Aerodynamic Performance Prediction for Wide-Incidence Turbines Using Graph Neural Network Models Driven by Small-Scale Experimental Data

Abstract

Abstract To rapidly and accurately predict turbine rotor blade losses within a wide range of incidences (−50–30 deg), graph neural networks (GNNs) are utilized to predict the aerodynamic parameters of two-dimensional turbine blades based on a small-scale experimental dataset. By comparing the backpropagation neural network (BPnn) model and computational fluid dynamics (CFD) results, it is demonstrated that GNNs with appropriately designed graph structures can accurately and quickly predict high-fidelity aerodynamic parameters based on limited experimental data. Unlike traditional data-driven modeling approaches, two innovative methods for improving blade profiles into graph structures are proposed. Relatively few input features are used to comprehensively and effectively represent the turbine blade profile by applying blade profile features in the GNN. The research findings indicate that due to the graph structure, which divides the turbine blade profile into five nodes based on five key points, coupled with high-fidelity experimental data and the unique weight updating mechanism of the graph attention network (GAT) model, the GAT-5 model exhibits the best performance among the studied models. Additionally, when assessing unknown validation blade profiles, the GAT-5 model maintains an absolute error below 6% at an incidence angle of 30 deg compared to the experimental results.