Novel Vision Based Estimation Techniques for the Analysis of Cavitation Bubbles
Mechatronics, MSc. Thesis, 2015
Prof. Dr. Mustafa Ünel (Thesis Advisor), Assoc. Prof. Dr. Ali Koşar, Assist. Prof. Dr. Hüseyin Üvet
Date & Time: August 5th, 2015 – 14:00, Place: FENS 2019
Keywords: Cavitation Bubbles, Cone Angle Estimation, Kalman Filter, Image Segmentation, Visual Tracking, Elliptic Fourier Descriptors
Visualization and analysis of micro/nano structures throughout multiphase flow have received significant attention in recent years due to remarkable advances in micro imaging technologies. In this context, monitoring bubbles and describing their structural and motion characteristics are crucial for hydrodynamic cavitation in biomedical applications.
In this thesis, novel vision based estimation techniques are developed for the analysis of cavitation bubbles. Cone angle of multiphase bubbly flow and distributions of scattered bubbles around main flow are important quantities in positioning the orifice of cavitation generator towards the target and controlling the destructive cavitation effect. To estimate the cone angle of the flow, a Kalman filter which utilizes 3D Gaussian modeling of multiphase flow and edge pixels of the cross-section is implemented. Scattered bubble swarm distributions around main flow are assumed to be Gaussian and geometric properties of the covariance matrix of the bubble position data are exploited. Moreover, a new method is developed to track evolution of single, double and triple rising bubbles during hydrodynamic cavitation. Proposed tracker fuses shape and motion features of the individually detected bubbles and employs the well-known Bhattacharyya distance. Furthermore, contours of the tracked bubbles are modeled using elliptic Fourier descriptors (EFD) to extract invariant properties of single rising bubbles throughout the motion. To verify the proposed techniques, hydrodynamic cavitating bubbles are generated under 10 to 120 bars inlet pressures and monitored via Particle Shadow Sizing (PSS) technique. Experimental results are quite promising.