Boosting Techniques for Physics-Based Vortex Detection (pages 282–293)
L. Zhang, Q. Deng, R. Machiraju, A. Rangarajan, D. Thompson, D. K. Walters and H.-W. Shen
Version of Record online: 15 JAN 2014 | DOI: 10.1111/cgf.12275
Robust automated vortex detection algorithms are needed to facilitate the exploration of large-scale turbulent fluid flow simulations. Unfortunately, robust non-local vortex detection algorithms are computationally intractable for large data sets and local algorithms, while computationally tractable, lack robustness. We argue that the deficiencies inherent to the local definitions occur because of two fundamental issues: the lack of a rigorous definition of a vortex and the fact that a vortex is an intrinsically non-local phenomenon. As a first step towards addressing this problem, we demonstrate the use of machine learning techniques to enhance the robustness of local vortex detection algorithms.