Reduced-order modelling of unsteady cavitating flow around a Clark-Y hydrofoil

Abstract

This paper proposes a novel approach that combines Proper Orthogonal Decomposition (POD) reduced-order system with Long Short-Term Memory (LSTM) neural network to predict flow velocity. Large Eddy Simulation (LES) is used to simulate the cavitating flow around a NACA66 hydrofoil. POD is adopted to reduce the dimensionality of the high-dimensional data. It was found that 66.81% of the flow field energy and dominant coherent structures can be captured with first eight POD modes. The LSTM network model was further used to predict the temporal data of the POD mode coefficients, and the error of the predicted coefficients was within an acceptable range. The reconstructed flow field agrees well with the real flow field and the cavitation development has also been well illustrated. This method provides a promising and efficient alternative for flow prediction and has potential for applications in fluid dynamics, aerospace engineering, and hydrodynamics.