adaptive network fuzzy inference system

The aerodynamic coefficients of airfoils are usually calculated through numerical and experimental methods causing time and cost consuming as well as depending on the airfoil surface cover. The estimation of aerodynamic coefficients of fabric covered airfoil through fuzzy logic, neural network and fuzzy-neural methods is the innovation of this research in order to determine a low-cost and fast method for optimal design as well as determining the aerodynamic coefficients of airfoils of vehicles having membrane wings. In the models, subsonic velocity was considered. Reynolds number and angle of attack were assumed as input and the values of lift and drag coefficients were assumed as output. Estimations were made on NACA2418 airfoil data, after which the final error of each method was compared. The mean squared error of lift and drag coefficients were 0.8023 and 4.3451e-04 for fuzzy model 0.0012 and 7.5767e-6 for neural network and 0.0697 and 0.0076 for network-fuzzy inference model respectively. The obtained results indicated good fitting of three studied models and best fitting for neural network model, which confirmed by the lift coefficients obtained from experimental tests done for validation.