基于RBF神經(jīng)網(wǎng)絡(luò)與NSGA-Ⅱ算法的渣漿泵多目標(biāo)參數(shù)優(yōu)化
發(fā)布時間:2018-12-07 08:36
【摘要】:由于渣漿泵普遍存在揚程低于設(shè)計揚程、效率低、磨損嚴重等問題,該文選取比轉(zhuǎn)速為75的離心式渣漿泵為研究對象,運用商用CFD求解軟件Flunet,選取RNG k-ε湍流模型與歐拉兩相流模型對其內(nèi)部流動進行計算。以離心式渣漿泵的效率、高效區(qū)作為優(yōu)化目標(biāo),結(jié)合Plackeet-Burman篩選試驗,將渣漿泵葉片的進口安放角、出口安放角與葉片包角作為優(yōu)化變量。采用均勻試驗設(shè)計安排樣本空間,利用RBF神經(jīng)網(wǎng)絡(luò)擬合優(yōu)化變量與優(yōu)化目標(biāo)間的映射關(guān)聯(lián),基于NSGA-Ⅱ遺傳算法進行多目標(biāo)尋優(yōu)。針對優(yōu)化所得的Pareto解集,選取其中效率最優(yōu)個體和高效區(qū)最優(yōu)個體與優(yōu)化前初始模型進行對比:分析了上述3個個體的通過數(shù)值模擬得到的性能曲線之間的差異,得到效率最優(yōu)與葉片進、出口安放角、葉片包角為21.76?、23.43?、145.56?,高效區(qū)最優(yōu)時為19.38?、22.68?、116.71?。通過試驗驗證,優(yōu)化后個體性能得到顯著提升,效率最優(yōu)個體的效率較初始個體的效率提高了3.81%,高效區(qū)最優(yōu)個體較初始個體高效區(qū)范圍提高了4.33%。給出并分析了上述3個個體在葉輪流道中間剖面上固相相對速度矢量及湍動能分布、葉片工作面、葉輪后蓋板的固相濃度分布差異。優(yōu)化結(jié)果表明,該優(yōu)化方法使葉輪的水力特性得到改善,提高了離心式渣漿泵的性能。
[Abstract]:Due to the problems of lower lift, lower efficiency and serious wear, the centrifugal slurry pump with 75 specific speed is selected as the research object, and the commercial CFD software Flunet, is used to solve the problem. RNG k- 蔚 turbulence model and Euler two-phase flow model are selected to calculate the internal flow. Taking the efficiency and high efficiency of centrifugal slurry pump as the optimization objective and combining with the Plackeet-Burman screening test, the inlet placement angle, the outlet placement angle and the blade envelope angle of the slurry pump blade were taken as the optimization variables. The uniform test design is used to arrange the sample space, and the RBF neural network is used to fit the mapping association between the optimization variables and the optimization objectives, and the multi-objective optimization is carried out based on the NSGA- 鈪,
本文編號:2366916
[Abstract]:Due to the problems of lower lift, lower efficiency and serious wear, the centrifugal slurry pump with 75 specific speed is selected as the research object, and the commercial CFD software Flunet, is used to solve the problem. RNG k- 蔚 turbulence model and Euler two-phase flow model are selected to calculate the internal flow. Taking the efficiency and high efficiency of centrifugal slurry pump as the optimization objective and combining with the Plackeet-Burman screening test, the inlet placement angle, the outlet placement angle and the blade envelope angle of the slurry pump blade were taken as the optimization variables. The uniform test design is used to arrange the sample space, and the RBF neural network is used to fit the mapping association between the optimization variables and the optimization objectives, and the multi-objective optimization is carried out based on the NSGA- 鈪,
本文編號:2366916
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