Application of neural network to performance predicting of multiphase rotodynamic pump

Author

Ma, Xijin ; Li, Xinkai ; Hu, Zhonghui ; Yang, Dengfeng ; Wang, Nan

Author_Institution

Sch. of Energy & Power Eng., Lanzhou Univ. of Technol., Lanzhou, China

fYear

2011

fDate

8-10 Aug. 2011

Firstpage

195

Lastpage

198

Abstract

Based on the third generation YQH-100 multiphase rotodynamic pump independently developed as the research object, The models of BP and RBF neural network were established respectively to predict the Multiphase Rotodynamic pump energy characteristics. The 27 groups of sample data of neural networks were created by FLUENT numerical calculated. The performance data of 20 multiphase rotodynamic pumps were used to train the two models and the data of the other 7 multiphase rotodynamic pumps were used to test the two models. The predict results show that the predicted of two models were favourably accorded with experiment. So it is possible to use BP and RBF neural network for predicting performance of multiphase rotodynamic pumps, which can reduce cost and shorten experimental time.

Keywords

backpropagation; mechanical engineering computing; pumps; radial basis function networks; BP neural network; FLUENT; RBF neural network; performance prediction; third generation YQH-100 multiphase rotodynamic pump; Accuracy; Biological neural networks; Data models; Neurons; Predictive models; Training; Transfer functions; multiphase rotodynamic pump; neural network; performance predicting;

fLanguage

English

Publisher

ieee

Conference_Titel

Artificial Intelligence, Management Science and Electronic Commerce (AIMSEC), 2011 2nd International Conference on

Conference_Location

Deng Leng

Print_ISBN

978-1-4577-0535-9

Type

conf

DOI

10.1109/AIMSEC.2011.6010450

Filename

6010450