Prediction of Head, Efficiency, and Power Characteristics in a Semi-Open Impeller

Artificial Neural Network (ANN) was used to predict the effects of splitter blades in a semi-open impeller on centrifugal pump performance. The characteristics of this impeller were compared with those of impellers without splitter blades. Experimental results for lengths of splitter blades in ratio of 1/3, 2/3, and 3/3 of the main blade length were evaluated by different ANN training algorithm. Training and test data were obtained from experimental studies. The best training algorithm and number of neurons were determined. The values of head, efficiency, and effective power were estimated in a semi-open impeller with splitter blades in ratio of 3/6 and 5/6 of the main blade length at the best efficiency point (b.e.p.). Here, as the splitter blade length increases; the flow rate and power increases, the efficiency decrease. All of the estimated values of performance in a semi-open impeller with splitter blades indicate the model works in line with expectations. Experimental studies to determine head, efficiency and effective power consumption in different types of pumps are complex, time consuming, and costly. It also requires specific measurement tools to obtain the characteristics values of pump. To overcome these difficulties, an ANN can be used for prediction of pump performance in semi open impeller.