Machine-learning and CFD based optimization and comprehensive experimental study on diagonal flow fan for energy conservation and efficiency enhancement
The diagonal flow fan, known for its innovative airflow guiding technique, presents a dual advantage of heightened efficiency and reduced noise levels. Despite garnering attention in energy production systems, research on optimizing outlet guide vanes for diagonal flow fan performance remains limite...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-12-01
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| Series: | Engineering Applications of Computational Fluid Mechanics |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/19942060.2024.2310608 |
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| author | Shuiqing Zhou Yu Luo Zijian Mao Laifa Lu Weiping Feng |
| author_facet | Shuiqing Zhou Yu Luo Zijian Mao Laifa Lu Weiping Feng |
| author_sort | Shuiqing Zhou |
| collection | DOAJ |
| description | The diagonal flow fan, known for its innovative airflow guiding technique, presents a dual advantage of heightened efficiency and reduced noise levels. Despite garnering attention in energy production systems, research on optimizing outlet guide vanes for diagonal flow fan performance remains limited. This study introduces a novel machine learning approach, utilizing Opt LHD sampling instead of traditional simulations and experiments to accumulate ample sample data. The fusion of LS-SVM and improved GA-PSO is employed for multi-objective optimization of outlet guide vanes, aiming to simultaneously enhance fan performance and reduce energy consumption. Experimental assessments reveal the optimized fan's significant improvements, including a 106 Pa increase in total pressure, a 3.6 dB reduction in noise levels, and a remarkable 16.3% enhancement in total pressure efficiency. These results highlight the robustness of the machine learning approach in optimizing diagonal flow fan exit guide vanes, effectively addressing the research gap in this area. Additionally, numerical analysis of internal flow dynamics and acoustic properties pre and post-optimization uncovers the intrinsic mechanisms influencing the overall performance of the diagonal flow fan. |
| format | Article |
| id | doaj-art-fdc69c11f2da4e0983a94cd47b98b80d |
| institution | OA Journals |
| issn | 1994-2060 1997-003X |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Engineering Applications of Computational Fluid Mechanics |
| spelling | doaj-art-fdc69c11f2da4e0983a94cd47b98b80d2025-08-20T02:20:56ZengTaylor & Francis GroupEngineering Applications of Computational Fluid Mechanics1994-20601997-003X2024-12-0118110.1080/19942060.2024.2310608Machine-learning and CFD based optimization and comprehensive experimental study on diagonal flow fan for energy conservation and efficiency enhancementShuiqing Zhou0Yu Luo1Zijian Mao2Laifa Lu3Weiping Feng4College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou, People’s Republic of ChinaCollege of Mechanical Engineering, Zhejiang University of Technology, Hangzhou, People’s Republic of ChinaCollege of Mechanical Engineering, Zhejiang University of Technology, Hangzhou, People’s Republic of ChinaCollege of Mechanical Engineering, Zhejiang University of Technology, Hangzhou, People’s Republic of ChinaElectrical Products Testing Laboratory, Zhejiang Fangyuan Test Group Co., LTD, Hangzhou, People’s Republic of ChinaThe diagonal flow fan, known for its innovative airflow guiding technique, presents a dual advantage of heightened efficiency and reduced noise levels. Despite garnering attention in energy production systems, research on optimizing outlet guide vanes for diagonal flow fan performance remains limited. This study introduces a novel machine learning approach, utilizing Opt LHD sampling instead of traditional simulations and experiments to accumulate ample sample data. The fusion of LS-SVM and improved GA-PSO is employed for multi-objective optimization of outlet guide vanes, aiming to simultaneously enhance fan performance and reduce energy consumption. Experimental assessments reveal the optimized fan's significant improvements, including a 106 Pa increase in total pressure, a 3.6 dB reduction in noise levels, and a remarkable 16.3% enhancement in total pressure efficiency. These results highlight the robustness of the machine learning approach in optimizing diagonal flow fan exit guide vanes, effectively addressing the research gap in this area. Additionally, numerical analysis of internal flow dynamics and acoustic properties pre and post-optimization uncovers the intrinsic mechanisms influencing the overall performance of the diagonal flow fan.https://www.tandfonline.com/doi/10.1080/19942060.2024.2310608Diagonal flow fanoutlet guide vaneCFDimproved GA-PSOmulti-objective optimisation |
| spellingShingle | Shuiqing Zhou Yu Luo Zijian Mao Laifa Lu Weiping Feng Machine-learning and CFD based optimization and comprehensive experimental study on diagonal flow fan for energy conservation and efficiency enhancement Engineering Applications of Computational Fluid Mechanics Diagonal flow fan outlet guide vane CFD improved GA-PSO multi-objective optimisation |
| title | Machine-learning and CFD based optimization and comprehensive experimental study on diagonal flow fan for energy conservation and efficiency enhancement |
| title_full | Machine-learning and CFD based optimization and comprehensive experimental study on diagonal flow fan for energy conservation and efficiency enhancement |
| title_fullStr | Machine-learning and CFD based optimization and comprehensive experimental study on diagonal flow fan for energy conservation and efficiency enhancement |
| title_full_unstemmed | Machine-learning and CFD based optimization and comprehensive experimental study on diagonal flow fan for energy conservation and efficiency enhancement |
| title_short | Machine-learning and CFD based optimization and comprehensive experimental study on diagonal flow fan for energy conservation and efficiency enhancement |
| title_sort | machine learning and cfd based optimization and comprehensive experimental study on diagonal flow fan for energy conservation and efficiency enhancement |
| topic | Diagonal flow fan outlet guide vane CFD improved GA-PSO multi-objective optimisation |
| url | https://www.tandfonline.com/doi/10.1080/19942060.2024.2310608 |
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