VortexViz: Finding Vortex Boundaries by Learning from Particle Trajectories

TL;DR

This paper introduces VortexViz, a deep learning method that improves vortex boundary detection in fluid flows by utilizing particle trajectories, offering more accurate visualization of vortices compared to traditional velocity-based approaches.

Contribution

The paper presents a novel deep learning approach that incorporates particle trajectories into vortex boundary detection, enhancing accuracy over existing velocity-based methods.

Findings

Improved vortex boundary detection accuracy

Effective use of particle trajectories in deep learning

Enhanced visualization of flow vortices

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.