Abstract
A pattern recognition technique for the identification of critical points in turbulent flows based on fuzzy C-means clustering is presented. The technique deals with, in particular, the problem that experimental data is normally obtained with coarsely spaced sensors making the identification of critical points uncertain. The technique was applied to hot-wire data from two different turbulent flows; one is the highly ordered wake of a mesh strip, while the second is the wake of a circular cylinder, which has a higher level of disorder. Foci and saddle points in the velocity fields were accurately detected and classified.
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