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
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)
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
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)
Karl, D.M.: A sea of change: biogeochemical variability in the North Pacific Subtropical Gyre. Ecosystems 2(3), 181–214 (1999)
Bordas, R.: Optical measurements in disperse two-phase flows: application to rain formation in cumulus clouds. Ph.D. thesis, University of Magdeburg (2011)
Günther, T., Theisel, H.: Vortex cores of inertial particles. IEEE Trans. Vis. Comput. Graph. 20(12), 2535–2544 (2014). (Proceedings of the IEEE SciVis)
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)
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
Roth, M., Peikert, R.: A higher-order method for finding vortex core lines. In: Proceedings of the IEEE Visualization, pp. 143–150 (1998)
Peikert, R., Roth, M.: The “parallel vectors” operator–a vector field visualization primitive. In: Proceedings of the IEEE Visualization, pp. 263–270 (1999)
Lugt, H.J.: The dilemma of defining a vortex. In: Recent Developments in Theoretical and Experimental Fluid Mechanics, pp. 309–321. Springer (1979)
Robinson, S.K.: Coherent motions in the turbulent boundary layer. Ann. Rev. Fluid Mech. 23(1), 601–639 (1991)
Crowe, C., Sommerfield, M., Tsuji, Y.: Multiphase Flows with Droplets and Particles. CRC Press, Boca Raton (1998)
Haller, G., Sapsis, T.: Where do inertial particles go in fluid flows? Physica D 237, 573–583 (2008)
Sudharsan, M., Brunton, S.L., Riley, J.J.: Lagrangian coherent structures and inertial particle dynamics. ArXiv e-prints 1512.05733 (2015)
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
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)
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)
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)
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)
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)
Vilela, R.D., de Moura, A.P.S., Grebogi, C.: Finite-size effects on open chaotic advection. Phys. Rev. E 73, 026302 (2006)
Günther, T., Theisel, H.: The state of the art in vortex extraction. Comput. Graph. Forum 37(6), 149–173 (2018)
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)
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)
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)
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)
Theisel, H., Seidel, H.-P.: Feature flow fields. In: Proceedings of the Symposium on Data Visualisation, pp. 141–148 (2003)
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)
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)
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)
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)
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)
Günther, T., Theisel, H.: Hyper-objective vortices. IEEE Trans. Vis. Comput. Graph. 26(3), 1532–1547 (2020)
Wiebel, A.: Feature detection in vector fields using the Helmholtz-Hodge decomposition. Diploma thesis, University of Kaiserslautern (2004)
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)
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)
Bujack, R., Hlawitschka, M., Joy, K.I.: Topology-inspired Galilean invariant vector field analysis. In: IEEE Pacific Visualization Symposium, pp. 72–79, April 2016
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)
Günther, T., Gross, M.: Flow-induced inertial steady vector field topology. Comput. Graph. Forum 36(2), 143–152 (2017). (Proceedings of the Eurographics)
Helman, J.L., Hesselink, L.: Representation and display of vector field topology in fluid flow data sets. Computer 22(8), 27–36 (1989)
Rockwood, A., Heaton, K., Davis, T.: Real-time rendering of trimmed surfaces. In: ACM SIGGRAPH Computer Graphics, vol. 23, pp. 107–116. ACM (1989)
Hoschek, J., Lasser, D.: Fundamentals of computer aided geometric design. AK Peters (1993)
Kelley, C.T.: Iterative methods for linear and nonlinear equations. Front. Appl. Math. 16, 575–601 (1995)
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)
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)
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)
Popinet, S.: Free computational fluid dynamics. Cluster World 2, 6 (2004)
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)
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)
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)
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:
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:
Equating Eq. (47) and Eq. (48) gives:
Considering the velocity subspace of Eq. (46):
and multiplying the right hand side with \(\kappa \) gives:
Adding the left hand side to the right hand side and dividing by \(\kappa \):
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):
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):
Vortex Centers in 2D. The motion of inertial particles is described with Eq. (9). We consider the limit \(r\rightarrow 0\) for tracer particles:
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):
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):
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 \(\tilde{\mathbf{b}}^*\):
in the optimal reference frame, i.e., \({\mathbf{d}}\) is the translation rate:
We rearrange the velocity subspace in Eq. (60), denoted as \({\mathbf{c}}^*\) with
and with \({\mathbf{a}}^*= {\mathbf{k}}- \frac{{\mathbf{v}}}{\kappa } = {\mathbf{J}}({\mathbf{u}}-{\mathbf{f}})\) [23] and \({\mathbf{b}}^*\) in Eq. (60) into
Inserting Model 1 with Eqs. (56) and (57) gives for \(r\rightarrow 0\):
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:
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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