Title of article :
Exhaust emissions prognostication for DI diesel group-hole injectors using a supervised artificial neural network approach
Author/Authors :
Taghavifar، نويسنده , , Hadi and Taghavifar، نويسنده , , Hamid and Mardani، نويسنده , , Aref and Mohebbi، نويسنده , , Arash، نويسنده ,
Issue Information :
روزنامه با شماره پیاپی سال 2014
Pages :
9
From page :
81
To page :
89
Abstract :
Broad information on exhaust emissions facilitates the design of modern machinery and processing equipment with modified quality specifications. This paper is aimed at investigating soot and NOx emissions as affected by crank-angle, liquid mass evaporated, mean diesel mass fraction and heat release rate of group-hole injectors utilizing computational fluid dynamics (CFD) while the objective parameters are prognosticated by a supervised artificial neural network (ANN). A feed-forward ANN with standard back propagation (BP) learning algorithm was adopted for problem modeling with varying number of neurons in the hidden layer. A 4-17-2 topology with Levenberg–Marquardt training algorithm (trainlm) denoted mean squared error (MSE) and mean relative error (MRE) of 0.8051 and 0.0818, respectively. The supervised ANN also represented coefficient of determination, R2 of 0.9716 and 0.9678 for NOx and soot emissions, respectively. The obtained results have shed light on promising ability of ANN as a powerful modeling tool for prognostication of soot and NOx emissions due to some spray specifications.
Keywords :
Crank-angle , Group-hole injectors , Soot , NOX , Artificial neural networks
Journal title :
Fuel
Serial Year :
2014
Journal title :
Fuel
Record number :
1472039
Link To Document :
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