【摘要】：泵的性能預(yù)測(cè)研究就是根據(jù)泵的葉輪、蝸殼、導(dǎo)葉等過流部件的幾何參數(shù),分析內(nèi)部流動(dòng)特征,以此預(yù)測(cè)泵的性能,是在泵產(chǎn)品設(shè)計(jì)中必不可少的重要環(huán)節(jié),具有縮短研發(fā)周期、降低開發(fā)成本和提高產(chǎn)品設(shè)計(jì)質(zhì)量等優(yōu)點(diǎn)。因此開展泵的性能預(yù)測(cè)研究一直都是從事泵領(lǐng)域研究專家學(xué)者一個(gè)十分重要的課題。但是,到目前為止,泵的性能預(yù)測(cè)研究的結(jié)果還存在精度不高、不能滿足工程實(shí)際需要的不足。因此對(duì)離心泵的性能研究不但具有重要的學(xué)術(shù)價(jià)值和社會(huì)效益,而且對(duì)研究其他泵的性能提供了可資借鑒的依據(jù)。本文的主要研究?jī)?nèi)容和成果有:(1)詳細(xì)闡述了目前國(guó)內(nèi)外針對(duì)泵性能預(yù)測(cè)研究的現(xiàn)狀,重點(diǎn)描述了水力損失法、流場(chǎng)計(jì)算法及神經(jīng)網(wǎng)絡(luò)法這三種泵性能預(yù)測(cè)的方法,根據(jù)這三種方法的優(yōu)缺點(diǎn),確定本文里研究?jī)?nèi)容。(2)通過對(duì)離心泵性能分析,闡述各參數(shù)對(duì)離心泵性能的影響,為后面的研究打下基礎(chǔ);(3)介紹了離心泵性能預(yù)測(cè)的相關(guān)技術(shù),即通過分析Matlab和VC6.0軟件及其神經(jīng)網(wǎng)絡(luò)工具在進(jìn)行數(shù)據(jù)處理、非線性擬合、動(dòng)態(tài)仿真等的巨大優(yōu)勢(shì),因此本文利用神經(jīng)網(wǎng)絡(luò)控制作為本課題的研究方向;(4)采用神經(jīng)網(wǎng)絡(luò)的兩種算法:使用VC6.0實(shí)現(xiàn)了貝葉斯BP神經(jīng)網(wǎng)絡(luò)算法,使用Matlab實(shí)現(xiàn)了GA-RBF算法,并分別對(duì)實(shí)現(xiàn)離心泵性能預(yù)測(cè)進(jìn)行設(shè)計(jì);(5)利用GA-RBF神經(jīng)網(wǎng)絡(luò)建立離心泵性能預(yù)測(cè)模型來實(shí)現(xiàn)離心泵的性能預(yù)測(cè),具體闡述了其實(shí)現(xiàn)過程,并根據(jù)選取的57組單級(jí)單吸離心泵的設(shè)計(jì)參數(shù)和試驗(yàn)參數(shù)進(jìn)行仿真驗(yàn)證,結(jié)果表明離心泵性能GA-RBF神經(jīng)網(wǎng)絡(luò)預(yù)測(cè)模型與原有的離心泵性能RBF神經(jīng)網(wǎng)絡(luò)預(yù)測(cè)模型同樣有效,并且設(shè)置參數(shù)更簡(jiǎn)單、更方便;(6)利用貝葉斯BP神經(jīng)網(wǎng)絡(luò)建立離心泵性能預(yù)測(cè)模型來實(shí)現(xiàn)離心泵的性能預(yù)測(cè),具體闡述實(shí)現(xiàn)過程,并根據(jù)選取的57組單級(jí)單吸離心泵的設(shè)計(jì)參數(shù)和試驗(yàn)參數(shù)進(jìn)行仿真驗(yàn)證,結(jié)果表明離心泵性能BRBP神經(jīng)網(wǎng)絡(luò)預(yù)測(cè)模型與原有的離心泵性能LMBP神經(jīng)網(wǎng)絡(luò)預(yù)測(cè)模型和離心泵性能RBF神經(jīng)網(wǎng)絡(luò)預(yù)測(cè)模型同樣有效,并且設(shè)置參數(shù)更簡(jiǎn)單、更方便,是一種比較有前途的離心泵性能預(yù)測(cè)方法。
[Abstract]:The prediction of pump performance is based on the geometric parameters of the impeller, volute, guide vane and other flow components, and analyzes the internal flow characteristics to predict the pump performance, which is an essential and important link in the design of pump products. It has the advantages of shortening R & D cycle, reducing development cost and improving product design quality. Therefore, the research of pump performance prediction has always been a very important topic in the field of pump research experts and scholars. However, up to now, the results of pump performance prediction are still not high precision and can not meet the practical needs of the project. Therefore, the research on the performance of centrifugal pump not only has important academic value and social benefit, but also can be used for reference to study the performance of other pumps. The main contents and achievements of this paper are as follows: (1) the present situation of pump performance prediction at home and abroad is described in detail, and the hydraulic loss method, flow calculation method and neural network method are described in detail. According to the advantages and disadvantages of these three methods, the research contents in this paper are determined. (2) through the analysis of the performance of centrifugal pump, the influence of each parameter on the performance of centrifugal pump is expounded, which lays a foundation for further research; (3) the related technology of centrifugal pump performance prediction is introduced, that is, by analyzing the great advantages of Matlab and VC6.0 software and their neural network tools in data processing, nonlinear fitting, dynamic simulation, etc. Therefore, this paper uses neural network control as the research direction of this subject. (4) two kinds of neural network algorithms are used: Bayesian BP neural network algorithm is implemented by using VC6.0, GA-RBF algorithm is implemented by Matlab, and the performance prediction of centrifugal pump is designed. (5) the performance prediction model of centrifugal pump is established by using GA-RBF neural network to predict the performance of centrifugal pump, and the realization process of centrifugal pump is described in detail. According to the design parameters and test parameters of 57 single-stage and single-suction centrifugal pumps, the simulation results show that the GA-RBF neural network prediction model of centrifugal pump performance is as effective as the original RBF neural network prediction model of centrifugal pump performance. And setting parameters is simpler and more convenient; (6) the performance prediction model of centrifugal pump is established by using Bayesian BP neural network to predict the performance of centrifugal pump, and the realization process is described in detail. The simulation results are verified according to the design parameters and test parameters of 57 single-stage and single-suction centrifugal pumps. The results show that the BRBP neural network prediction model of centrifugal pump performance is as effective as the original LMBP neural network prediction model of centrifugal pump performance and the RBF neural network prediction model of centrifugal pump performance, and setting parameters is simpler and more convenient. It is a promising method to predict the performance of centrifugal pump.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TH311

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相關(guān)期刊論文 前3條

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本文編號(hào)：2446769