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Issue No.06 - June (2013 vol.19)
pp: 1020-1033
J. Clyne , Nat. Center for Atmos. Res., Boulder, CO, USA
P. Mininni , Dept. de Fsica, Univ. de Buenos Aires (UBA), Buenos Aires, Argentina
A. Norton , Nat. Center for Atmos. Res., Boulder, CO, USA
Numerical simulations of turbulent fluid flow in areas ranging from solar physics to aircraft design are dominated by the presence of repeating patterns known as coherent structures. These persistent features are not yet well understood, but are believed to play an important role in the dynamics of turbulent fluid motion, and are the subject of study across numerous scientific and engineering disciplines. To facilitate their investigation a variety of techniques have been devised to track the paths of these structures as they evolve through time. Heretofore, all such feature tracking methods have largely ignored the physics governing the motion of these objects at the expense of error prone and often computationally expensive solutions. In this paper, we present a feature path prediction method that is based on the physics of the underlying solutions to the equations of fluid motion. To the knowledge of the authors the accuracy of these predictions is superior to methods reported elsewhere. Moreover, the precision of these forecasts for many applications is sufficiently high to enable the use of only the most rudimentary and inexpensive forms of correspondence matching. We also provide insight on the relationship between the internal time stepping used in a CFD simulation, and the evolution of coherent structures, that we believe is of benefit to any feature tracking method applicable to CFD. Finally, our method is easy to implement, and computationally inexpensive to execute, making it well suited for very high-resolution simulations.
Equations, Computational fluid dynamics, Tracking, Mathematical model, Aerodynamics, Computational modeling, CFD, Feature tracking, flow visualization, time-varying data
J. Clyne, P. Mininni, A. Norton, "Physically-Based Feature Tracking for CFD Data", IEEE Transactions on Visualization & Computer Graphics, vol.19, no. 6, pp. 1020-1033, June 2013, doi:10.1109/TVCG.2012.171
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