CFD和人工神经网络中叶轮叶片形状参数与性能参数的预测研究

Q2 Materials Science
F. Jin, R. Tao, R. Xiao
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引用次数: 0

摘要

叶片形状参数对离心泵的性能有显著影响。为了揭示这些参数与泵性能之间的关系,以单通道为研究对象,通过数值模拟计算了其性能,并在实验台上进行了性能测量。提出了优化后的人工神经网络,并证明其具有较高的准确率。保留数千条后,总响应的人工神经网络相关系数可达0.997以上。通过灰色关联分析,找出了影响性能参数的种类和程度。结果表明,前缘叶片角对反作用力、扬程和最小压力的影响较大,而包叶角对效率的影响较大。建立了多元线性回归模型,量化了叶片形状参数对性能影响的权重和趋势。研究结果可为离心叶轮的优化设计提供参考指导,以提高泵的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Prediction and investigation between impeller blade shape parameters and performance parameters in CFD and ANN
The blade shape parameters have a remarkable effect on the centrifugal pump performance. In order to reveal the relationship between these parameters and pump performance, a single channel was regarded as the research object to calculate its performance by numerical simulation, and the performance was measured on an experimental rig. The optimized ANN is proposed, and it is proved to be highly accurate. The ANN correlation coefficient of the total response could be above 0.997 after thousands of retaining. The sorts and degrees affecting performance parameters were found out by gray relation analysis. It was found that the blade angles at the leading edge were more influential for reaction force, head and minimum pressure, while the wrap angles had greater impact for efficiency. Furthermore, a multiple linear regression model was established to quantify the weight and trend of the influence of blade shape parameters on performance. The results provide a reference guide for the optimized design of centrifugal impeller to improve pump performance.
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来源期刊
Engineering Solid Mechanics
Engineering Solid Mechanics Materials Science-Metals and Alloys
CiteScore
3.00
自引率
0.00%
发文量
21
期刊介绍: Engineering Solid Mechanics (ESM) is an online international journal for publishing high quality peer reviewed papers in the field of theoretical and applied solid mechanics. The primary focus is to exchange ideas about investigating behavior and properties of engineering materials (such as metals, composites, ceramics, polymers, FGMs, rocks and concretes, asphalt mixtures, bio and nano materials) and their mechanical characterization (including strength and deformation behavior, fatigue and fracture, stress measurements, etc.) through experimental, theoretical and numerical research studies. Researchers and practitioners (from deferent areas such as mechanical and manufacturing, aerospace, railway, bio-mechanics, civil and mining, materials and metallurgy, oil, gas and petroleum industries, pipeline, marine and offshore sectors) are encouraged to submit their original, unpublished contributions.
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