VortexViz: Finding Vortex Boundaries by Learning from Particle Trajectories

• URL: http://arxiv.org/abs/2404.01352v1
• Date: Mon, 1 Apr 2024 05:12:55 GMT
• Title: VortexViz: Finding Vortex Boundaries by Learning from Particle Trajectories
• Authors: Akila de Silva, Nicholas Tee, Omkar Ghanekar, Fahim Hasan Khan, Gregory Dusek, James Davis, Alex Pang,
• Abstract summary: Vortices are studied in various scientific disciplines, offering insights into fluid flow behavior. Visualizing the boundary of vortices is crucial for understanding flow phenomena and detecting flow irregularities. This paper addresses the challenge of accurately extracting vortex boundaries using deep learning techniques.
• Score: 2.96658114892031
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Vortices are studied in various scientific disciplines, offering insights into fluid flow behavior. Visualizing the boundary of vortices is crucial for understanding flow phenomena and detecting flow irregularities. This paper addresses the challenge of accurately extracting vortex boundaries using deep learning techniques. While existing methods primarily train on velocity components, we propose a novel approach incorporating particle trajectories (streamlines or pathlines) into the learning process. By leveraging the regional/local characteristics of the flow field captured by streamlines or pathlines, our methodology aims to enhance the accuracy of vortex boundary extraction.

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