Bubble detection and tracking, which is essential for the enhancement of gas-liquid two-phase flow applications, is difficult due to optical noise surrounding bubble boundaries. Two-phase flow applications involving unconventional wavy channels further complicate these tasks by introducing non-linearity. In this work, the authors apply a path-based approach to transform a sinusoidal channel into a straightened horizontal channel for bubble detection and tracking. Segmented morphological operations are applied to the linear channel to identify each bubble as a single entity and to eliminate noise caused due to illumination conditions. The bubbles are detected through blob analysis and are associated across different frames based on motion, estimated by Kalman filter in both sinusoidal and path-based approach to the sinusoidal channel. The results show that the proposed path-based straightened channel gives more accurate results for the benchmark parameters of bubble count and bubble velocities than the sinusoidal channel with a percentage error reduction of 31.61% for bubble count and 27.36% for velocity.
ASJC Scopus subject areas
- Signal Processing
- Computer Vision and Pattern Recognition
- Electrical and Electronic Engineering