Prediction of Aerodynamic Coefficients Using Neural Networks for Sparse Data

T. Rajkumar and Jorge Bardina

A reliable and fast method of predicting complex aerodynamic coefficients for flight simulation is presented using neural networks. The training data for the neural network is derived from numerical simulations and wind tunnel experiments. The aerodynamic coefficients are modeled as functions of the flow characteistics 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.


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