Abstract
Based on MATLAB, a BP artificial neural network (BPANN) model for predicting the efficiency and head of centrifugal pumps with splitters were built. 85 groups of test results were used to train and test the network, where the Levenberg—Marquardt algorithm was adopted to train the neural network model. Five parameters Q,Z,β 2,D i ,b 2 were chosen in the input layer, η and H were the output factors. Through the analysis of prediction results, the conclusion was got that, the accuracy of the BP ANN is good enough for performance prediction. And the BP ANN can be used for assisting design of centrifugal pumps with splitters. Meanwhile, the method of CFD flow field simulation was also used to predict the head and power for a centrifugal pump with splitters, and compared with that from the BPANN model. The comparison of prediction results and experimental value demonstrated that the prediction values acquired through numerical simulation and BPANN were uniform with the test data. Both methods could be used to predict the performance of low specific speed centrifugal pump with splitters.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Yuan, S.Q.: Advances in Hydraulic Design of Centrifugal Pumps. In: ASME, Fluid Engineering Division Summer Meeting, Vancouver, BC, Canada, pp. 1–15 (1997)
Yuan, S.Q.: The Theory and Design of Low Specific-Speed Centrifugal Pumps. Mechanical Industry Press, Beijing (1997) (in Chinese)
Yuan, S.Q., Chen, C.Q.: CAO Wuling: Design Method of Obtaining Stable Head-Flow Curves of Centrifugal Pumps. In: ASME. Pumping Machinery Meeting, FED, vol. 154, pp. 171–175 (1993)
Miyamoto, H., Nakashima, Y., Ohba, H.: Effects of Splitter Blades on the Flows and Characteristics in Centrifugal Impellers. JSME Int. J. Ser. 2: Fluid Eng. Heat Transfer Power Combustion Thermo Physical Properties 35, 238–246 (1992)
Asuaje, M., Bakir, F., Noguera, R., et al.: 3-D Quasi-Unsteady Flow Simulation in a Centrifugal Pump. Influence of Splitter Blades in Velocity and Pressure Field. ASME Fluids Engineering Forum - FEDSM 2004 - HT-FED2004-56600 (2004)
Gölcü, M., Pancar, Y., Sekmen, Y.: Energy Saving in a Deep Well Pump with Splitter Blade. Energy Conversion and Management 47, 638–651 (2006)
Gölcü, M., Pancar, Y.: Investigation of Performance Characteristics in a Pump Impeller with Low Blade Discharge Angle. World Pumps 9, 32–40 (2005)
Gölcü, M.: Neural Network Analysis of Head-Flow Curves in Deep Well Pumps. Energy Conversion and Management 47, 992–1003 (2006)
Gölcü, M.: Artificial Neural Network Based Modeling of Performance Characteristics of Deep Well Pumps with Splitter Blade. Energy Conversion and Management 47, 3333–3343 (2006)
Yuan, S.Q., Zhang, J.F., Yuan, J.P., et al.: Research on the Design Method of the Centrifugal Pump with Splitter Blades. In: Proceedings of the ASME, Fluids Engineering Division Summer Meeting, Vail, Colorado USA (August 2009)
Chen, S.S., Zhou, Z.F., Ge, Q., et al.: Orthogonal experimental study on centrifugal pump with deviated splitter vanes. Journal of Yangzhou University Natural Science Edition 8, 45–48 (2005) (in Chinese)
Luo, C.L., Yuan, Y.C.: The Problem of the Efficiency and Unsteady Performance Curve for Low-Specific Speed Centrifugal Pump. Pump Technology 3, 20–27 (1983) (in Chinese)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zhang, J., Yuan, S., Shen, Y., Zhang, W. (2010). Performance Prediction for a Centrifugal Pump with Splitter Blades Based on BP Artificial Neural Network. In: Li, K., Li, X., Ma, S., Irwin, G.W. (eds) Life System Modeling and Intelligent Computing. ICSEE LSMS 2010 2010. Communications in Computer and Information Science, vol 98. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15859-9_31
Download citation
DOI: https://doi.org/10.1007/978-3-642-15859-9_31
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15858-2
Online ISBN: 978-3-642-15859-9
eBook Packages: Computer ScienceComputer Science (R0)