Data-driven methods for low-dimensional representation and state identification for the spatiotemporal structure of cavitation flow fields

Computational Fluid Dynamics (CFD) generates high-dimensional spatiotemporal data. The data-driven method approach to extracting physical information from CFD has attracted widespread concern in fluid mechanics. While good results have been obtained for some benchmark problems, the performance on complex flow field problems has not been extensively studied. In this paper, we use a dimensionality reduction approach to preserve the main features of the flow field. Based on this, we perform unsupervised identification of flow field states using a clustering approach that applies data-driven analysis to the spatiotemporal structure of complex three-dimensional unsteady cavitation flows. The result shows that the data-driven method can effectively represent the changes in the spatial structure of the unsteady flow field over time and to visualize changes in the quasi-periodic state of the flow. Furthermore, we demonstrate that the combination of principal component analysis and Toeplitz inverse covariance-based clustering can identify different states of the cavitated flow field with high accuracy. This suggests that the method has great potential for application in complex flow phenomena.