Comparative Study of Machine Learning Modeling for Unsteady Aerodynamics

Abstract

Modern fighters are designed to fly at high angle of attacks reaching 90 deg as part of their routine maneuvers. These maneuvers generate complex nonlinear and unsteady aerodynamic loading. In this study, different aerodynamic prediction tools are investigated to achieve a model which is highly accurate, less computational, and provides a stable prediction of associated unsteady aerodynamics that results from high angle of attack maneuvers. These prediction tools include Artificial Neural Networks (ANN) model, Adaptive Neuro Fuzzy Logic Inference System (ANFIS), Fourier model, and Polynomial Classifier Networks (PCN). The main aim of the prediction model is to estimate the pitch moment and the normal force data obtained from forced tests of unsteady delta-winged aircrafts performing high angles of attack maneuvers. The investigation includes three delta wing models with 1, 1.5, and 2 aspect ratios with four determined variables: change rate in angle of attack (0 to 90 deg), non-dimensional pitch rate (0 to .06), and angle of attack. Following a comprehensive analysis of the proposed identification methods, it was found that the newly proposed model of PCN showed the least error in modeling and prediction results. Based on prediction capabilities, it is seen that polynomial networks modeling outperformed ANFIS and ANN for the present nonlinear problem.