Artificial neural network based modelling of performance of a beta-type Stirling engine
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Dosyalar
Tarih
2013
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Sage Publications Ltd
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
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 (R-2) for the torque values were 0.998331 and 0.997231 for the training and test sets, respectively, while R-2 value for power values were 0.998331 and 0.997231 for the training and test sets, respectively. R-2 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.
Açıklama
WOS: 000322197800003
Anahtar Kelimeler
Beta-type Stirling engine, air, artificial neural networks, engine performance
Kaynak
Proceedings Of The Institution Of Mechanical Engineers Part E-Journal Of Process Mechanical Engineering
WoS Q Değeri
Q3
Scopus Q Değeri
Q3
Cilt
227
Sayı
3
