A Rapid Design Method for Centrifugal Pump Impellers Based on Machine Learning
Centrifugal pumps are widely used across various industries, and the design of high-efficiency centrifugal pumps is essential for energy savings and emission reductions. The development of centrifugal pump models primarily uses an iterative design approach combining direct and inverse problem-solvin...
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| Format: | Article |
| Language: | English |
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Isfahan University of Technology
2025-05-01
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| Series: | Journal of Applied Fluid Mechanics |
| Subjects: | |
| Online Access: | https://www.jafmonline.net/article_2669_0e45fbc2c7e111bc8a15727f44514bf3.pdf |
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| author | Y. Chen W. Li Y. Luo L. Ji S. Li Y. Long |
| author_facet | Y. Chen W. Li Y. Luo L. Ji S. Li Y. Long |
| author_sort | Y. Chen |
| collection | DOAJ |
| description | Centrifugal pumps are widely used across various industries, and the design of high-efficiency centrifugal pumps is essential for energy savings and emission reductions. The development of centrifugal pump models primarily uses an iterative design approach combining direct and inverse problem-solving based on one-dimensional flow theory. However, this semi-empirical, semi-theoretical design process is time-consuming and costly. To reduce development time and costs, this paper proposes a rapid impeller design method focused on hydraulic performance, integrating traditional similarity design theory with machine learning. The proposed model uses neural networks to predict empirical coefficients, determine key dimensions such as the impeller’s inlet diameter, outlet diameter, outlet width, and axial distance. Once these parameters are defined, the main dimensions of the impeller can be calculated. The blade profile is defined using a 5-point B´ezier curve. Variations in the cross-sectional area of the flow passage influence the internal flow state of the centrifugal pump, ultimately impacting its hydraulic efficiency. A genetic algorithm, guided by variations in the cross-sectional area of the flow passage, optimizes the blade profile, achieving an improved impeller flow path and completing the rapid design of the centrifuge. This method significantly shortens the development cycle and lowers design costs, making it a promising technique for future impeller designs. |
| format | Article |
| id | doaj-art-1e591f2eaf424f5ab8cd331dea0bdb1e |
| institution | DOAJ |
| issn | 1735-3572 1735-3645 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Isfahan University of Technology |
| record_format | Article |
| series | Journal of Applied Fluid Mechanics |
| spelling | doaj-art-1e591f2eaf424f5ab8cd331dea0bdb1e2025-08-20T03:09:38ZengIsfahan University of TechnologyJournal of Applied Fluid Mechanics1735-35721735-36452025-05-011871735174910.47176/jafm.18.7.32582669A Rapid Design Method for Centrifugal Pump Impellers Based on Machine LearningY. Chen0W. Li1Y. Luo2L. Ji3S. Li4Y. Long5Jiangsu University, National Research Center of Pumps, Zhenjiang, JiangSu, 212013, ChinaJiangsu University, National Research Center of Pumps, Zhenjiang, JiangSu, 212013, ChinaJiangsu University, National Research Center of Pumps, Zhenjiang, JiangSu, 212013, ChinaJiangsu University, National Research Center of Pumps, Zhenjiang, JiangSu, 212013, ChinaJiangsu University, National Research Center of Pumps, Zhenjiang, JiangSu, 212013, ChinaJiangsu University, National Research Center of Pumps, Zhenjiang, JiangSu, 212013, ChinaCentrifugal pumps are widely used across various industries, and the design of high-efficiency centrifugal pumps is essential for energy savings and emission reductions. The development of centrifugal pump models primarily uses an iterative design approach combining direct and inverse problem-solving based on one-dimensional flow theory. However, this semi-empirical, semi-theoretical design process is time-consuming and costly. To reduce development time and costs, this paper proposes a rapid impeller design method focused on hydraulic performance, integrating traditional similarity design theory with machine learning. The proposed model uses neural networks to predict empirical coefficients, determine key dimensions such as the impeller’s inlet diameter, outlet diameter, outlet width, and axial distance. Once these parameters are defined, the main dimensions of the impeller can be calculated. The blade profile is defined using a 5-point B´ezier curve. Variations in the cross-sectional area of the flow passage influence the internal flow state of the centrifugal pump, ultimately impacting its hydraulic efficiency. A genetic algorithm, guided by variations in the cross-sectional area of the flow passage, optimizes the blade profile, achieving an improved impeller flow path and completing the rapid design of the centrifuge. This method significantly shortens the development cycle and lowers design costs, making it a promising technique for future impeller designs.https://www.jafmonline.net/article_2669_0e45fbc2c7e111bc8a15727f44514bf3.pdfcentrifugal pumpmachine learningimpeller designcross-sectional areaneural network |
| spellingShingle | Y. Chen W. Li Y. Luo L. Ji S. Li Y. Long A Rapid Design Method for Centrifugal Pump Impellers Based on Machine Learning Journal of Applied Fluid Mechanics centrifugal pump machine learning impeller design cross-sectional area neural network |
| title | A Rapid Design Method for Centrifugal Pump Impellers Based on Machine Learning |
| title_full | A Rapid Design Method for Centrifugal Pump Impellers Based on Machine Learning |
| title_fullStr | A Rapid Design Method for Centrifugal Pump Impellers Based on Machine Learning |
| title_full_unstemmed | A Rapid Design Method for Centrifugal Pump Impellers Based on Machine Learning |
| title_short | A Rapid Design Method for Centrifugal Pump Impellers Based on Machine Learning |
| title_sort | rapid design method for centrifugal pump impellers based on machine learning |
| topic | centrifugal pump machine learning impeller design cross-sectional area neural network |
| url | https://www.jafmonline.net/article_2669_0e45fbc2c7e111bc8a15727f44514bf3.pdf |
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