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http://dx.doi.org/10.6109/jkiice.2008.12.4.685

Comparison of the neural networks with spline interpolation in modelling superheated water  

Lee, Tae-Hwan (진주산업대학교 메카트로닉스공학과)
Park, Jin-Hyun (진주산업대학교 메카트로닉스공학과)
Kim, Bong-Hwan (진주산업대학교 자동차공학과)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.12, no.4, 2008 , pp. 685-690 More about this Journal
Abstract
In numerically evaluating the thermal performance of the heat exchanger, numerical values of thermodynamic properties such as temperature, pressure, specific volume, enthalpy and entropy are required. But the steam table or diagram itself cannot be directly used without modelling. In this study the applicability of neural networks in modelling superheated water vapor was examined. The multi-layer neural networks consist of an input layer with 2 nodes, two hidden layers with 15 and 25 nodes respectively and an output layer with 3 nodes. Quadratic spline interpolation was also applied for comparison. Neural networks model revealed smaller percentage error compared with spline interpolation. From this result, it is confirmed that the neural networks could be a powerful method in modelling the superheated water vapor.
Keywords
Steam table; Superheated vapor; Neural network; Spline interpolation;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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