Prediction of Transition Point on an Oscillating Airfoil Using Neural Network

Dynamic Neural network was used to minimize the amount of data required to predict the location of transition point on a 2-D oscillatory wing. For this purpose, various experimental tests were carried out on a section of a 660kw wind turbine blade. A multi layer non linear perceptrons network was trained using the output signals of four hot films attached on the upper surface of the model. Results show that using only 50% of the test data, the trained network was able to the transition point with an acceptable accuracy. Moreover, the method can predict the transition points at any position of the wing surface for different Reynolds numbers, amplitudes and initial angles of oscillation, and of course at various reduced frequencies.