Rapid Prediction of Prandtl Number of Compressed Air
By Mohammad M. Ghiasi National Iranian Gas Co.(NIGC) Mohammad Bahadori Griffith University Moonyong Lee Yeungnam University Tomoaki Kashiwao National Institute of Technology, Niihama College Alireza Bahadori Southern Cross University |
Two methods are presented and compared for quickly calculating this important, yet neglected parameter
Over the last few decades, a considerable effort has been directed to toward the evaluation of thermophysical and transport properties of air for a wide range of temperatures. However, relatively limited attention has been given to investigation of the compressed air Prandtl number at elevated pressures.
In this article, two new approaches for the accurate prediction of Prandtl number (Pr) of compressed air are presented. The first approach is based on developing a simple-to-use polynomial correlation for predicting Pr of compressed air as a function of temperature and pressure. The second approach is based on the feed-forward back-propagation (FF-BP) artificial neural network (ANN) methodology, wherein the results demonstrate the ability of the presented ANN method to predict accurate Pr values of air at elevated pressures. A comparison of the two approaches indicates that the developed ANN-based model provides slightly more accurate results than the new empirical correlation.
The development of methods for evaluation of air properties was the subject of a number of earlier investigations, which were employed…