Experimental study of fine center electrode spark plug in bi-fuel engines

In the present work, the erosion of platinum fine center electrode spark plugs and conventional nickel plugs are investigated in a gasoline and natural gas bi-fuel engine. The effect of electrode erosion is evaluated by comparing the required ignition voltage and cold start ability of the different plug designs. After durability tests, platinum fine center electrode plug had insignificant electrode erosion and negligible gap growth; whereas the nickel plug had notable erosion and gap growth. There was no detectable side sparking for fine center electrode plugs. In terms of performance, the required ignition voltage of fine center electrode plug was lower than conventional spark plug. Also, results of a cold start test demonstrated that the starting time of the engine with fine electrode plugs was lower than conventional spark plugs. The surface of electrodes was studied by the scanning electron microscope and the energy dispersive X-ray spectroscopy techniques. Cracking and peeling was observed on the surface of the nickel conventional electrodes, but not on the surface of the platinum fine electrodes. These tests show that platinum fine center electrodes could be suitable for gasoline/natural gas bi-fuel engines to meet long lifetime demand.     

Predicting discharge coefficient of triangular labyrinth weir using extreme learning machine,artificial neural network and genetic programming

Weirs are a type of hydraulic structure used to direct and transfer water flows in the canals and overflows in the dams. The important index in computing flow discharge over the weir is discharge coefficient (C _d). The aim of this study is accurate determination of the C _d in triangular labyrinth side weirs by applying three intelligence models [i.e., artificial neural network (ANN), genetic programming (GP) and extreme learning machine (ELM)]. The calculated discharge coefficients were then compared with some experimental results. In order to examine the accuracy of C _d predictions by ANN, GP and ELM methods, five statistical indices including coefficient of determination (R ²), root-mean-square error (RMSE), mean absolute percentage error (MAPE), SI and δ have been used. Results showed that R ² values in the ELM, ANN and GP methods were 0.993, 0.886 and 0.884, respectively, at training stage and 0.971, 0.965 and 0.963, respectively, at test stage. The ELM method, having MAPE, RMSE, SI and δ values of 0.81, 0.0059, 0.0082 and 0.81, respectively, at the training stage and 0.89, 0.0063, 0.0089 and 0.88, respectively, at the test stage, was superior to ANN and GP methods. The ANN model ranked next to the ELM model.