Comparative study of mathematical and experimental analysis of spark ignition engine performance used ethanol-gasoline blend fuel

Serdar Yucesu H. S., SÖZEN A., Topgul T., ARCAKLIOĞLU E.

APPLIED THERMAL ENGINEERING, vol.27, no.2-3, pp.358-368, 2007 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 27 Issue: 2-3
  • Publication Date: 2007
  • Doi Number: 10.1016/j.applthermaleng.2006.07.027
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.358-368
  • Keywords: alternative fuel, gasoline-ethanol blends, artificial neural network, compression ratio, brake specific fuel consumption, ARTIFICIAL NEURAL-NETWORKS, ETHYL-ALCOHOL, EMISSION, HYDROGEN, WORKING
  • Gazi University Affiliated: Yes


This study consists of two cases: (i) The experimental analysis: Ethanol obtained from biomass can be used as a fuel in spark ignition engines. As renewable energy source ethanol, due to the high octane number, low emissions and high engine performance is preferred alternative fuel. First stage of this study, ethanol-unleaded gasoline blends (E10, E20, E40 and E60) were tested in a single cylinder, four-stroke spark ignition and fuel injection engine. The tests were performed by varying the ignition timing, relative air-fuel ratio (RAFR) and compression ratio at a constant speed of 2000 rpm and at wide open throttle (WOT). Effect of ethanol-unleaded gasoline blends and tests variables on engine torque and specific fuel consumption were examined experimentally. (ii) The mathematical modeling analysis: The use of ANN has been proposed to determine the engine torque and specific fuel consumption based on the ignition timing, RAFR and compression ratio at a constant speed of 2000 rpm and at WOT for different fuel densities using results of experimental analysis. The back-propagation learning algorithm with two different variants and logistic sigmoid transfer function were used in the network. In order to train the neural network, limited experimental measurements were used as training and test data. The best fitting training data set was obtained Levenberg-Marquardt (LM) algorithm with five neurons in the hidden layer, which made it possible to the engine torque and specific fuel consumption with accuracy at least as good as that of the experimental error, over the whole experimental range. After training, it was found the RZ values are 0.999996 and 0.999991 for, the engine torque and specific fuel consumption, respectively. Similarly, these values for testing data are 0.999977 and 0.999915, respectively. As seen from the results of mathematical modeling, the calculated engine torque and specific fuel consumption are obviously within acceptable uncertainties. (c) 2006 Elsevier Ltd. All rights reserved.