Closed-loop Flow Control Method Based on Deep Reinforcement Learning using a Co-flow Jet
A closed-loop control framework is developed for the co-flow jet (CFJ) airfoil by combining the numerical flow field environment of a CFJ0012 airfoil with a deep reinforcement learning (DRL) module called tensorforce integrated in Python. The DRL agent, which is trained through interacting with the...
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| Format: | Article |
| Language: | English |
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Isfahan University of Technology
2024-01-01
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| Series: | Journal of Applied Fluid Mechanics |
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| Online Access: | https://www.jafmonline.net/article_2388_dcef01bf948c73f7b118b5595f7804f4.pdf |
| _version_ | 1827368105001615360 |
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| author | Y. R. Zhao H. Y. Xu Z. Y. Xie |
| author_facet | Y. R. Zhao H. Y. Xu Z. Y. Xie |
| author_sort | Y. R. Zhao |
| collection | DOAJ |
| description | A closed-loop control framework is developed for the co-flow jet (CFJ) airfoil by combining the numerical flow field environment of a CFJ0012 airfoil with a deep reinforcement learning (DRL) module called tensorforce integrated in Python. The DRL agent, which is trained through interacting with the numerical flow field environment, is capable of acquiring a policy that instructs the mass flow rate of the CFJ to make the stalled airfoil at an angle of attack (AoA) of 18 degrees reach a specific high lift coefficient set to 2.0, thereby effectively suppressing flow separation on the upper surface of the airfoil. The subsequent test shows that the policy can be implemented to find a precise jet momentum coefficient of 0.049 to make the lift coefficient of the CFJ0012 airfoil reach 2.01 with a negligible error of 0.5%. Moreover, to evaluate the generalization ability of the policy trained at an AoA of 18 degrees, two additional tests are conducted at AoAs of 16 and 20 degrees. The results show that, although using the policy gained under another AoA cannot help the lift coefficient of the airfoil reach a set target of 2 accurately, the errors are acceptable with less than 5.5%, which means the policy trained under an AoA of 18 degrees can also be applied to other AoAs to some extent. This work is helpful for the practical application of CFJ technology, as the closed-loop control framework ensures good aerodynamic performance of the CFJ airfoil, even in complex and changeable flight conditions. |
| first_indexed | 2024-03-08T09:25:43Z |
| format | Article |
| id | doaj.art-25da32b54e2d4b0ebe10aa93816894bb |
| institution | Directory Open Access Journal |
| issn | 1735-3572 1735-3645 |
| language | English |
| last_indexed | 2024-03-08T09:25:43Z |
| publishDate | 2024-01-01 |
| publisher | Isfahan University of Technology |
| record_format | Article |
| series | Journal of Applied Fluid Mechanics |
| spelling | doaj.art-25da32b54e2d4b0ebe10aa93816894bb2024-01-31T09:46:43ZengIsfahan University of TechnologyJournal of Applied Fluid Mechanics1735-35721735-36452024-01-0117481682710.47176/jafm.17.4.22482388Closed-loop Flow Control Method Based on Deep Reinforcement Learning using a Co-flow JetY. R. Zhao0H. Y. Xu1Z. Y. Xie2National Key Laboratory of Science and Technology on Aerodynamic Design and Research, Northwestern Polytechnical University, Xi’an, 710072, ChinaNational Key Laboratory of Science and Technology on Aerodynamic Design and Research, Northwestern Polytechnical University, Xi’an, 710072, ChinaNational Key Laboratory of Science and Technology on Aerodynamic Design and Research, Northwestern Polytechnical University, Xi’an, 710072, ChinaA closed-loop control framework is developed for the co-flow jet (CFJ) airfoil by combining the numerical flow field environment of a CFJ0012 airfoil with a deep reinforcement learning (DRL) module called tensorforce integrated in Python. The DRL agent, which is trained through interacting with the numerical flow field environment, is capable of acquiring a policy that instructs the mass flow rate of the CFJ to make the stalled airfoil at an angle of attack (AoA) of 18 degrees reach a specific high lift coefficient set to 2.0, thereby effectively suppressing flow separation on the upper surface of the airfoil. The subsequent test shows that the policy can be implemented to find a precise jet momentum coefficient of 0.049 to make the lift coefficient of the CFJ0012 airfoil reach 2.01 with a negligible error of 0.5%. Moreover, to evaluate the generalization ability of the policy trained at an AoA of 18 degrees, two additional tests are conducted at AoAs of 16 and 20 degrees. The results show that, although using the policy gained under another AoA cannot help the lift coefficient of the airfoil reach a set target of 2 accurately, the errors are acceptable with less than 5.5%, which means the policy trained under an AoA of 18 degrees can also be applied to other AoAs to some extent. This work is helpful for the practical application of CFJ technology, as the closed-loop control framework ensures good aerodynamic performance of the CFJ airfoil, even in complex and changeable flight conditions.https://www.jafmonline.net/article_2388_dcef01bf948c73f7b118b5595f7804f4.pdfco-flow jetclosed-loop controlflow controllift enhancementdeep reinforcement learning |
| spellingShingle | Y. R. Zhao H. Y. Xu Z. Y. Xie Closed-loop Flow Control Method Based on Deep Reinforcement Learning using a Co-flow Jet Journal of Applied Fluid Mechanics co-flow jet closed-loop control flow control lift enhancement deep reinforcement learning |
| title | Closed-loop Flow Control Method Based on Deep Reinforcement Learning using a Co-flow Jet |
| title_full | Closed-loop Flow Control Method Based on Deep Reinforcement Learning using a Co-flow Jet |
| title_fullStr | Closed-loop Flow Control Method Based on Deep Reinforcement Learning using a Co-flow Jet |
| title_full_unstemmed | Closed-loop Flow Control Method Based on Deep Reinforcement Learning using a Co-flow Jet |
| title_short | Closed-loop Flow Control Method Based on Deep Reinforcement Learning using a Co-flow Jet |
| title_sort | closed loop flow control method based on deep reinforcement learning using a co flow jet |
| topic | co-flow jet closed-loop control flow control lift enhancement deep reinforcement learning |
| url | https://www.jafmonline.net/article_2388_dcef01bf948c73f7b118b5595f7804f4.pdf |
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