H.: Rotation invariant vortices for flow visualization. IEEE Trans. Vis. Comput. Graph. 22(1), 817–826 (2016). (Proceedings of the IEEE SciVis 2015)

    Article  Google Scholar 

  30. Günther, T., Gross, M., Theisel, H.: Generic objective vortices for flow visualization. ACM Trans. Graph. 36(4), 141:1–141:11 (2017). (Proceedings of the SIGGRAPH)

    Google Scholar 

  31. Hadwiger, M., Mlejnek, M., Theuÿl, T., Rautek, P.: Time-dependent flow seen through approximate observer killing fields. IEEE Trans. Vis. Comput. Graph. 25(1), 1257–1266 (2019). (Proceedings of IEEE Scientific Visualization 2018)

    Article  Google Scholar 

  32. Chong, M.S., Perry, A.E., Cantwell, B.J.: A general classification of three-dimensional flow fields. Phys. Fluids A 2(5), 765–777 (1990)

    Article  MathSciNet  Google Scholar 

  33. Rojo, I.B., Günther, T.: Vector field topology of time-dependent flows in a steady reference frame. IEEE Trans. Vis. Comput. Graph. 26(1), 280–290 (2019). (Proceedings of the IEEE Scientific Visualization 2019)

    Google Scholar 

  34. Günther, T., Theisel, H.: Hyper-objective vortices. IEEE Trans. Vis. Comput. Graph. 26(3), 1532–1547 (2020)

    Article  Google Scholar 

  35. Wiebel, A.: Feature detection in vector fields using the Helmholtz-Hodge decomposition. Diploma thesis, University of Kaiserslautern (2004)

    Google Scholar 

  36. Wiebel, A., Garth, C., Scheuermann, G.: Computation of localized flow for steady and unsteady vector fields and its applications. IEEE Trans. Vis. Comput. Graph. 13(4), 641 (2007)

    Article  Google Scholar 

  37. Bhatia, H., Pascucci, V., Kirby, R.M., Bremer, P.-T.: Extracting features from time-dependent vector fields using internal reference frames. Comput. Graph. Forum 33(3), 21–30 (2014). (Proceedings of the EuroVis)

    Article  Google Scholar 

  38. Bujack, R., Hlawitschka, M., Joy, K.I.: Topology-inspired Galilean invariant vector field analysis. In: IEEE Pacific Visualization Symposium, pp. 72–79, April 2016

    Google Scholar 

  39. Kim, B., Günther, T.: Robust reference frame extraction from unsteady 2D vector fields with convolutional neural networks. Comput. Graph. Forum 38(3), 285–295 (2019). (Proceedings of the EuroVis)

    Article  Google Scholar 

  40. Günther, T., Gross, M.: Flow-induced inertial steady vector field topology. Comput. Graph. Forum 36(2), 143–152 (2017). (Proceedings of the Eurographics)

    Article  Google Scholar 

  41. Helman, J.L., Hesselink, L.: Representation and display of vector field topology in fluid flow data sets. Computer 22(8), 27–36 (1989)

    Article  Google Scholar 

  42. Rockwood, A., Heaton, K., Davis, T.: Real-time rendering of trimmed surfaces. In: ACM SIGGRAPH Computer Graphics, vol. 23, pp. 107–116. ACM (1989)

    Google Scholar 

  43. Hoschek, J., Lasser, D.: Fundamentals of computer aided geometric design. AK Peters (1993)

    Google Scholar 

  44. Kelley, C.T.: Iterative methods for linear and nonlinear equations. Front. Appl. Math. 16, 575–601 (1995)

    MathSciNet  Google Scholar 

  45. Van Gelder, A., Pang, A.: Using PVsolve to analyze and locate positions of parallel vectors. IEEE Trans. Vis. Comput. Graph. 15(4), 682–695 (2009)

    Article  Google Scholar 

  46. Hofmann, L., Sadlo, F.: The dependent vectors operator. Comput. Graph. Forum 38(3), 261–272 (2019). https://doi.org/10.1111/cgf.13687. (Proceedings of the EuroVis)

    Article  Google Scholar 

  47. Rojo, B.I., Gross, M., Gunther, T.: Visualizing the phase space of heterogeneous inertial particles in 2D flows. Comput. Graph. Forum 37(3), 289–300 (2018). (Proceedings of the EuroVis)

