Cavitation is a process of liquid evaporation, bubble or vapor sheet formation, and further collapse of vapor structures, which plays a destructive role in many industrial applications. In marine transport and hydraulic machinery, cavitation usually occurs nearby the surface of a ship propeller and rudder, impeller blades in a pump, and distributor vanes and runner blades in a hydroturbine and causes various undesirable effects such as vibrations of frameworks and/or moving parts, material erosion, and noise enhancement. Based on an extensive literature review, this research is aimed at an experimental investigation of a passive approach to control cavitation on a benchmark hydrofoil using a wedge-type vortex generator in different flow regimes with a high Reynolds number. In this study, we employed a high-speed imaging method to explore the spatial patterns and time evolutions of cavitation structures and utilized a hydroacoustic pressure transducer to record and analyze local pressure pulsations due to the collapse of the cavities in the hydrofoil wake region. The results show that the examined control technique is quite effective and capable of hindering the formation of cloud cavities and reducing the amplitude of pressure pulsations associated with unsteady cavitation dynamics. This study provides important experimental information, which can be useful for improving industrial technologies and for promoting new developments in this particular research field.

Two-phase bubble flows have been used in many technological and energy processes as processing oil, chemical and nuclear reactors. This explains large interest to experimental and numerical studies of such flows last several decades. Exploiting of optical diagnostics for analysis of the bubble flows allows researchers obtaining of instantaneous velocity fields and gaseous phase distribution with the high spatial resolution non-intrusively. Behavior of light rays exhibits an intricate manner when they cross interphase boundaries of gaseous bubbles hence the identification of the bubbles images is a complicated problem. This work presents a method of bubbles images identification based on a modern technology of deep learning called convolutional neural networks (CNN). Neural networks are able to determine overlapping, blurred, and non-spherical bubble images. They can increase accuracy of the bubble image recognition, reduce the number of outliers, lower data processing time, and significantly decrease the number of settings for the identification in comparison with standard recognition methods developed before. In addition, usage of GPUs speeds up the learning process of CNN owning to the modern adaptive subgradient optimization techniques.