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dc.contributor.authorGhiasi, Mohammad M
dc.contributor.authorBahadori, Mohammad
dc.contributor.authorLee, Moonyong
dc.contributor.authorKashiwao, Tomoaki
dc.contributor.authorBahadori, Alireza
dc.description.abstractTwo 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.
dc.publisherAccess Intelligence
dc.relation.ispartofjournalChemical Engineering
dc.subject.fieldofresearchChemical Engineering not elsewhere classified
dc.titleRapid prediction of Prandtl number of compressed air
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
gro.hasfulltextNo Full Text
gro.griffith.authorBahadori, Mohammad

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    Contains articles published by Griffith authors in scholarly journals.

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