Unsupervised learning for identifying cavitation stages dominated by re-entrant jets and condensation fronts

作者：Xuesong Wei, Teng Liu, Xun Sun, Benlong Wang, Sivakumar Manickam, Weibin You, Joon Yoon, Hao Zhang · 发表于：AIP Publishing · 年份：2026 · DOI：10.60893/figshare.pof.c.8387608.v1 · 研究领域：Mechanics、Computer science、Biological system、Materials science、Physics、Acoustics

Understanding and identifying detachment mechanisms of unsteady partial cavitation, successively governed by re-entrant jet and condensation front mechanisms, is essential for effective control and utilization of cavitating flows. In this study, a machine learning-based framework is proposed to identify the development stages of partial cavitation in an axisymmetric Venturi by integrating dimensionality reduction with unsupervised clustering, without requiring prior labeling or empirical assumptions. High-speed imaging snapshots of cavitating flow field at σ = 0.56 were analyzed using spectral proper orthogonal decomposition (SPOD) combined with t-distributed stochastic neighbor embedding (t-SNE). This approach reduced the flow field dimensionality from 104,320 to 3 while preserving dominant spatiotemporal features of cavitation evolution. Subsequently, three unsupervised clustering algorithms, density peaks clustering (DPC), K-means (KM), and mean shift (MS), were independently applied to identify distinct cavitation development stages. Among them, the DPC demonstrated superior performance, successfully identifying six cavitation stages: cavitation inception, sheet cavity growth, re-entrant jet development, local sheet cavity shedding, cloud collapse accompanied by condensation front propagation, and rapid retraction of residual cavity. In contrast, both the KM and MS failed to clearly distinguish between the first and sixth stages, and the KM also shows ambiguity in diffe...