Neural network analysis of head-flow curves in deep well pumps
作者： Mustafa Gölcü
DOI: 10.1016/J.ENCONMAN.2005.06.023
关键词:
摘要: Abstract In impellers with splitter blades, the difficulty in calculation of flow area impeller is because unknown rate occurring two separate areas when blades are added. Experimental studies were made to investigate effects blade length on deep well pump performance for different numbers blades. Head-flow curves investigated using artificial neural networks (ANNs). Gradient descent (GD), momentum (GDM) and Levenberg–Marquardt (LM) learning algorithms used networks. completed obtain training test data. Blade number ( z ), non-dimensional L ¯ ) Q as input layer, while output head H m ). For testing data, root mean squared error (RMSE), fraction variance R 2 absolute percentage (MAPE) found be 0.1285, 0.9999 1.6821%, respectively. With these results, we believe that ANN can prediction head-flow an appropriate method
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