Journal of Mechanical Engineering >

2017 , Vol. 53 >Issue 6: 166 - 175

DOI: https://doi.org/10.3901/JME.2017.06.166

Multi-objective Intelligent Collaborative Optimization of Structure Parameters for High-power Remote Sprayer

  • CHEN Bo ,
  • GAO Dianrong ,
  • YANG Chao ,
  • WU Shaofeng ,
  • ZHANG Guangtong
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  • 1. College of Mechanical Engineering， Yanshan University， Qinhuangdao 066004;
    2. Qinhuangdao Capital Starlight Environmental Technology Co.， Ltd.， Qinhuangdao 066004

Online published: 2017-03-20

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Abstract

In order to explore the influence of main structure parameters on spray distance and spray efficiency of high-power remote sprayer,and find out the optimal design scheme,a multi-objective intelligent collaborative optimization method is proposed. Using numerical simulation method to establish orthogonal test database,the multi-objective intelligent collaborative optimization method which includes multi-objective orthogonal matrix method,BP (Back Propagation) neural network and genetic algorithms is applied to main structure parameters. The parameters include setting angle of bladeθ,the radial clearance of blade and spray barrelh,inlet angle of guide vaneα,chord length of guide vaneb,numbers of guide vanen,inclination angle of spray barrelβ. A prototype is produced according to the optimal results and a verification test is carried out. It is concluded that the primary and secondary sequence of structure parameters isβ>α>h>θ>b>n,which could reflect the comprehensive influence on spray distance and spray efficiency,the optimal structure parameters of sprayer areθ=47.8°, h=3.6, α=15.2°, b=261.6, n=7, β=1°,the spray distance is increased by 18.92% and the spray efficiency is increased by 12.94% through the multi-objective intelligent collaborative optimization method,the experimental test results are consist with the optimal result of numerical calculation. The research results provide reference for the design and experiment of remote sprayer device.

Key words： multi-objective optimization; sprayer; orthogonal test; BP neural network; genetic algorithms

Cite this article

CHEN Bo , GAO Dianrong , YANG Chao , WU Shaofeng , ZHANG Guangtong . Multi-objective Intelligent Collaborative Optimization of Structure Parameters for High-power Remote Sprayer[J]. Journal of Mechanical Engineering, 2017 , 53(6) : 166 -175 . DOI: 10.3901/JME.2017.06.166

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