    Article  Google Scholar 

  48. Popinet, S.: Free computational fluid dynamics. Cluster World 2, 6 (2004)

    Google Scholar 

  49. Camarri, S., Salvetti, M.-V., Buffoni, M., Iollo, A.: Simulation of the three-dimensional flow around a square cylinder between parallel walls at moderate Reynolds numbers. In: XVII Congresso di Meccanica Teorica ed Applicata (2005)

    Google Scholar 

  50. Stalling, D., Westerhoff, M., Hege, H.-C.: Amira: a highly interactive system for visual data analysis. In: The Visualization Handbook, pp. 749–767. Elsevier (2005)

    Google Scholar 

  51. Oster, T., Rossl, C., Theisel, H.: Core lines in 3D second-order tensor fields. Comput. Graph. Forum 37(3), 327–337 (2018). (Proceedings of the EuroVis)

    Article  Google Scholar 

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Acknowledgements

The Cylinder flow was numerically simulated with Gerris Flow solver [48], The Delta Wing vector field was kindly provided by Markus Rütten and the Square Cylinder flow was simulated by Camarri et al. [49] and resampled by Tino Weinkauf. The Helicopter flow was simulated by Benjamin Kutz [3]. For all visualizations, we used Amira [50]. This work was supported by the Swiss National Science Foundation (SNSF) Ambizione grant no. PZ00P2_180114 and by ETH Research Grant ETH-07 18-1.

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Appendices

Appendix 1 - Derivation of First-Order 3D Criterion

Next, we show how the 6D parallel vectors condition of the first-order case:

$$\begin{aligned} \tilde{\mathbf{v}}^*\parallel \tilde{\mathbf{J}}^*\tilde{\mathbf{v}}^*\quad \Rightarrow \quad \begin{pmatrix} {\mathbf{v}}+ {\mathbf{d}}\\ {\mathbf{k}}- \frac{{\mathbf{v}}}{\kappa } \end{pmatrix} \parallel \begin{pmatrix} {\mathbf{k}}- \frac{{\mathbf{v}}}{\kappa } \\ \nabla {\mathbf{k}}({\mathbf{v}}+ {\mathbf{d}}) - \frac{{\mathbf{k}}- \frac{{\mathbf{v}}}{\kappa }}{\kappa } \end{pmatrix}. \end{aligned}$$
(46)

can be simplified to a 3D criterion. First, we look at the position subspace. Multiplying with \(\kappa \) and adding \({\mathbf{v}}+ {\mathbf{d}}\) to the right hand side gives:

$$\begin{aligned} {\mathbf{v}}+ {\mathbf{d}}\parallel {\mathbf{k}}- \frac{{\mathbf{v}}}{\kappa }&\quad \Leftrightarrow \quad {\mathbf{v}}+ {\mathbf{d}}\parallel \kappa {\mathbf{k}}- {\mathbf{v}}\end{aligned}$$
(47)
$$\begin{aligned}&\quad \Leftrightarrow \quad {\mathbf{v}}+ {\mathbf{d}}\parallel \kappa {\mathbf{k}}+ {\mathbf{d}}, \end{aligned}$$
(48)

Equating Eq. (47) and Eq. (48) gives:

$$\begin{aligned} {\mathbf{k}}- \frac{{\mathbf{v}}}{\kappa } \parallel \kappa {\mathbf{k}}+ {\mathbf{d}}. \end{aligned}$$
(49)

Considering the velocity subspace of Eq. (46):

$$\begin{aligned} {\mathbf{k}}- \frac{{\mathbf{v}}}{\kappa } \parallel \nabla {\mathbf{k}}\, ({\mathbf{v}}+ {\mathbf{d}}) - \frac{{\mathbf{k}}- \frac{{\mathbf{v}}}{\kappa }}{\kappa }, \end{aligned}$$
(50)

and multiplying the right hand side with \(\kappa \) gives:

$$\begin{aligned} {\mathbf{k}}- \frac{{\mathbf{v}}}{\kappa } \parallel \kappa \nabla {\mathbf{k}}\, ({\mathbf{v}}+ {\mathbf{d}}) - {\mathbf{k}}+ \frac{{\mathbf{v}}}{\kappa } \;. \end{aligned}$$
(51)

Adding the left hand side to the right hand side and dividing by \(\kappa \):

$$\begin{aligned} {\mathbf{k}}- \frac{{\mathbf{v}}}{\kappa } \parallel \nabla {\mathbf{k}}\, ({\mathbf{v}}+ {\mathbf{d}}). \end{aligned}$$
(52)

Finally, substituting Eq. (49) on the left hand side of Eq. (52) and inserting Eq. (48) on the right hand side of Eq. (52) gives Eq. (25):

$$\begin{aligned} \kappa {\mathbf{k}}+ {\mathbf{d}}\parallel \nabla {\mathbf{k}}\, (\kappa {\mathbf{k}}+ {\mathbf{d}}) , \end{aligned}$$
(53)

which is a 3D condition, independent of the particle velocity \({\mathbf{v}}\).

