An Artificial Neural Network Based Methodology for the Prediction of Power & Torque Coefficients of a Two Bladed Airfoil Shaped H-Rotor

An artificial neural network based model can effectively predict any functional relationship. In this paper, a neural network model is used to predict power coefficient and torque coefficient of a two bladed airfoil shaped H-rotor as function of different input parameters. The important input parameters considered are blade tip speed, free stream velocity with blockage and rotor inlet velocity. The values of all the process parameters are taken from the experimental work done on two-bladed airfoil shaped H-rotor. The rotor was earlier designed, fabricated, and tested in a subsonic wind tunnel available in the department. Since neural networks are good at interpolation, once the model is properly trained & tested, it has successfully interpolated the values of power and torque coefficients within an acceptable accuracy. Initially, the optimum no. of neurons in the hidden layer has been found out using hit and trial method by training the network using back propagation learning algorithm. The effect of increasing the size of training and testing data set is studied as well. It is found that only a single neuron has been able to predict both the coefficients successfully. A strategy has been developed to reduce both the training and testing errors. The root mean squared functional errors (rms error) of testing and training for power coefficient prediction are 0.0357 and 0.0387 respectively, while the corresponding values for torque coefficients are 0.0283 and 0.0449 respectively. The proposed methodology is fast and accurate. And testing error being less than the training error, makes the proposed algorithm a superior one.