| 基于CFD的离心泵优化设计与试验 |
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| 引用本文： | 赵伟国,盛建萍,杨军虎,宋启策.基于CFD的离心泵优化设计与试验[J].农业工程学报,2015,31(21):125-131. |
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| 作者姓名： | 赵伟国 盛建萍 杨军虎 宋启策 |
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| 作者单位： | 1. 兰州理工大学能源与动力工程学院，兰州 730050; 2. 甘肃省流体机械及系统重点实验室，兰州 730050;,1. 兰州理工大学能源与动力工程学院，兰州 730050;,1. 兰州理工大学能源与动力工程学院，兰州 730050; 2. 甘肃省流体机械及系统重点实验室，兰州 730050;,1. 兰州理工大学能源与动力工程学院，兰州 730050; |
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| 基金项目： | 国家自然科学基金资助项目（51269011）；国家科技支撑计划（2013BAF01B02）；甘肃省自然科学基金资助项目（1208RJYA023）；甘肃省高等学校基本科研项目。 |
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| 摘 要： | 为了提高离心泵的效率，以叶轮效率最大为优化目标进行优化设计。对叶轮进行参数化设计，以实现叶轮几何形状的自动控制以及为优化计算提供优化变量。选择控制叶片积叠线周向定位的2个参数作为优化变量，以?3°～3°作为优化变量的约束范围。利用人工神经网络的学习功能，建立了目标函数与优化变量之间的映射关系。采用遗传算法寻找目标函数的最优值，得到优化变量约束范围内的最优叶轮模型。数值计算结果表明：在设计流量点1 200 m3/h时，优化后叶轮的效率较优化前提高了4.02个百分点，离心泵的效率提高了4.41个百分点，扬程提升了2.63 m。针对非设计工况点性能改善不明显这一问题，对原始蜗壳进行重新设计并与优化叶轮组合进行数值计算。在设计工况点效率提高了1.59%，在1.2倍设计工况点处效率提升了9.93%，在1.4倍设计工况点处效率提升了8.83%；较原始叶轮与原始蜗壳的组合，在设计工况点泵的效率提高了6%，在1.2倍设计工况点点效率提高了9.2%，在1.4倍设计工况点点效率提高了8.59%。优化拓宽了水泵运行的高效区，增强了泵的运行稳定性，离心泵的性能得到了优化，叶轮与蜗壳之间的匹配更合理。该研究对离心泵的优化设计提供了参考。
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| 关 键 词： | 离心泵 优化 算法 叶轮 效率 参数化 神经网络 |
| 收稿时间： | 5/4/2015 12:00:00 AM |
| 修稿时间： | 2015/9/15 0:00:00 |
Optimization design and experiment of centrifugal pump based on CFD |
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| Zhao Weiguo,Sheng Jianping,Yang Junhu and Song Qice.Optimization design and experiment of centrifugal pump based on CFD[J].Transactions of the Chinese Society of Agricultural Engineering,2015,31(21):125-131. |
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| Authors: | Zhao Weiguo Sheng Jianping Yang Junhu and Song Qice |
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| Institution: | 1. School of Energy and Power Engineering, Lanzhou University of Technology, LanZhou 730050, China2. Key Laboratory of Fluid machinery and Systems, Gansu Province, Lanzhou 730050, China,1. School of Energy and Power Engineering, Lanzhou University of Technology, LanZhou 730050, China,1. School of Energy and Power Engineering, Lanzhou University of Technology, LanZhou 730050, China2. Key Laboratory of Fluid machinery and Systems, Gansu Province, Lanzhou 730050, China and 1. School of Energy and Power Engineering, Lanzhou University of Technology, LanZhou 730050, China |
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| Abstract: | Abstract: The centrifugal pump is one of the most widely used fluid machinery. However, 3 problems i.e. lower efficiency, unsteady flow and bad cavitations performance are perplexing the development of centrifugal pump. For a single centrifugal pump, the impeller is one of the most important flow components, so it is selected as the optimum objective. Parametric fitting is a prerequisite in impeller optimization design. This process provides optimization variables and controls impeller automatically for the optimization design. Bezier curve and B-spline curve are used to reconstruct the impeller to obtain the profile of the blade and the meridional surface. The stacking point is reference point which defines the position of the two-dimensional (2D) blade section on a stream surface. This point is first defined on the 2D blade section, and then positioned on the corresponding stream surface in the meridional and tangential directions. Trailing edge is selected as stacking curve. Bezier-line-Bezier curve can be used to fit tangential location. The optimization variables are the angle between linear segment and vertical direction and the angle between the second Bezier curve and vertical direction with the span of 1, which 2 variables control the tangential position of stacking line on the 2D blade section. The range of -3°-3° is chosen as the constraint condition of optimization variables. Recently, CFD (computational fluid dynamics) technology has been widely applied to numerical computation of the three-dimensional viscous flow inside turbomachinery, which has made much progress. Meanwhile, many excellent optimization algorithms have been proposed. Fortunately, the CFD technology isn't confined to the research of centrifugal pump inner flow. Combining the CFD technology and optimization algorithm will play a very important role in the increase of pump efficiency, the decrease of flow loss and the extension of high-performance areas. An automatic optimization design platform for the centrifugal impellers is constructed by the genetic algorithm combined with the parameterization method and the commercial computational fluid dynamics software NUMECA. Based on the genetic algorithm and the artificial neural network, a new optimization method for the optimization of a centrifugal impeller is presented. Different from the traditional optimization method, the performance of centrifugal impeller is predicted with the CFD technology in the new developed method. The relationship between objective function and optimization variables is established by the learning function of artificial neural network. The results show that the efficiency of impeller achieves the maximum, when the angle between linear segment and vertical direction is -2.886° and the angle between the second Bezier curve and vertical direction with the span of 1 is 1.31°. Compared with the original, the efficiency is improved by 4.02% for optimum impeller in the design point. The centrifugal pump efficiency is increased by 4.41%, and the head is increased by 2.63 m. Volute is one of important flow components and has a great effect on the single centrifugal pump. The loss in volute is very great with optimized impeller, or with original one, especially in the large flow area. The volute is redesigned and the numerical simulation of modified volute with optimum impeller is performed for the flow field analysis of the flow passage components. The efficiency is improved by 1.59% compared with the pump with optimum impeller and original volute in design point, and by 6% compared with the pump with original impeller and original volute in design point. The performance of centrifugal pump is optimized, and the purpose of energy saving is achieved. These findings confirm that the optimization design method is effective for the centrifugal impellers. |
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| Keywords: | centrifugal pumps optimization algorithms impellers efficiency parameterization artificial neural network |
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