Data-driven modal decomposition of transient cavitating flow

Abstract

The objective of this paper is to identify the dominant coherent structures within cavitating flow around a Clark-Y hydrofoil using two data-driven modal decomposition methods, proper orthogonal decomposition (POD) and dynamic mode decomposition (DMD). A snapshot data sequence is obtained using a large eddy simulation and the interaction between cavitation and the vortex during cloud cavity shedding evolution is investigated. Modal decomposition via POD and DMD indicates that the dominant coherent structures include the large-scale cavity–vortex, re-entrant jet, shear layer, and small-scale vortex in the wake. In addition, the flow field can be reconstructed from the most energetic POD or DMD modes. The errors in the flow reconstructions produced using the first four POD modes, first eight POD modes, and first eight DMD modes are 3.884%, 3.240%, and 3.889%, respectively. Furthermore, transient cavitating flow can be predicted via the DMD method with an error of 8.081%. The largest errors in the reconstructed and predicted results occur mostly in the shear layer, trailing edge, and near wake. POD and DMD provide accurate and practically beneficial techniques for understanding cavitating flow, although substantial challenges remain with regard to predicting this intense nonlinear system.