Reduced Order Data-Driven Analysis of Cavitating Flow over Hydrofoil with Machine Learning

Abstract

Predicting the flow situation of cavitation owing to its high-dimensional nonlinearity has posed great challenges. To address these challenges, this study presents a novel reduced order modeling (ROM) method to accurately analyze and predict cavitation flow fields under different conditions. The proposed ROM decomposes the flow field into linearized low-order modes while maintaining its accuracy and effectively reducing its dimensionality. Specifically, this study focuses on predicting cavitation on the Clark-Y hydrofoil using a combination of numerical simulation, proper orthogonal decomposition (POD), and neural networks. By analyzing different cavitation conditions, the results revealed that the POD method effectively reduces the order of the cavity flow field while achieving excellent flow field reconstruction. Notably, the zeroth- and first-order modes are associated with attachment cavitation, while the second-, third- and fourth-order modes correspond to cavitation shedding. Additionally, the fifth- and sixth-order modes along the hydrofoil surface are associated with the backward jet flow. To predict the conditions of high-energy modes, the neural network proved to be more effective, exhibiting excellent performance in stable attached cavitation. However, for cloud cavitation, the accuracy of the neural network model requires further improvement. This study not only introduces a novel approach for predicting cavitation flow fields but also highlights new challenges that will require continuous attention in future research endeavors.

Keywords

• cavitation
• proper orthogonal decomposition
• neural network
• hydrofoil
• machine learning