Vortex Lens: Interactive Vortex Core Line Extraction using Observed Line Integral Convolution

Peter Rautek, Xingdi Zhang, Bernhard Woschizka, Thomas Theussl, Markus Hadwiger

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

This paper describes a novel method for detecting and visualizing vortex structures in unsteady 2D fluid flows. The method is based on an interactive local reference frame estimation that minimizes the observed time derivative of the input flow field v(x,t). A locally optimal reference frame w(x,t) assists the user in the identification of physically observable vortex structures in Observed Line Integral Convolution (LIC) visualizations. The observed LIC visualizations are interactively computed and displayed in a user-steered vortex lens region, embedded in the context of a conventional LIC visualization outside the lens. The locally optimal reference frame is then used to detect observed critical points, where v = w, which are used to seed vortex core lines. Each vortex core line is computed as a solution of the ordinary differential equation (ODE) w?(t) = w(w(t),t), with an observed critical point as initial condition (w(t0),t0). During integration, we enforce a strict error bound on the difference between the extracted core line and the integration of a path line of the input vector field, i.e., a solution to the ODE v?(t) = v(v(t),t). We experimentally verify that this error depends on the step size of the core line integration. This ensures that our method extracts Lagrangian vortex core lines that are the simultaneous solution of both ODEs with a numerical error that is controllable by the integration step size.

Original languageEnglish (US)
Pages (from-to)55-65
Number of pages11
JournalIEEE Transactions on Visualization and Computer Graphics
Volume30
Issue number1
DOIs
StatePublished - Jan 1 2024

Keywords

  • Flow visualization
  • Lie algebras
  • objectivity
  • observers
  • reference frames
  • visual lens metaphors
  • vortex detection

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Computer Graphics and Computer-Aided Design

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