Appendix 2 - Tracer Particles as Limit Case

Next, we show that our inertial first-order and second-order criteria approach the massless case in the limit. The proofs of Model 1 and 2 are analogue. For brevity, we show the derivation for Model 1.

Inertial Motion. First, the motion of inertial particles is consistent with tracer particles for \(r\rightarrow 0\), as shown by Günther and Theisel [6]: Rearranging the velocity subspace of \(\tilde{\mathbf{v}}\) in Eq. (7) for \({\mathbf{v}}\) and substituting in the position subspace of \(\tilde{\mathbf{v}}\) gives with Eq. (9):

$$\begin{aligned} \lim _{r\rightarrow 0} \frac{{\mathrm d}{\mathbf{x}}}{{\mathrm d}t} = {\mathbf{v}}= {\mathbf{u}}({\mathbf{x}},t) \underbrace{- r\frac{{\mathrm d}{\mathbf{v}}}{{\mathrm d}t} + r{\mathbf{g}}}_{0}. \end{aligned}$$
(54)

Vortex Centers in 2D. The motion of inertial particles is described with Eq. (9). We consider the limit \(r\rightarrow 0\) for tracer particles:

$$\begin{aligned} \kappa = r \qquad \kappa {\mathbf{k}}&= {\mathbf{u}}({\mathbf{x}},t) + r {\mathbf{g}}\end{aligned}$$
(55)
$$\begin{aligned} \lim _{r \rightarrow 0} \kappa {\mathbf{k}}&= {\mathbf{u}}({\mathbf{x}},t) . \end{aligned}$$
(56)

With \({\mathbf{d}}= {\mathbf{J}}^{-1}{\mathbf{u}}_t = -{\mathbf{f}}\) from Eq. (15), we insert the limit in Eq. (56) into the general 2D vortex center condition in Eq. (17):

$$\begin{aligned} \kappa {\mathbf{k}}+ {\mathbf{d}}= {\mathbf{0}}\qquad \overset{r\rightarrow 0}{\Rightarrow }\qquad {\mathbf{u}}({\mathbf{x}},t) - {\mathbf{f}}= {\mathbf{0}}, \end{aligned}$$
(57)

which is the Galilean invariant 2D vortex coreline criterion for massless particles by Weinkauf et al. [27], cf. Eq. (55) in [23].

First-Order Corelines in 3D. For the first-order vortex corelines in 3D, we insert the limit in Eq. (56) into the general first-order vortex coreline condition in Eq. (25):

$$\begin{aligned} \kappa {\mathbf{k}}+ {\mathbf{d}}\parallel \nabla {\mathbf{k}}\, (\kappa {\mathbf{k}}+ {\mathbf{d}}) \qquad \overset{r\rightarrow 0}{\Rightarrow }\qquad {\mathbf{u}}- {\mathbf{f}}&\parallel \frac{{\mathbf{J}}}{r} \, ({\mathbf{u}}- {\mathbf{f}}) \end{aligned}$$
(58)
$$\begin{aligned} {\mathbf{u}}- {\mathbf{f}}&\parallel {\mathbf{J}}\, ({\mathbf{u}}- {\mathbf{f}}), \end{aligned}$$
(59)

which is the Galilean invariant first-order 3D vortex coreline criterion for massless particles by Weinkauf et al. [27], cf. Eq. (53) in [23].

Second-Order Corelines in 3D. We consider the transformed velocity \(\tilde{\mathbf{v}}^*\) in Eq. (13) and the rate of acceleration \(\tilde{\mathbf{b}}^*\):

$$\begin{aligned} \tilde{\mathbf{b}}^*= \begin{pmatrix} \nabla {\mathbf{k}}({\mathbf{v}}+{\mathbf{d}}) + \frac{{\mathbf{v}}}{\kappa ^2} - \frac{{\mathbf{k}}}{\kappa } \\ \nabla (\nabla {\mathbf{k}})({\mathbf{v}}+{\mathbf{d}})({\mathbf{v}}+{\mathbf{d}}) - \frac{\nabla {\mathbf{k}}}{\kappa }({\mathbf{v}}+{\mathbf{d}}) + \nabla {\mathbf{k}}({\mathbf{k}}-\frac{{\mathbf{v}}}{\kappa }) - \frac{{\mathbf{v}}}{\kappa ^3} + \frac{{\mathbf{k}}}{\kappa ^2} \end{pmatrix} \end{aligned}$$
(60)

