裴 吉,甘星城,王文杰,袁寿其,唐亚静.基于人工神经网络的管道泵进水流道性能优化[J].农业机械学报,2018,49(9):130-137.
PEI Ji,GAN Xingcheng,WANG Wenjie,YUAN Shouqi,TANG Yajing.Hydraulic Optimization on Inlet Pipe of Vertical Inline Pump Based on Artificial Neural Network[J].Transactions of the Chinese Society for Agricultural Machinery,2018,49(9):130-137.
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基于人工神经网络的管道泵进水流道性能优化   [下载全文]
Hydraulic Optimization on Inlet Pipe of Vertical Inline Pump Based on Artificial Neural Network   [Download Pdf][in English]
投稿时间:2018-02-27  
DOI:10.6041/j.issn.1000-1298.2018.09.015
中文关键词:  管道泵  进水弯管  自动优化  人工神经网络  粒子群算法
基金项目:国家自然科学基金项目(51879121)、“青蓝工程”项目和江苏大学“青年骨干教师培养工程”项目
作者单位
裴 吉 江苏大学 
甘星城 江苏大学 
王文杰 江苏大学 
袁寿其 江苏大学 
唐亚静 江苏大学 
中文摘要:立式管道泵是一种具有进口弯管的单级单吸离心泵,常被应用于安装空间受限的地方。由于进口的特殊结构,该泵不可避免地产生了一定程度的能量损失,从而降低了整体的效率。为了提高管道泵的性能,基于人工神经网络进行了肘形进水流道的优化研究。进水流道的形状可由流道中线和各截面的形状控制,选择五阶贝塞尔曲线拟合流道中线,三阶贝塞尔曲线拟合截面控制参数沿流道中线的变化趋势。考虑到泵实际安装需求,选取进水流道的11个参数为优化变量,泵效率为优化目标。采用拉丁方试验设计方法设计了149个进水流道方案,应用人工神经网络建立了泵效率与11个设计变量间的高精度非线性数学表达式,采用粒子群算法对数学表达式进行了优化,得到了肘形进水流道的最优参数组合。研究结果表明:计算结果与试验结果在小流量和设计流量下呈现出很好的一致性;人工神经网络(ANN)能够准确反映泵效率和设计变量之间的关系,优化后预测值与计算值之间的偏差为0.32%;优化后的模型相对于原始模型效率提高了1.17个百分点,扬程提高了0.23m,高效运行区得到拓宽;相比于原始进口管,优化后进口管内流动得到改善。
PEI Ji  GAN Xingcheng  WANG Wenjie  YUAN Shouqi  TANG Yajing
Jiangsu University,Jiangsu University,Jiangsu University,Jiangsu University and Jiangsu University
Key Words:vertical inline pump  bent pipe  automatic optimization  artificial neural network  particle swarm optimization
Abstract:Vertical inline pump is a single stage single suction centrifugal pump with a bent pipe before the impeller, which is usually used in where the constraint is installation space such as pumphouses. But these unavoidable bents before the impeller inlet also result in the hydraulic losses at the entry of the pump and the decrease of efficiency. In order to improve the performance of a vertical inline pump, an optimization on inlet pipe was proposed based on artificial neural network (ANN) and particle swarm optimization (PSO). The profile of inlet pipe was controlled by the mid curve and the shape of cross sections. The shape of mid curve was fitted by using a fifth ordered Bezier curve and the trend of parameters of cross sections along the mid curve were fitted by third ordered Bezier curves. Considering the real installation of the pump, totally 11 design parameters of inlet pipe were set as the design variables and the efficiency of the pump was set as the objective function. In order to build high precision ANN model between the objective function and the 11 design variables, totally 149 groups of sample data were created by using Latin hypercube sampling. After that, the ANN model was solved for the optimum solution of the design variables of inlet pipe by using particle swarm optimization. The result showed that there was a good agreement between computational results and experimental results; the ANN model could accurately fit the objective function and variables, the deviation between predicted value and actual value was 0.32%; after optimization, the efficiency and head of the pump was increased by 1.17 percentage points and 0.23m, respectively. The high efficiency period was also expended. Compared with the original inlet pipe, the flow condition in inlet pipe was improved after optimization.

Transactions of the Chinese Society for Agriculture Machinery (CSAM), in charged of China Association for Science and Technology (CAST), sponsored by CSAM and Chinese Academy of Agricultural Mechanization Science(CAAMS), started publication in 1957. It is the earliest interdisciplinary journal in Chinese which combines agricultural and engineering. It always closely grasps the development direction of agriculture engineering disciplines and the published papers represent the highest academic level of agriculture engineering in China. Currently, nearly 8,000 papers have been already published. There are around 3,000 papers contributed to the journal each year, but only around 600 of them will be accepted. Transactions of CSAM focuses on a wide range of agricultural machinery, irrigation, electronics, robotics, agro-products engineering, biological energy, agricultural structures and environment and more. Subjects in Transactions of the CSAM have been embodied by many internationally well-known index systems, such as: EI Compendex, CA, CSA, etc.

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