Optimization of end-wall fence in turbine based on response surface methodology and genetic algorithm
Secondary flow loss accounts for a large proportion of the internal flow loss in turbine stages. The use of end-wall fences can effectively reduce secondary flow loss. In this study, the White cascade was taken as the research object, and the position of the end-wall fence was parameterized. Based o...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-09-01
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| Series: | Case Studies in Thermal Engineering |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214157X23006123 |
| _version_ | 1797729928057192448 |
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| author | Xu Han Qiuliang Zhu Jiandong Guan Zhongwen Liu Bochuan Yao Zhonghe Han |
| author_facet | Xu Han Qiuliang Zhu Jiandong Guan Zhongwen Liu Bochuan Yao Zhonghe Han |
| author_sort | Xu Han |
| collection | DOAJ |
| description | Secondary flow loss accounts for a large proportion of the internal flow loss in turbine stages. The use of end-wall fences can effectively reduce secondary flow loss. In this study, the White cascade was taken as the research object, and the position of the end-wall fence was parameterized. Based on the response surface method, the mapping relationship between the fence position and the isentropic expansion efficiency was obtained, and a surrogate model was constructed. Finally, single-objective and multiobjective optimizations were carried out using genetic algorithms to obtain the optimal fence position parameters. The results showed that the existence of the fence can not only reduce secondary flow but also effectively reduce shock losses when the fence is located at the rear of the passage. Therefore, the optimization effect of the blade position is best when it is located at the end of the passage. When the flow deviates significantly from the design condition, the end-wall fence can significantly reduce the low-speed region on the pressure side of the fence. Therefore, the fence can play a greater role in low-load conditions with significant deviations from the design condition. After optimizing the design conditions, when H is 128.6 mm, V is 66.93 mm, and A is 56.48°, the isentropic expansion efficiency is the highest, reaching 96.160%. After optimizing the multi-inlet angle at low load conditions, when θ is 56.07°, R is 129.7 mm, and A is 64.36°, the comprehensive optimization result is the best, and the isentropic expansion efficiencies at inlet angles of 0°, 10°, and 45° are 96.098%, 96.050%, and 93.930%, respectively. The research results can provide a reference for the design of turbine flow. |
| first_indexed | 2024-03-12T11:36:41Z |
| format | Article |
| id | doaj.art-aca2b69cdd964a41b7984dee73fffcf0 |
| institution | Directory Open Access Journal |
| issn | 2214-157X |
| language | English |
| last_indexed | 2024-03-12T11:36:41Z |
| publishDate | 2023-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Case Studies in Thermal Engineering |
| spelling | doaj.art-aca2b69cdd964a41b7984dee73fffcf02023-09-01T05:01:48ZengElsevierCase Studies in Thermal Engineering2214-157X2023-09-0149103306Optimization of end-wall fence in turbine based on response surface methodology and genetic algorithmXu Han0Qiuliang Zhu1Jiandong Guan2Zhongwen Liu3Bochuan Yao4Zhonghe Han5Corresponding author.; Hebei Key Laboratory of Low Carbon and High-Efficiency Power Generation Technology, North China Electric Power University, Baoding, 071003, Hebei, ChinaHebei Key Laboratory of Low Carbon and High-Efficiency Power Generation Technology, North China Electric Power University, Baoding, 071003, Hebei, ChinaHebei Key Laboratory of Low Carbon and High-Efficiency Power Generation Technology, North China Electric Power University, Baoding, 071003, Hebei, ChinaHebei Key Laboratory of Low Carbon and High-Efficiency Power Generation Technology, North China Electric Power University, Baoding, 071003, Hebei, ChinaHebei Key Laboratory of Low Carbon and High-Efficiency Power Generation Technology, North China Electric Power University, Baoding, 071003, Hebei, ChinaHebei Key Laboratory of Low Carbon and High-Efficiency Power Generation Technology, North China Electric Power University, Baoding, 071003, Hebei, ChinaSecondary flow loss accounts for a large proportion of the internal flow loss in turbine stages. The use of end-wall fences can effectively reduce secondary flow loss. In this study, the White cascade was taken as the research object, and the position of the end-wall fence was parameterized. Based on the response surface method, the mapping relationship between the fence position and the isentropic expansion efficiency was obtained, and a surrogate model was constructed. Finally, single-objective and multiobjective optimizations were carried out using genetic algorithms to obtain the optimal fence position parameters. The results showed that the existence of the fence can not only reduce secondary flow but also effectively reduce shock losses when the fence is located at the rear of the passage. Therefore, the optimization effect of the blade position is best when it is located at the end of the passage. When the flow deviates significantly from the design condition, the end-wall fence can significantly reduce the low-speed region on the pressure side of the fence. Therefore, the fence can play a greater role in low-load conditions with significant deviations from the design condition. After optimizing the design conditions, when H is 128.6 mm, V is 66.93 mm, and A is 56.48°, the isentropic expansion efficiency is the highest, reaching 96.160%. After optimizing the multi-inlet angle at low load conditions, when θ is 56.07°, R is 129.7 mm, and A is 64.36°, the comprehensive optimization result is the best, and the isentropic expansion efficiencies at inlet angles of 0°, 10°, and 45° are 96.098%, 96.050%, and 93.930%, respectively. The research results can provide a reference for the design of turbine flow.http://www.sciencedirect.com/science/article/pii/S2214157X23006123TurbineWet steamEnd-wall fenceResponse surface methodGenetic algorithmShock wave |
| spellingShingle | Xu Han Qiuliang Zhu Jiandong Guan Zhongwen Liu Bochuan Yao Zhonghe Han Optimization of end-wall fence in turbine based on response surface methodology and genetic algorithm Case Studies in Thermal Engineering Turbine Wet steam End-wall fence Response surface method Genetic algorithm Shock wave |
| title | Optimization of end-wall fence in turbine based on response surface methodology and genetic algorithm |
| title_full | Optimization of end-wall fence in turbine based on response surface methodology and genetic algorithm |
| title_fullStr | Optimization of end-wall fence in turbine based on response surface methodology and genetic algorithm |
| title_full_unstemmed | Optimization of end-wall fence in turbine based on response surface methodology and genetic algorithm |
| title_short | Optimization of end-wall fence in turbine based on response surface methodology and genetic algorithm |
| title_sort | optimization of end wall fence in turbine based on response surface methodology and genetic algorithm |
| topic | Turbine Wet steam End-wall fence Response surface method Genetic algorithm Shock wave |
| url | http://www.sciencedirect.com/science/article/pii/S2214157X23006123 |
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