Unsteady Three Dimensional Aerodynamic Load Prediction Using Neural Networks

The focus of the current research is to develop an intelligent design process that uses existing data as a tool for the designers, one that fully utilizes the ability of the computer to interpolate and extrapolate the scattered data. Surface pressure measurements were conducted for a pitch oscillation wing in a subsonic closed circuit wind tunnel. Experimental results have been used to train a multilayer perceptron network. This work indicates that neural networks can reliably predict aerodynamic coefficients and forecast the effects of reduced frequencies on the wind turbine blade performance.