Artificial neural network based modelling of performance of a beta-type Stirling engine

Abstract

In this article, artificial neural network has been used in order to predict the power ( P) and torque ( T) values obtained from a beta-type Stirling engine that uses air as working fluid. Experimental data have been obtained for different charge pressures and hot source temperatures using ZrO2-coated and uncoated displacers. The closest artificial neural network results to experimental torque and power values were obtained with double hidden layer 5–13–9–1 and 5–13–7–1 network architectures, respectively. The best prediction values were obtained by Levenberg–Marquardt learning algorithm. Correlation coefficient ( R2) for the torque values were 0.998331 and 0.997231 for the training and test sets, respectively, while R2 value for power values were 0.998331 and 0.997231 for the training and test sets, respectively. R2 values show that the developed artificial neural network is an acceptable and powerful modelling technique in predicting the torque and power values of the beta-type Stirling engine.