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International Standard Serial Number:
ISSN 1001-4551
Sponsor:
Zhejiang University;
Zhejiang Machinery and Electrical Group
Edited by:
Editorial of Journal of Mechanical & Electrical Engineering
Chief Editor:
ZHAO Qun
Vice Chief Editor:
TANG ren-zhong,
LUO Xiang-yang
Tel:
86-571-87041360,87239525
Fax:
86-571-87239571
Add:
No.9 Gaoguannong,Daxue Road,Hangzhou,China
P.C:
310009
E-mail:
meem_contribute@163.com
Abstract: Hydraulic cone valve will produce severe noise when it works in the state of gas-liquid two-phase flow, which seriously affects the working performance and working environment of the cone valve. Aiming at this problem, a method based on radial basis function (RBF) neural network and multi-island genetic algorithm (MIGA) was proposed to optimize the structural parameters of hydraulic cone valves. Firstly, the structural parameters affecting the flow field and sound field of the cone valve were analyzed by the finite element analysis software. Then, the half cone angle, throat length and inlet angle of the valve core were taken as the optimization variables, and the noise including weighted average noise and the weighted maximum noise were used as the optimization objectives. The optimal Latin hypercube test method was used to design the sample points. Finally, RBF neural network method was used to establish the approximate model of the relationship between cone valve structural parameters and noise, and the approximate model was optimized by the multi-island genetic algorithm. The optimal parameters were used to establish the optimization model of cone valve, and the acoustic characteristics of that model were analyzed. The results show that compared with the original model, the average noise of the optimized model is reduced by 23.846 dB and the maximum noise is reduced by 5.092 dB, thus the effectiveness of the proposed optimization method is verified. The research results can provide theoretical support for the noise reduction research of hydraulic cone valve.
Key words: hydraulic control valve; cone valve noise suppression; radial basis function (RBF) neural network; multi-island genetic algorithm (MIGA); structural parameters of cone valve;acoustic characteristic analysis;optimal Latin hypercube
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