Unsteady aerodynamic prediction for iced airfoil based on multi-task learning

Ice accretion on wind turbine blades and wings changes the effective shape of the airfoil and considerably deteriorates the aerodynamic performance. However, the unsteady performance of iced airfoil is often difficult to predict. In this study, the unsteady aerodynamic performance of iced airfoil is simulated under different pitching amplitudes and reduced frequencies. In order to efficiently predict aerodynamic performance under icing conditions, a multi-fidelity reduced-order model based on multi-task learning is proposed. The model is implemented using lift and moment coefficient of clean airfoil as low-fidelity data. Through using few aerodynamic data from iced airfoils as high-fidelity data, the model can achieve aerodynamic prediction for different ice shapes and pitching motions. The results indicate that, compared with single-fidelity and single-task modeling, the proposed model can achieve better accuracy and generalization capability. At the same time, the model can be generalized to different ice shapes, which can effectively improve the unsteady prediction efficiency.