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

Abstract

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.

Reduced Order Data-Driven Analysis of Cavitating Flow over Hydrofoil with Machine Learning

“…In this study, the case of the Clark-Y hydrofoil model is used to examine the characteristics of surface low-pressure region cavitation and its shedding behavior [49]. Figure 4 shows the computational domain, and the specific values of each linear dimension are presented in Table 1.…”

“…To verify the accuracy of the CFD convection field prediction, a comparative analysis was conducted between CFD data and test data [49] at a cavitation number σ = 0.8, as shown in Figure 7. The main focus was on comparing the distribution of time-averaged velocity off the wall at five different positions on the hydrofoil surface.…”

“…Resseguier et al [48] introduced a random model based on the POD, which improved the accuracy and stability of small-scale turbulent flow analysis. Although POD has gained wide applications, challenges persist in the mode decomposition of two-phase flow fields, mainly arising from the complexity of flow field information (discussed in the next chapter) [49]. This complexity presents obstacles to machine learning and requires further exploration.…”