Ar e liable and fast method of predicting complex aerodynamic coefficients for flight simulation is presented using neural networks. The training data for the neural network is derive df rom numerical simulations and wind-tunnel experiments. The aerodynamic coefficients are modele da s f unctions of the flow characteristics and the control surfaces of the vehicle. The basic coefficients of lift, drag and pitching moment are expressed as function of angles of attack and Mach number. The modeled and training aerodynamic coefficients show good agreement. This method shows excellent potential for rapid development of aerodynamic models for flight simulation.
['Thirumalainambi Rajkumar', 'Jorge Bardina']
Prediction of Aerodynamic Coefficients Using Neural Networks for Sparse Data