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    Energy saving in a deep well pump with splitter blade
    (2006) Gölcü, M.; Pancar, Y.; Sekmen, Y.
    Design parameters, like blade number, blade outlet angle and impeller outlet diameter, affect pump performance and energy consumption. Deep well pumps with splitter blades (DWPwsb) are manufactured to achieve energy saving and improve efficiency. Splitter blades are generally located at the centerline of the main blades. Blade number and blade discharge angle should be conveniently determined when splitter blades are used on the impellers. In this study, impellers having different numbers of blades (z = 5, 6, 7) with and without splitter blades (35%, 60% and 80% of the main blade length) were tested in a deep well pump. Tests have been conducted on a total of 12 impellers, and the characteristics of deep well pumps without splitter blade (DWPwosb) and DWPwsb were obtained experimentally. These results show that splitter blades cause negative effects on pump performance in impellers with blade numbers of 6 and 7. When the splitter blade is added to the impeller with the blade number of 5, the efficiency increases with flow up to 10 l/s flow rate, after which it decreases as the splitter blade length increases. The highest efficiency and the lowest energy consumption were obtained in DWPwsb with 80% of the main blade length. At the best efficiency point (b.e.p), an energy saving of 6.6% and an improvement of 1.14% in efficiency were achieved. An analysis of the additional cost of the splitter blade and the application in an agricultural area were performed. © 2005 Elsevier Ltd. All rihgts reserved.
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    Prediction of performance and smoke emission using artificial neural network in a diesel engine
    (Association for Scientific Research, 2006) Sekmen, Y.; Gölcü, M.; Erduranli, P.; Pancar, Y.
    The fuel injection pressure is one of the significant operating parameters affects atomization of fuel and mixture formation; therefore, it determines the performance and emissions of a diesel engine. Increasing the fuel injection pressure decrease the particle diameter and caused the diesel fuel spray to vaporize quickly. However, with decreasing fuel particles their inertia will also decrease and for this reason fuel can not penetrate deeply into the combustion chamber. In this study, artificial neural-networks (ANNs) are used to determine the effects of injection pressure on smoke emissions and engine performance in a diesel engine. Experimental studies were used to obtain training and test data. Injection pressure was changed from 100bar to 300bar in experiment (standard injection pressure of test engine is 150bar). Injection pressure and engine speed have been used as the input layer; smoke emission, engine torque and specific fuel consumption have been used as the output layer. Two different training algorithms were studied. The best results were obtained from LevenbergMarquardt (LM) and Scaled Conjugate gradient (SCG) algorithms with 11 neurons. However, The LM algorithm is faster than the SCG algorithm, and its error values are smaller than those of the SCGs. For the torque with LM algorithm, fraction of variance (R2) and mean absolute percentage error (MAPE) were found to be 0.9927 and 7.2108%, respectively. Similarly, for the specific fuel consumption (SPC), R2 and MAPE were calculated as 0.9872 and 6.0261%, respectively. For the torque with SCG algorithm, R 2 and MAPE were found to be 0.9879 and 9.0026%, respectively. Similarly, for the specific fuel consumption (SPC), R2 and MAPE were calculated as 0.9793 and 8.7974%, respectively. So, these ANN predicted results can be considered within acceptable limits and the results show good agreement between predicted and experimental values. © Association for Scientific Research.