Predictive modeling of performance of a helium charged Stirling engine using an artificial neural network
Yükleniyor...
Dosyalar
Tarih
2013
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Pergamon-Elsevier Science Ltd
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
In this study, an artificial neural network (ANN) model was developed to predict the torque and power of a beta-type Stirling engine using helium as the working fluid. The best results were obtained by 5-11-7-1 and 5-13-7-1 network architectures, with double hidden layers for the torque and power respectively. For these network architectures, the Levenberg-Marquardt (LM) learning algorithm was used. Engine performance values predicted with the developed ANN model were compared with the actual performance values measured experimentally, and substantially coinciding results were observed. After ANN training, correlation coefficients (R-2) of both engine performance values for testing and training data were very close to 1. Similarly, root-mean-square error (RMSE) and mean error percentage (MEP) values for the testing and training data were less than 0.02% and 3.5% respectively. These results showed that the ANN is an acceptable model for prediction of the torque and power of the beta-type Stirling engine. (C) 2012 Elsevier Ltd. All rights reserved.
Açıklama
KARA, Fuat/0000-0002-3811-3081
WOS: 000316831100038
WOS: 000316831100038
Anahtar Kelimeler
Beta type Stirling engine, Helium, ANN, Engine performance
Kaynak
Energy Conversion And Management
WoS Q Değeri
Q1
Scopus Q Değeri
Q1
Cilt
67
