A novel approach to predict the unsteady aerodynamic behavior of a delta wing undergoing pitching motion

In this paper, a new approach based on generalized regression neural networks (GRNNs) has been proposed to predict the unsteady forces and moments on a 70deg swept wing undergoing sinusoidal pitching motion. Extensive wind tunnel testing results were being used for training the network and also for verification of the values predicted by this approach. The generalized regression neural network (GRNN) has been trained by the aforementioned experimental data and subsequently was used as a prediction tool to determine the unsteady longitudinal coefficient of the pitching delta wing for various reduced frequencies. The obtained results are in a good agreement with those determined by an experimental method