Hostname: page-component-78c5997874-lj6df Total loading time: 0 Render date: 2024-11-04T20:09:14.709Z Has data issue: false hasContentIssue false

Detecting vortices in fluid dynamics simulations using computer vision

Published online by Cambridge University Press:  20 January 2023

Thomas Rometsch*
Affiliation:
Universität Tübingen Auf der Morgenstelle 10, 72076 Tübingen, Germany email: [email protected]
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

Vortices are patches of fluid revolving around a central axis. They are ubiquitous in fluid dynamics. To the human eye, detecting vortices is a trivial task thanks to our inherent ability to identify patterns. To solve this task automatically, we developed the Vortector pipeline which was used to identify and characterize vortices in around one million snapshots of planet-disk interaction simulations in the context of planet formation. From the emergence of two regimes of vortex lifetime, one of which shows very long-lived vortices, we conclude that future resolved disk observations will predominantly detect vortices in the outer parts of protoplanetary disks.

Type
Contributed Paper
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright
© The Author(s), 2023. Published by Cambridge University Press on behalf of International Astronomical Union

References

Bae, J., Zhu, Z., & Hartmann, L. 2016, ApJ, 819, 134 10.3847/0004-637X/819/2/134CrossRefGoogle Scholar
De Val-Borro, M., Artymowicz, P., D’Angelo, G. & Peplinski, A. 2007, A&A, 471, 1043 Google Scholar
Gammie, C. F. 2001, ApJ, 553, 174 10.1086/320631CrossRefGoogle Scholar
Godon, P. & Livio, M. 1999, ApJ, 523, 350 10.1086/307720CrossRefGoogle Scholar
Goodman, J., Narayan, R., & Goldreich, P. 1987 MNRAS 225, 695711 10.1093/mnras/225.3.695CrossRefGoogle Scholar
Les, R., & Lin, M.-K. 2015, MNRAS, 450, 1503 10.1093/mnras/stv712CrossRefGoogle Scholar
Lesur, G., & Papaloizou, J. C. B. 2009, A&A, 498, 1 Google Scholar
Lin, M.-K., &, Pierens, A. 2018, MNRAS, 478, 575-59110.1093/mnras/sty947CrossRefGoogle Scholar
Lovelace, R. V. E., Li, H., Colgate, S. A., & Nelson, A. F. 1999, ApJ, 513, 805 10.1086/306900CrossRefGoogle Scholar
Masset, F. 2000, A&AS, 141, 165 Google Scholar
Mignone, A., Bodo, G., Massaglia, S., et al. 2007, ApJS, 170, 228 10.1086/513316CrossRefGoogle Scholar
Miranda, R., & Rafikov, R. R. 2020, ApJ, 904, 121 10.3847/1538-4357/abbee7CrossRefGoogle Scholar
Pérez, L. M., Benisty, M., Andrews, S. M., et al. 2018, 2018 ApJ, 869, L50 10.3847/2041-8213/aaf745CrossRefGoogle Scholar
Rometsch, T., Ziampras, A., Kley, W. & Béthune, W. 2021, A&A, 656, A130 Google Scholar
Shakura, N. I., & Sunyaev, R. A. 1973, A&A, 500, 33 Google Scholar
Tarczay-Nehéz, D., Regály, Z., & Vorobyov, E. 2020, MNRAS, 493, 3014 10.1093/mnras/staa364CrossRefGoogle Scholar
Marel, N. v. d., Dishoeck, E. F. v., Bruderer, S. et al. 2013, Science, 340, 1199CrossRefGoogle Scholar
Ziampras, A., Kley, W., & Dullemond, C. P. 2020, A&A, 637, A50 Google Scholar