in the optimal reference frame, i.e., \({\mathbf{d}}\) is the translation rate:

$$\begin{aligned} \tilde{\mathbf{v}}^*= \begin{pmatrix} {\mathbf{v}}+{\mathbf{d}}\\ {\mathbf{a}}^*\end{pmatrix}. \qquad \tilde{\mathbf{b}}^*= \begin{pmatrix} {\mathbf{b}}^*\\ {\mathbf{c}}^*\end{pmatrix}. \end{aligned}$$
(61)

We rearrange the velocity subspace in Eq. (60), denoted as \({\mathbf{c}}^*\) with

$$\begin{aligned} {\mathbf{c}}^*= \nabla (\nabla {\mathbf{k}})({\mathbf{v}}+{\mathbf{d}})({\mathbf{v}}+{\mathbf{d}}) - \frac{\nabla {\mathbf{k}}}{\kappa }({\mathbf{v}}+{\mathbf{d}}) + \nabla {\mathbf{k}}({\mathbf{k}}-\frac{{\mathbf{v}}}{\kappa }) - \frac{{\mathbf{v}}}{\kappa ^3} + \frac{{\mathbf{k}}}{\kappa ^2} \end{aligned}$$

and with \({\mathbf{a}}^*= {\mathbf{k}}- \frac{{\mathbf{v}}}{\kappa } = {\mathbf{J}}({\mathbf{u}}-{\mathbf{f}})\) [23] and \({\mathbf{b}}^*\) in Eq. (60) into

$$\begin{aligned} {\mathbf{c}}^*= \nabla (\nabla {\mathbf{k}})({\mathbf{v}}+{\mathbf{d}})({\mathbf{v}}+{\mathbf{d}}) + \nabla {\mathbf{k}}\left( {\mathbf{J}}({\mathbf{u}}-{\mathbf{f}})\right) -\frac{{\mathbf{b}}^*}{\kappa }. \end{aligned}$$
(62)

Inserting Model 1 with Eqs. (56) and (57) gives for \(r\rightarrow 0\):

$$\begin{aligned} {\mathbf{c}}^*&= \frac{1}{r}\nabla {\mathbf{J}}({\mathbf{u}}-{\mathbf{f}})({\mathbf{u}}-{\mathbf{f}}) + \frac{{\mathbf{J}}}{r}\left( {\mathbf{J}}({\mathbf{u}}-{\mathbf{f}})\right) -\frac{{\mathbf{b}}^*}{r} \end{aligned}$$
(63)
$$\begin{aligned} \Leftrightarrow \; r\cdot {\mathbf{c}}^*&= \nabla {\mathbf{J}}({\mathbf{u}}-{\mathbf{f}})({\mathbf{u}}-{\mathbf{f}}) + {\mathbf{J}}\left( {\mathbf{J}}({\mathbf{u}}-{\mathbf{f}})\right) -{\mathbf{b}}^*= {\mathbf{0}}. \end{aligned}$$
(64)

Rearranging for the rate of acceleration \({\mathbf{b}}^*\) gives the material derivative of the steady acceleration \(\frac{D}{Dt}({\mathbf{J}}{\mathbf{u}}^*)\) in the optimal frame:

$$\begin{aligned} \lim _{r\rightarrow 0} {\mathbf{b}}^*= \left( \nabla {\mathbf{J}}{\mathbf{u}}^*+ {\mathbf{J}}{\mathbf{J}}\right) {\mathbf{u}}^*\quad \text {with}\quad {\mathbf{u}}^*={\mathbf{u}}-{\mathbf{f}}. \end{aligned}$$
(65)

Thus, the position subspace simplifies to \({\mathbf{u}}^*\parallel {\mathbf{b}}^*\), which is equivalent to the criterion of Roth and Peikert [9] in the optimal steady reference frame. Thus, all proposed inertial vortex criteria are consistent with the massless case for \(r\rightarrow 0\).

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Rojo, I.B., Günther, T. (2021). Coreline Criteria for Inertial Particle Motion. In: Hotz, I., Bin Masood, T., Sadlo, F., Tierny, J. (eds) Topological Methods in Data Analysis and Visualization VI. Mathematics and Visualization. Springer, Cham. https://doi.org/10.1007/978-3-030-83500-2_8

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