Study on Optimization Design of Airfoil Transonic Buffet with Reinforcement Learning Method
Transonic buffet is a phenomenon of large self-excited shock oscillations caused by shock wave-boundary layer interaction, which is one of the common flow instability problems in aeronautical engineering. This phenomenon involves unsteady flow, which makes optimal design more difficult. In this pape...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-05-01
|
| Series: | Aerospace |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2226-4310/10/5/486 |
| _version_ | 1797601636806295552 |
|---|---|
| author | Hao Chen Chuanqiang Gao Jifei Wu Kai Ren Weiwei Zhang |
| author_facet | Hao Chen Chuanqiang Gao Jifei Wu Kai Ren Weiwei Zhang |
| author_sort | Hao Chen |
| collection | DOAJ |
| description | Transonic buffet is a phenomenon of large self-excited shock oscillations caused by shock wave-boundary layer interaction, which is one of the common flow instability problems in aeronautical engineering. This phenomenon involves unsteady flow, which makes optimal design more difficult. In this paper, aerodynamic shape optimization design is combined with reinforcement learning to address the problem of transonic buffet. Using the deep deterministic policy gradient (DDPG) algorithm, a reinforcement learning-based design framework for airfoil shape optimization was constructed to achieve effective suppression of transonic buffet. The aerodynamic characteristics of the airfoil were calculated by the computational fluid dynamics (CFD) method. After optimization, the buffet onset angles of attack of the airfoils NACA0012 and RAE2822 were improved by 2° and 1.2° respectively, and the lift-drag ratios improved by 83.5% and 30% respectively. Summarizing and verifying the optimization results, three general conclusions can be drawn to improve the buffet performance: (1) narrowing of the leading edge of the airfoil; (2) situating the maximum thickness position at approximately 0.4 times the chord length; (3) increasing the thickness of the trailing edge within a certain range. This paper established a reinforcement learning-based unsteady optimal design method that enables the optimization of unsteady problems, including buffet. |
| first_indexed | 2024-03-11T04:03:09Z |
| format | Article |
| id | doaj.art-ed3cbb75f2ba48af93e862d4c6debb94 |
| institution | Directory Open Access Journal |
| issn | 2226-4310 |
| language | English |
| last_indexed | 2024-03-11T04:03:09Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Aerospace |
| spelling | doaj.art-ed3cbb75f2ba48af93e862d4c6debb942023-11-18T00:01:02ZengMDPI AGAerospace2226-43102023-05-0110548610.3390/aerospace10050486Study on Optimization Design of Airfoil Transonic Buffet with Reinforcement Learning MethodHao Chen0Chuanqiang Gao1Jifei Wu2Kai Ren3Weiwei Zhang4School of Aeronautics, Northwestern Polytechnical University, Xi’an 710072, ChinaSchool of Aeronautics, Northwestern Polytechnical University, Xi’an 710072, ChinaAerodynamics Research and Development Center, Mianyang 621000, ChinaSchool of Aeronautics, Northwestern Polytechnical University, Xi’an 710072, ChinaSchool of Aeronautics, Northwestern Polytechnical University, Xi’an 710072, ChinaTransonic buffet is a phenomenon of large self-excited shock oscillations caused by shock wave-boundary layer interaction, which is one of the common flow instability problems in aeronautical engineering. This phenomenon involves unsteady flow, which makes optimal design more difficult. In this paper, aerodynamic shape optimization design is combined with reinforcement learning to address the problem of transonic buffet. Using the deep deterministic policy gradient (DDPG) algorithm, a reinforcement learning-based design framework for airfoil shape optimization was constructed to achieve effective suppression of transonic buffet. The aerodynamic characteristics of the airfoil were calculated by the computational fluid dynamics (CFD) method. After optimization, the buffet onset angles of attack of the airfoils NACA0012 and RAE2822 were improved by 2° and 1.2° respectively, and the lift-drag ratios improved by 83.5% and 30% respectively. Summarizing and verifying the optimization results, three general conclusions can be drawn to improve the buffet performance: (1) narrowing of the leading edge of the airfoil; (2) situating the maximum thickness position at approximately 0.4 times the chord length; (3) increasing the thickness of the trailing edge within a certain range. This paper established a reinforcement learning-based unsteady optimal design method that enables the optimization of unsteady problems, including buffet.https://www.mdpi.com/2226-4310/10/5/486transonic buffetreinforcement learningairfoil optimization designcomputational fluid dynamicsdeep deterministic policy gradient algorithm |
| spellingShingle | Hao Chen Chuanqiang Gao Jifei Wu Kai Ren Weiwei Zhang Study on Optimization Design of Airfoil Transonic Buffet with Reinforcement Learning Method Aerospace transonic buffet reinforcement learning airfoil optimization design computational fluid dynamics deep deterministic policy gradient algorithm |
| title | Study on Optimization Design of Airfoil Transonic Buffet with Reinforcement Learning Method |
| title_full | Study on Optimization Design of Airfoil Transonic Buffet with Reinforcement Learning Method |
| title_fullStr | Study on Optimization Design of Airfoil Transonic Buffet with Reinforcement Learning Method |
| title_full_unstemmed | Study on Optimization Design of Airfoil Transonic Buffet with Reinforcement Learning Method |
| title_short | Study on Optimization Design of Airfoil Transonic Buffet with Reinforcement Learning Method |
| title_sort | study on optimization design of airfoil transonic buffet with reinforcement learning method |
| topic | transonic buffet reinforcement learning airfoil optimization design computational fluid dynamics deep deterministic policy gradient algorithm |
| url | https://www.mdpi.com/2226-4310/10/5/486 |
| work_keys_str_mv | AT haochen studyonoptimizationdesignofairfoiltransonicbuffetwithreinforcementlearningmethod AT chuanqianggao studyonoptimizationdesignofairfoiltransonicbuffetwithreinforcementlearningmethod AT jifeiwu studyonoptimizationdesignofairfoiltransonicbuffetwithreinforcementlearningmethod AT kairen studyonoptimizationdesignofairfoiltransonicbuffetwithreinforcementlearningmethod AT weiweizhang studyonoptimizationdesignofairfoiltransonicbuffetwithreinforcementlearningmethod |