Learning active flow control strategies of a swept wing by intelligent wind tunnel
An intelligent wind tunnel using an active learning approach automates flow control experiments to discover the aerodynamic impact of sweeping jet on a swept wing. A Gaussian process regression model is established to study the jet actuator’s performance at various attack and flap deflection angles....
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2024-09-01
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| Series: | Theoretical and Applied Mechanics Letters |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2095034924000540 |
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| author | Yusi Wu Tingwei Ji Xinyu Lv Changdong Zheng Zhixian Ye Fangfang Xie |
| author_facet | Yusi Wu Tingwei Ji Xinyu Lv Changdong Zheng Zhixian Ye Fangfang Xie |
| author_sort | Yusi Wu |
| collection | DOAJ |
| description | An intelligent wind tunnel using an active learning approach automates flow control experiments to discover the aerodynamic impact of sweeping jet on a swept wing. A Gaussian process regression model is established to study the jet actuator’s performance at various attack and flap deflection angles. By selectively focusing on the most informative experiments, the proposed framework was able to predict 3721 wing conditions from just 55 experiments, significantly reducing the number of experiments required and leading to faster and cost-effective predictions. The results show that the angle of attack and flap deflection angle are coupled to affect the effectiveness of the sweeping jet. Meanwhile, increasing the jet momentum coefficient can contribute to lift enhancement; a momentum coefficient of 3% can increase the lift coefficient by at most 0.28. Additionally, the improvement effects are more pronounced when actuators are placed closer to the wing root. |
| format | Article |
| id | doaj-art-37fdea5d6aa64a28a368e3c52cdba6d1 |
| institution | Kabale University |
| issn | 2095-0349 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Theoretical and Applied Mechanics Letters |
| spelling | doaj-art-37fdea5d6aa64a28a368e3c52cdba6d12024-12-17T04:59:30ZengElsevierTheoretical and Applied Mechanics Letters2095-03492024-09-01145100543Learning active flow control strategies of a swept wing by intelligent wind tunnelYusi Wu0Tingwei Ji1Xinyu Lv2Changdong Zheng3Zhixian Ye4Fangfang Xie5School of Aeronautics and Astronautics, Zhejiang University, Hangzhou 310027, ChinaSchool of Aeronautics and Astronautics, Zhejiang University, Hangzhou 310027, ChinaSchool of Aeronautics and Astronautics, Zhejiang University, Hangzhou 310027, ChinaSchool of Aeronautics and Astronautics, Zhejiang University, Hangzhou 310027, ChinaFair Friend Institute of Intelligent Manufacturing, Hangzhou Vocational & Technical College, Hangzhou 310018, ChinaCorresponding author.; School of Aeronautics and Astronautics, Zhejiang University, Hangzhou 310027, ChinaAn intelligent wind tunnel using an active learning approach automates flow control experiments to discover the aerodynamic impact of sweeping jet on a swept wing. A Gaussian process regression model is established to study the jet actuator’s performance at various attack and flap deflection angles. By selectively focusing on the most informative experiments, the proposed framework was able to predict 3721 wing conditions from just 55 experiments, significantly reducing the number of experiments required and leading to faster and cost-effective predictions. The results show that the angle of attack and flap deflection angle are coupled to affect the effectiveness of the sweeping jet. Meanwhile, increasing the jet momentum coefficient can contribute to lift enhancement; a momentum coefficient of 3% can increase the lift coefficient by at most 0.28. Additionally, the improvement effects are more pronounced when actuators are placed closer to the wing root.http://www.sciencedirect.com/science/article/pii/S2095034924000540Active flow controlSweeping jetActive learningGaussian process regression |
| spellingShingle | Yusi Wu Tingwei Ji Xinyu Lv Changdong Zheng Zhixian Ye Fangfang Xie Learning active flow control strategies of a swept wing by intelligent wind tunnel Theoretical and Applied Mechanics Letters Active flow control Sweeping jet Active learning Gaussian process regression |
| title | Learning active flow control strategies of a swept wing by intelligent wind tunnel |
| title_full | Learning active flow control strategies of a swept wing by intelligent wind tunnel |
| title_fullStr | Learning active flow control strategies of a swept wing by intelligent wind tunnel |
| title_full_unstemmed | Learning active flow control strategies of a swept wing by intelligent wind tunnel |
| title_short | Learning active flow control strategies of a swept wing by intelligent wind tunnel |
| title_sort | learning active flow control strategies of a swept wing by intelligent wind tunnel |
| topic | Active flow control Sweeping jet Active learning Gaussian process regression |
| url | http://www.sciencedirect.com/science/article/pii/S2095034924000540 |
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