A hybrid original approach for prediction of the aerodynamic coefficients of an ATR-42 scaled wing model

Abstract

A new approach for the prediction of lift, drag, and moment coefficients is presented. This approach is based on the support vector machines (SVMs) methodology and an optimization meta-heuristic algorithm called extended great deluge (EGD). The novelty of this approach is the hybridization between the SVM and the EGD algorithm. The EGD is used to optimize the SVM parameters. The training and validation of this new identification approach is realized using the aerodynamic coefficients of an ATR-42 wing model. The aerodynamic coefficients data are obtained with the XFoil software and experimental tests using the Price–Païdoussis wind tunnel. The predicted results with our approach are compared with those from the XFoil software and experimental results for different flight cases of angles of attack and Mach numbers. The main purpose of this methodology is to rapidly predict aircraft aerodynamic coefficients.