Research progress in hydrofoil cavitation prediction and suppression methods

To reduce the adverse damage caused by cavitation phenomena to the hydraulic machinery, such as surface erosion of the equipment, increased mechanical vibration, and decreased service life, this review summarizes from the aspects of cavitation instability mechanisms, cavitation prediction methods, and cavitation suppression methods. In terms of cavitation flow instability mechanisms, two main mechanisms that affect the shedding of cloud cavitation, reentrant jet, and bubbly shock wave, were thoroughly summarized. It is pointed out that the shedding behavior of the cavity is greatly influenced by the thickness of the reentrant jet relative to the cavity, and the bubbly shock wave is also one of the important factors in cavitation vortex dynamics. In terms of cavitation prediction methods, a detailed comparison and analysis were made between the traditional cavitation prediction methods based on numerical simulation and the currently popular cavitation prediction methods based on neural networks. The former mainly includes cavitation models and turbulence models, while the latter mainly summarizes the application of chain physics-informed neural network, pressure–velocity network, long short-term memory, and other neural networks in cavitation prediction. It is pointed out that artificial intelligence predictive models have advantages in model order reduction and accurate prediction of cavitation flow field feature parameters. In terms of cavitation suppression methods, active and passive cavitation suppression methods were thoroughly summarized. Finally, based on the current research status of hydrofoil cavitation prediction methods and cavitation suppression methods, this article discusses and looks forward to the direction of development.