Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
649792 | Applied Thermal Engineering | 2006 | 5 Pages |
Abstract
The ability of an artificial neural network model, using a back propagation learning algorithm, to predict specific fuel consumption and exhaust temperature of a Diesel engine for various injection timings is studied. The proposed new model is compared with experimental results. The comparison showed that the consistence between experimental and the network results are achieved by a mean absolute relative error less than 2%. It is considered that a well-trained neural network model provides fast and consistent results, making it an easy-to-use tool in preliminary studies for such thermal engineering problems.
Related Topics
Physical Sciences and Engineering
Chemical Engineering
Fluid Flow and Transfer Processes
Authors
Adnan Parlak, Yasar Islamoglu, Halit Yasar, Aysun Egrisogut,