Vector post-processing algorithm for phase discrimination of two-phase PIV

Abstract

A simple phase separation method using vector post-processing techniques is evaluated to measure velocity fields in a bubble plume. To provide for validation, fluorescent seeding is used, and two sets of synoptic images are obtained: mixed-phase images containing bubbles and fluorescent particles, and fluid-phase images containing only fluorescent particles. A third dataset is derived by applying a digital mask to remove bubbles from the mixed-phase images. All datasets are processed using cross-correlation particle image velocimetry (PIV). The resulting vector maps for the raw, mixed-phase data contain both bubble and continuous-phase velocity vectors. To separate the phases, a vector post-processing algorithm applies a maximum velocity threshold for the continuous-phase velocities coupled with the vector median filter to identify remaining bubble-velocity vectors and remove them from the mixed-phase velocity field. To validate the phase separation algorithm, the post-processed fluid-phase vectors are compared to PIV results obtained from both the optically separated and digitally masked data. The comparison among these methods shows that the post-processed mixed-phase data have small errors in regions near some bubbles, but for dilute environmental flows (low void fraction and slip velocity approximately equal to the entrained fluid velocity), the algorithm predicts well both instantaneous and time average statistical quantities. The method is reliable for flows having 10% or less of the field of view occupied by bubbles. The resulting instantaneous data provide information on plume wandering and eddy-size distributions within the bubble plume. By comparison among the datasets, it is shown that the patchiness of the vector-post processed and image masked data limit the diameter of identifiable eddy structures to the average distance between bubbles in the image, and that both datasets give identical probability density functions of eddy size. The optically filtered data have better data coverage and predict a greater probability of larger eddies as compared to the other two datasets.