Data-driven insights into cavitation phenomena: From spatiotemporal features to physical state transitions

The application of data-driven methods to study cavitation flow provides insights into the underlying mechanisms and richer physical details of cavitation phenomena. This paper aims to analyze the physically interpretable multi-state cavitation behavior. Initially, the spatiotemporal features of the cavitation flow are represented as network trajectories using principal component analysis. The k-means++ algorithm is then employed to obtain coarse-grained flow field states, and the centroid of each cluster served as a representative for the attributes of that state. Subsequently, the Markov state model is constructed to capture the dynamic transitions in the cavitation flow field. Through a detailed analysis of the dynamic transition model, the cavitation flow field states with genuine physical mechanisms are refined. Finally, proper orthogonal decomposition (POD) is utilized to extract the flow patterns corresponding to different states. The distribution characteristics of the flow field modes in different states correspond to their physical properties. These data-driven algorithm enables a detailed analysis of the typical states in periodic cavitation processes, such as cavity growth, development, shedding, and collapse, providing a deeper understanding of the cavitation flow characteristics in different typical states.