Novel vision based estimation techniques for the analysis of cavitation bubbles

Official URL: http://risc01.sabanciuniv.edu/record=b1615020 (Table of Contents)

Visualization and analysis of micro/nano structures throughout multiphase ow have received signi cant 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 ow and distributions of scattered bubbles around main ow are important quantities in positioning the ori ce of cavitation generator towards the target and controlling the destructive cavitation e ect. To estimate the cone angle of the ow, a Kalman lter which utilizes 3D Gaussian modeling of multiphase ow and edge pixels of the cross-section is implemented. Scattered bubble swarm distributions around main ow 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.