This paper aims to reveal the influence of a rigid wall with a gas entrapping hole on the characteristics of the dynamic behavior of a laser-induced bubble collapse. A high-speed camera system was used to record the oscillation process of the laser-induced bubble on a rigid wall with a gas entrapping hole. When a bubble is generated by a laser above the wall with a gas entrapping hole, the entire bubble collapse stays away from the wall or splits into two bubbles because of a radial jet induced by bubble contraction. These two distinctive collapse modes are dependent on the distance between the wall and the bubble. The focus of this study is the quantitative analysis of the jet formation, bubble migration, and oscillation period, and compared with the behavior of the bubble near a rigid wall. The results show that unlike the generation of the bubble near a rigid wall, a rigid wall with a gas entrapping hole affects the morphology of the jet and changes the direction of migration of the bubble, and decreases the oscillation period. Thus, the rigid wall with a gas entrapping hole could be effective for reducing cavitation erosion on the wall surface, which is supported by our experiment results.

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The cavitation of tip leakage vortex (TLV) induced by tip leakage has always been a difficult problem faced by turbomachinery, its flow structure is complex and diverse. How to accurately extract the main structures that affect the cavitating flow of the TLV from the two-phase flow field is a key problem. In this study, the main mode extraction and low order mode reconstruction accuracy of the cavitation flow field of TLV downstream of National Advisory Committee for Aeronautics (NACA)0009 hydrofoil by two dynamic mode decomposition (DMD) methods are compared. The research shows that the main modes extracted by the standard DMD method contain a large number of noise modes, while the sparsity-promoting DMD (SPDMD) eliminates the noise modes, showing obvious advantages in the reconstruction accuracy of the velocity field. The characteristics of cavitation signals are analyzed, the cavitation signals are divided into four categories, which explains the reason why DMD methods have low reconstruction accuracy in cavitation. This study provides a theoretical basis and strong guarantee for the extraction of mode decomposition characteristics of two-phase flow field. This is of great significance for accelerating the prediction of multiphase flow field based on intelligent flow pattern learning in the future. Meanwhile, it also provides a new method and road for the introduction of artificial intelligence technology in the future scientific research.

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