A deep learning-based optimization framework of two-dimensional hydrofoils for tidal turbine rotor design

Abstract

Convolutional Neural Network (CNN) is a commonly used deep learning algorithm due to its excellent capability in identification of structural features and parameter predictions in many domains. In addition, it has incomparable advantages of high analysis efficiency and generalization performance. However, it has been questioned in the research community on whether CNN method can be applied to effectively predict hydrofoil performance for hydraulic machinery design. To this end, this paper demonstrates a novel optimization platform using CNN for hydrofoil performance prediction, which can effectively and accurately obtain the optimized hydrofoils results in aid of the structural design of tidal turbine. The prediction model uses signed distance function (SDF) to graphically represent the shape of the hydrofoil which is subsequently imported into CNN as the network input. Three different hydrofoil performance properties including the lift coefficient, drag coefficient and pressure coefficient of surface are used as output to train the neural network. In order to guarantee the accuracy of the forecasting model, Computational Fluid Dynamics (CFD) method characterized by high precision is applied to generate the dataset for neural network training. The results show that it can accurately predict the hydrodynamic parameters at a lower angle of attack with extremely short period of time. On top of the established hydrofoil performance prediction model, the Pareto curve of the optimized hydrofoils is obtained and applied to the design of 3D horizontal axis tidal turbine (HATT) blades. It proves that the optimization platform is effective and versatile in a manner that achieves both accurate and rapid prediction/optimization of the hydrofoil, which greatly facilitates to apply it for the tidal turbine rotor design.