Application and comparison of dynamic mode decomposition methods in the tip leakage cavitation of a hydrofoil case

Abstract

The cavitation of the tip leakage vortex (TLV) induced by tip leakage has always been a difficult problem faced by turbomachinery, and 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 eliminates the noise modes, showing obvious advantages in the reconstruction accuracy of the velocity field. The characteristics of cavitation signals are analyzed, and 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 the two-phase flow field. This is of great significance for accelerating the prediction of multiphase flow fields 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 future scientific research.