| 基于径向基神经网络的叶轮轴面投影图优化 |
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| 引用本文： | 王文杰,裴吉,袁寿其,张金凤,许长征,张帆.基于径向基神经网络的叶轮轴面投影图优化[J].农业机械学报,2015,46(6):78-83. |
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| 作者姓名： | 王文杰 裴吉 袁寿其 张金凤 许长征 张帆 |
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| 作者单位： | 江苏大学,江苏大学,江苏大学,江苏大学,宜兴优纳特机械有限公司,江苏大学 |
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| 基金项目： | “十二五”国家科技支撑计划资助项目（2011BAF14B04）、国家自然科学基金资助项目（51349004）、江苏省自然科学基金青年基金资助项目（BK20140554）、中国博士后科学基金面上资助项目（2014M560402）、江苏省博士后科研资助项目（1401069B）、江苏省普通高校研究生科研创新计划资助项目（KYLX_1042）和江苏省高校优势学科建设工程资助项目（PAPD） |
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| 摘 要： | 为了提高余热排出泵的效率,采用拉丁超立方试验设计方法对叶轮轴面投影图上的前盖板圆弧半径、后盖板圆弧半径、前盖板倾角和后盖板倾角4个几何变量进行35组叶轮方案设计,应用ANSYS CFX 14.5软件对余热排出泵进行定常数值模拟,得到设计工况下的效率,应用径向基神经网络建立效率与轴面投影图的4个几何变量之间的近似模型,最后采用遗传算法对近似模型进行极值寻优,获得最优的轴面投影图几何参数组合。研究结果表明:对比原始泵的数值模拟性能曲线和试验外特性能曲线,两者吻合较好;径向基神经网络能较好地预测泵设计点效率;优化的轴面投影图使得余热排出泵的水力效率提高了6.18个百分点,改善了叶轮内流场特性。因此,叶轮轴面投影图的优化设计方法是可行的。
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| 关 键 词： | 余热排出泵 叶轮轴面投影图 优化 拉丁超立方试验设计 径向基神经网络 遗传算法 |
| 收稿时间： | 2014/7/21 0:00:00 |
Optimization of Impeller Meridional Shape Based on Radial Basis Neural Network |
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| Wang Wenji,Pei Ji,Yuan Shouqi,Zhang Jinfeng,Xu Changzheng and Zhang Fan.Optimization of Impeller Meridional Shape Based on Radial Basis Neural Network[J].Transactions of the Chinese Society of Agricultural Machinery,2015,46(6):78-83. |
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| Authors: | Wang Wenji Pei Ji Yuan Shouqi Zhang Jinfeng Xu Changzheng and Zhang Fan |
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| Institution: | Jiangsu University,Jiangsu University,Jiangsu University,Jiangsu University,Yixing Unite Machinery Co., Ltd. and Jiangsu University |
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| Abstract: | To improve the efficiency of residual heat removal pump, 35 impellers, whose design variables are the radius of shroud arc, radius of hub arc, angle of shroud and angle of hub, were designed by Latin hypercube sampling method. 3D steady simulation was conducted to get the efficiency under designed flow rate by ANSYS CFX 14.5 software. A radial basis neural network was used to build the approximation model between efficiency and design variables. Finally, the best combination of the design variables was obtained by solving the approximation model with genetic algorithm. The results showed that performance curve simulated by CFD had a good agreement with that of experiment. The deviations of efficiency and head between numerical result and experimental result were -2.1% and -3.7%, respectively. Compared the efficiency predicted by CFD with that predicted by radial basis neural network, the deviation was only 0.02%, thus the radial basis neural network can predict the efficiency under design condition accurately. The efficiency of the optimal pump was 76.75% and the optimization made an increase in efficiency by a percentage of 6.18. The optimization improved the velocity and turbulence kinetic energy distributions in the impeller. The vortexes disappeared and the velocity became uniform at the shroud. Thus, the optimization process for the impeller meridional shape was practical. |
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| Keywords: | Residual heat removal pump Impeller meridional shape Optimization Latin hypercube sampling method Radial basis neural network Genetic algorithm |
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