The effects of splitter blades with low blade number on deep well pump performance

Debi-yük karakteristiğinin kararlılık durumu; kanat sayısı, kanat çıkış açısı, çark çıkış çapı gibi geometrik faktörlere bağlı olarak değişmektedir. Kanat sayısının düşük olmasından dolayı oluşan hidrolik kayıplar, iki ana kanat arasındaki merkez akış hattı üzerine ara kanatçık yerleştirilmesi ile azaltılabilir. Uygulamada kanat sayısı genellikle tecrübe edilmiş değerlere bağlı olarak seçildikten sonra, kanatların çıkış açısının hesabı yapılır. Bu çalışmada; kanat sayısı z = 3 ve z = 4 olan düşük kanat sayılı ve düşük kanat çıkış açısına (β2K=150 ) sahip dalgıç pompa çarklarına; iki ana kanadın merkez akış hattı üzerine ana kanat boyunun % 25-35-50-60 ve 80 oranlarında ara kanatçıklar yerleştirerek, dalgıç pompa performansı üzerine etkileri deneysel olarak incelenmiş ve Hm=f (Q) karakteristiği kararlı hale getirilerek verimde artış sağlanmıştır.Stability of head-flow characteristics depend on the geometrical factors ie. on the number of blades, blade discharge angle, impeller diameter. Splitter blades are located at the center line of the flow that is between adjacent blades. On the location, the hydraulics losses caused by low blade number can be overcomed. In practice, the blade number is chosen as request and the discharge angle is only calculated. In this study, deep weel pumps having low number of blades (z = 3 and 4) and low blade discharge angle (β2K=150 ) are considered. Splitter blades having different length (25, 35, 50, 60, and 80 % of the main blade length) are located at the center line of the main blades. It has been experimentally investigated the effect of splitter blades on the deep well pump performance. The head-flow charactaristics are obtained for each pump and the efficiency is improved

Prediction of Performance and Smoke Emission Using Artificial Neural Network in a Diesel Engine

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 Levenberg-Marquardt (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 (SFC), R2 and MAPE were calculated as 0.9872 and 6.0261%, respectively. For the torque with SCG algorithm, R2 and MAPE were found to be 0.9879 and 9.0026%, respectively. Similarly, for the specific fuel consumption (SFC), 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