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航空專用離心噴頭霧化性能試驗(yàn)與影響因子研究
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國(guó)家自然科學(xué)基金項(xiàng)目(51705264)和國(guó)家重點(diǎn)研發(fā)計(jì)劃項(xiàng)目(2017YFD0701000)


Atomization Performance Test and Influence Factors of Aviation Special Centrifugal Nozzle
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    摘要:

    針對(duì)航空施藥模式下噴頭噴霧參數(shù)與霧化參數(shù)關(guān)系不明確的問題,本文結(jié)合噴霧性能測(cè)試與建立代理數(shù)學(xué)模型,討論了CN1215型航空專用離心噴頭主要工作參數(shù)對(duì)霧滴體積中徑(Dv50)、噴幅的影響規(guī)律。標(biāo)定了離心噴頭噴霧參數(shù)對(duì)應(yīng)的供液系統(tǒng)工作參數(shù),在室內(nèi)無風(fēng)環(huán)境下測(cè)試了不同噴頭流量(100~350mL/min)、噴頭轉(zhuǎn)速(8000~10000r/min)下的霧滴中徑及噴幅。以噴頭噴霧參數(shù)(噴頭流量、噴頭轉(zhuǎn)速)作為試驗(yàn)因素,以航空離心噴頭霧化后霧滴體積中徑Dv50、對(duì)應(yīng)噴幅為響應(yīng)因數(shù),分別采用四階響應(yīng)面法(Response surface method,RSM)、克里金法(Kriging)、橢球基神經(jīng)網(wǎng)絡(luò)(Ellipsoidal basis function neural network,EBFNN)3種數(shù)學(xué)方法逼近試驗(yàn)因素與響應(yīng)因數(shù)之間的關(guān)系,建立了噴頭霧化參數(shù)(Dv50、對(duì)應(yīng)噴幅)與噴頭噴霧參數(shù)(噴頭流量、噴頭轉(zhuǎn)速)之間的代理數(shù)學(xué)模型,3種代理模型對(duì)Dv50的決定系數(shù)R2分別為:0.705、0.718、0.925,3種代理模型關(guān)于Dv50對(duì)應(yīng)噴幅的決定系數(shù)R2分別為:0.819、0.890、0.930。基于EBFNN隱式代理數(shù)學(xué)模型建立了兩個(gè)霧化參數(shù)的響應(yīng)面,實(shí)現(xiàn)了噴霧參數(shù)影響下的霧滴Dv50、噴幅的快速預(yù)測(cè)。

    Abstract:

    Aiming at the unclear relationship between spray parameters of nozzle and atomization parameters in the aviation spray mode, and considering the urgent need of precision aerosol spray, spray performance test and agent mathematical model modeling method were combined to discuss the influence of main working parameters on atomized particle size and spray width for the CN1215 special aviation centrifugal nozzle. The operating parameters of the liquid supply system corresponding to the spray parameters of the centrifugal nozzle were calibrated, and then the variation laws of droplet size and spray width under the influence of the working parameters (flow rate range was 100~350mL/min, nozzle rotating speed was 8000~10000r/min) were also analyzed in an indoor windless environment. Secondly, taking the spray parameters of the nozzle (nozzle flow, nozzle speed) as the test factor, and taking the droplet diameter (Dv50) and the spray width corresponding to droplet diameter (Dv50) as response factor, three kinds of mathematical methods, including fourthorder response surface method (RSM), Kriging method and ellipsoidal basis function neural network (EBFNN), were used to approximate the relationship between experimental factors and response factors respectively. The agent mathematical models between the nozzle atomization parameters (Dv50, and corresponding spray width) and nozzle operating parameters (nozzle flow, nozzle speed) were established. The decisive coefficients R2 of the three agent models for the particle size Dv50 were 0.705, 0.718 and 0.925, and the decisive coefficients R2 of the three agent models for the Dv50 corresponding spray width were 0.819, 0.890 and 0.930, respectively. Based on the EBFNN implicit proxy mathematical model, the response surface of two atomization parameters was established, the rapid prediction of droplet Dv50 and spray width under the influence of working parameters was achieved. Based on the EBFNN implicit proxy mathematical model, the response surfaces of two atomization parameters were established, which realized the rapid prediction of droplet Dv50 and spray amplitude under the influence of spray parameters. It was of great significance for accelerating the development of aviation precision pesticide application.

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楊風(fēng)波,薛新宇,蔡晨,周晴晴,孫竹.航空專用離心噴頭霧化性能試驗(yàn)與影響因子研究[J].農(nóng)業(yè)機(jī)械學(xué)報(bào),2019,50(9):96-104. YANG Fengbo, XUE Xinyu, CAI Chen, ZHOU Qingqing, SUN Zhu. Atomization Performance Test and Influence Factors of Aviation Special Centrifugal Nozzle[J]. Transactions of the Chinese Society for Agricultural Machinery,2019,50(9):96-104.

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  • 收稿日期:2019-04-12
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  • 在線發(fā)布日期: 2019-09-10
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