Normal Force Coefficient Prediction on a Tail Planform using aGRNN Algorithm

A new approach based on a Generalized Regression Neural Network (GRNN) has been proposed to predict the normal force coefficient on a wing-tail combination in low subsonic flow. Extensive wind tunnel results were used for training the network and verification of the values predicted by this approach. GRNN has been trained by theaforementioned experimental data and subsequently was used as a prediction tool to determine the normal force coefficient. Most of the previous applications of the GRNN in prediction problems were restricted to single or limited outputs, while in the present method the entire normal force coefficient was predicted at once. This highly decreases the calculation time while preserving a remarkable degree of accuracy. The wind tunnel results verify the accuracy of the data offered by the GRNN, which indicates that the present prediction and optimization tool provides sufficient accuracy with modest amount of experimental data. With the above method and using the data, Normal force coefficient graphs according to angles of attack and tail deflection angles were produced. These graphs were being drawn at different smoothness parameters (б). Finally, an optimum quantity for б was selected and Results were compared. Predictions have the least errors at the optimum б.

کلیدواژه ها:

GRNN ، Force ، Prediction ، Smoothness