Prediction of engine performance for an alternative fuel using artificial neural network
Küçük Resim Yok
Tarih
2012
Yazarlar
Dergi Başlığı
Dergi ISSN
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Yayıncı
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
This study deals with artificial neural network (ANN) modeling to predict the brake specific fuel consumption, effective power and average effective pressure and exhaust gas temperature of the methanol engine. To obtain training and testing data, a number of experiments were performed with a four-cylinder, four-stroke test engine operated at different engine speeds and torques. Using some of the experimental data for training, an ANN model based on standard back propagation algorithm was developed. Then, the performance of the ANN predictions was measured by comparing the predictions with the experimental results. Engine speed, engine torque, fuel flow, intake manifold mean temperature and cooling water entrance temperature have been used as the input layer, while brake specific fuel consumption, effective power, average effective pressure and exhaust gas temperature have also been used separately as the output layer. After training, it was found that the R 2 values are close to 1 for both training and testing data. RMS values are smaller than 0.015 and mean errors are smaller than 3.8% for the testing data. This shows that the developed ANN model is a powerful one for predicting the brake specific fuel consumption, effective power and average effective pressure and exhaust gas temperature of internal combustion engines. © 2011 Elsevier Ltd. All rights reserved.
Açıklama
Anahtar Kelimeler
Artificial neural network, Methanol engine performance, Spark ignition engine
Kaynak
Applied Thermal Engineering
WoS Q Değeri
Scopus Q Değeri
Q1
Cilt
37
