标题:Prediction model of flow-induced noise in large-scale centrifugal pumps based on BP neural network
作者:Guo, Chang ;Gao, Ming ;Dong, Peixin ;Shi, Yuetao ;Sun, Fengzhong
作者机构:[Guo, Chang ;Gao, Ming ;Shi, Yuetao ;Sun, Fengzhong ] School of Energy and Power Engineering, Shandong University Jinan, Shandong, China;[Dong, Peixin 更多
会议名称:ASME 2017 Power Conference Joint with ICOPE 2017, POWER 2017-ICOPE 2017, 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
会议日期:26 June 2017 through 30 June 2017
来源:American Society of Mechanical Engineers, Power Division (Publication) POWER
出版年:2017
卷:2
DOI:10.1115/POWER-ICOPE2017-3280
摘要:As one kind of serious environmental problems, flowinduced 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 ASME.
收录类别:EI;SCOPUS
资源类型:会议论文;期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85029836230&doi=10.1115%2fPOWER-ICOPE2017-3280&partnerID=40&md5=78e015a3a338480a8581e76a5195a876
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