Effects of ice geometry on airfoil performance using neural networks prediction

Purpose - The purpose of this paper is to describe a methodology for predicting the effects of glaze ice geometry on airfoil aerodynamic coefficients by using neural network (NN) prediction. Effects of icing on angle of attack stall are also discussed. Design/methodology/approach - The typical glaze ice geometry covers ice horn leading-edge radius, ice height, and ice horn position on airfoil surface. By using artificial NN technique, several NNs are developed to study the correlations between ice geometry parameters and airfoil aerodynamic coefficients. Effects of ice geometry on airfoil hinge moment coefficient are also obtained to predict the angle of attack stall. Findings - NN prediction is feasible and effective to study the effects of ice geometry on airfoil performance. The ice horn location and height, which have a more evident and serious effect on airfoil performance than ice horn leading-edge radius, are inversely proportional to the maximum lift coefficient. Ice accretions on the after-location of the upper surface of the airfoil leading edges have the most critical effects on the airfoil performance degradation. The catastrophe of hinge moment and unstable hinge moment coefficient can be used to predict the stall effectively. Practical implications - Since the simulation results of NNs are shown to have high coherence with the tunnel test data, it can be further used to predict coefficients at non-experimental conditions. Originality/value - The simulation method by using NNs here can lay the foundation of the further research about the airfoil performance in different ice cloud conditions through predicting the relations between the ice cloud conditions and ice geometry.