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Prediction Model of Flow-Induced Noise in Large-Scale Centrifugal Pumps Based on BP Neural Network

[+] Author Affiliations
Chang Guo, Ming Gao, Yuetao Shi, Fengzhong Sun

Shandong University, Jinan, China

Peixin Dong

University of Queensland, Brisbane, Australia

Paper No. POWER-ICOPE2017-3280, pp. V002T13A006; 5 pages
  • ASME 2017 Power Conference Joint With ICOPE-17 collocated with the ASME 2017 11th International Conference on Energy Sustainability, the ASME 2017 15th International Conference on Fuel Cell Science, Engineering and Technology, and the ASME 2017 Nuclear Forum
  • Volume 2: I&C, Digital Controls, and Influence of Human Factors; Plant Construction Issues and Supply Chain Management; Plant Operations, Maintenance, Aging Management, Reliability and Performance; Renewable Energy Systems: Solar, Wind, Hydro and Geothermal; Risk Management, Safety and Cyber Security; Steam Turbine-Generators, Electric Generators, Transformers, Switchgear, and Electric BOP and Auxiliaries; Student Competition; Thermal Hydraulics and Computational Fluid Dynamics
  • Charlotte, North Carolina, USA, June 26–30, 2017
  • Conference Sponsors: Power Division, Advanced Energy Systems Division, Solar Energy Division, Nuclear Engineering Division
  • ISBN: 978-0-7918-5761-8
  • Copyright © 2017 by ASME


As one kind of serious environmental problems, flow-induced noise in centrifugal pumps pollutes the working circumstance and deteriorates the performance of pumps, meanwhile, it always changes drastically under various working conditions. Consequently, it is extremely significant to predict flow-induced noise of centrifugal pumps under various working conditions with a practical mathematical model. In this paper, a three-layer back propagation (BP) neural network model is established and the number of input, hidden and output layer node is set as 3, 6 and 1, respectively. To be specific, the flow rate, rotational speed and medium temperature are chosen as input layer, and the corresponding flow-induced noise evaluated by average of total sound pressure level (A_TSPL) as output layer. Furthermore, the tansig function is used to act as transfer function between the input layer and hidden layer, and the purelin function is used between hidden layer and output layer. The trainlm function based on Levenberg-Marquardt algorithm is selected as the training function. By using a large number of sample data, the training of the network model and prediction research are accomplished. The results indicate that good correlation is established among the sample data, and the predictive values show great consistence with simulation ones, of which the average relative error of A_TSPL in process of verification is 0.52%. The precision of the model can satisfy the requirement of relevant research and engineering application.

Copyright © 2017 by ASME



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