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基于徑向基神經(jīng)網(wǎng)絡的葉輪軸面投影圖優(yōu)化

發(fā)布時間:2018-08-11 17:53
【摘要】:為了提高余熱排出泵的效率,采用拉丁超立方試驗設計方法對葉輪軸面投影圖上的前蓋板圓弧半徑、后蓋板圓弧半徑、前蓋板傾角和后蓋板傾角4個幾何變量進行35組葉輪方案設計,應用ANSYS CFX 14.5軟件對余熱排出泵進行定常數(shù)值模擬,得到設計工況下的效率,應用徑向基神經(jīng)網(wǎng)絡建立效率與軸面投影圖的4個幾何變量之間的近似模型,最后采用遺傳算法對近似模型進行極值尋優(yōu),獲得最優(yōu)的軸面投影圖幾何參數(shù)組合。研究結果表明:對比原始泵的數(shù)值模擬性能曲線和試驗外特性能曲線,兩者吻合較好;徑向基神經(jīng)網(wǎng)絡能較好地預測泵設計點效率;優(yōu)化的軸面投影圖使得余熱排出泵的水力效率提高了6.18個百分點,改善了葉輪內(nèi)流場特性。因此,葉輪軸面投影圖的優(yōu)化設計方法是可行的。
[Abstract]:In order to improve the efficiency of waste heat pump, a Latin hypercube experimental design method was used to design 35 impellers with four geometric variables, i.e. the arc radius of the front cover plate, the arc radius of the rear cover plate, the inclination angle of the front cover plate and the inclination angle of the rear cover plate. The constant value of the waste heat pump was simulated by ANSYS CFX 14.5 software. An approximate model between the efficiency and the four geometric variables of the axial projection graph is established by using the radial basis function neural network. Finally, the optimal geometric parameters of the axial projection graph are obtained by optimizing the approximate model with the genetic algorithm. The radial basis function neural network (RBF-NN) can predict the design point efficiency of the pump, and the optimized axial projection diagram can improve the hydraulic efficiency of the waste heat pump by 6.18 percentage points and improve the flow field characteristics of the impeller. Therefore, the optimization design method of the axial projection diagram of the impeller is feasible.
【作者單位】: 江蘇大學國家水泵及系統(tǒng)工程技術研究中心;宜興優(yōu)納特機械有限公司;
【基金】:“十二五”國家科技支撐計劃資助項目(2011BAF14B04) 國家自然科學基金資助項目(51349004) 江蘇省自然科學基金青年基金資助項目(BK20140554) 中國博士后科學基金面上資助項目(2014M560402) 江蘇省博士后科研資助項目(1401069B) 江蘇省普通高校研究生科研創(chuàng)新計劃資助項目(KYLX_1042) 江蘇省高校優(yōu)勢學科建設工程資助項目(PAPD)
【分類號】:TM623

【參考文獻】

相關期刊論文 前10條

1 楊軍虎;張云周;孟瑞鋒;王s,

本文編號:2177776


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