New tracking algorithm for stereoscopic imaging velocimetry

摘要

Abstract: Particle tracking is an essential step in data process of stereoscopic imaging velocimetry. It is known that in particle tracking velocimetry, part of the individual particle images or equivalently data points are likely to be lost when a flow field is seeded with a high-density particles. In order to maximize the data point-recovery and to enhance the measurement reliability, the neural networks are employed to attain a globally-optical solution in finding appropriate particle tracks. Our investigation indicates that the neural networks offer very good potential for performance enhancement and has proven to be very useful for stereoscopic imaging velocimetry. !9