Phase discrimination and a high accuracy algorithm for PIV image processing of particle–fluid two-phase flow inside high-speed rotating centrifugal slurry pump

Abstract

PIV technology is an efficient and powerful measurement method to investigate the characteristics of fluid flow field. But for PIV particle image post-processing, some problems still exit in two-phase particles discrimination and velocity field algorithm, especially for high-speed rotating centrifugal slurry pump. In this study, through summarization and comparison of the various phase discrimination methods, we proposed a two-phase identification method based on statistics of gray-scale level and particle size. The assessment of performance through experimental PIV images shows that a satisfying effect for particle identification. For high speed rotation of the impeller, a combination of adaptive cross-correlation window deformation algorithm and multistage grid subdivision is presented. The algorithm is applied to experimental PIV images of solid–liquid two-phase flow in a centrifugal slurry pump, the results show that the algorithm in the present study has less pseudo vector number and more matching particle pairs than those of fixed window and window translation methods, having the ability to remove pseudo vector efficiently. It confirmed that the algorithm proposed in the present study has good performance and reliability for PIV image processing of particle–fluid two-phase flow inside high-speed rotating centrifugal slurry pump.