A Hopfield neural network for flow field computation based on particle image velocimetry/particle tracking velocimetry image sequences

A new application of a Hopfield network for the detection of particle pairs in particle image velocimetry/particle tracking velocimetry (PIV/PTV) is described. PIV/PTV are the most advanced techniques for the examination of flow fields. Our aims are to apply these techniques to fluid mechanics and the investigation of hydraulic turbomachinery and artificial heart valves. To obtain correct particle correspondences in subsequent images, a specific cost function is defined and then mapped onto a two-dimensional Hopfield network. First investigations show better performance than conventional techniques for PTV/PIV. In comparison to conventional nearest neighbor techniques, the number of correct particle pairs detected significantly increases, whereas the number of mismatches decreases