Rapid prediction of prandtl number of compressed air

Document Type


Publication details

Ghiasi, MM, Bahadori, M, Lee, M, Kashiwao, T & Bahadori, A 2016, 'Rapid prediction of prandtl number of compressed air', Chemical Engineering.

Article available on Open Access

Peer Reviewed



Over the last few decades, a considerable amount of effort has been devoted towards the evaluation of thermophysical and transport properties of air for a wide range of temperatures. However, a relatively limited attention was oriented toward 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 feedforward back-propagation (FF-BP) artificial neural network (ANN) methodology, wherein the results demonstrate the ability of the presented ANN method to predict accurate Pr valures of air at elevated pressures. Comparisons of the two approaches indicates that the developed ANNbased model provides slightly more accurate results than the new empirical correlation.