A particle-based image segmentation method for phase separation and interface detection in PIV images of immiscible multiphase flow
Measurement Science and Technology
Particle image velocimetry (PIV) is a valuable tool for experimentally studying multiphase flows. In order to distinguish the flow dynamics of each individual phase, proper image segmentation must be performed. In immiscible multiphase flows, the fidelity of phase segmentation is directly linked to the accuracy of the interface detection. This paper reports a novel method for robust phase separation and phase boundary identification applicable to particle images acquired via PIV. The method, which requires a seeding density differentiation between the phases, is based on particle detection and triangular meshing. In this method, tracer particles in all seeded phases are first identified, and the coordinates of the particle locations are then used to formulate a 2D unstructured mesh, where the triangular grids provide the basis for phase separation and the outer edges provide a basis for interface detection. This paper presents a parametric analysis on synthetic particle images to assess the performance of the method and to compare the results to existing approaches. In addition, an application to experimentally generated images is reported. These results show that this method can successfully track the complex interface evolution in a particularly challenging flow system consisting of immiscible multiphase flow in porous media.
multiphase flow, particle detection, particle image velocimetry, phase discrimination, porous media
Yaofa Li, Gianluca Blois, Farzan Kazemifar, and Kenneth T. Christensen. "A particle-based image segmentation method for phase separation and interface detection in PIV images of immiscible multiphase flow" Measurement Science and Technology (2021). https://doi.org/10.1088/1361-6501/abf0dc