Extraction of structures from turbulent signals

An automatic procedure based on of the Fuzzy ARTMAP neural network is applied to classify the structure embedded in two-component velocity signals measured in a turbulent wake behind a circular cylinder. A small part of the velocity field in the horizontal plane of the wake recorded at two downstream positions x/D = 30 and 150 was pre-processed to extract a set of relevant patterns from the data in order to train the network. The complete data files were tested with the trained net, obtaining nine different structures: clockwise and anticlockwise eddies, sinks, sources, four types of saddle points and jet-like motions. Comparison of the number of classes and patterns belonging to the same category at at x/D = 30 and 150 shows that the number of structures present in the wake increase with downstream position, i.e. with the development of turbulence. The jets present in the near wake appear in this preliminary analysis to be linked to the formation of double rollers in the far wake.