Adaptive control of transonic buffet and buffeting flow with deep reinforcement learning Article Swipe
Kai Ren
,
Chuanqiang Gao
,
Neng Xiong
,
Weiwei Zhang
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1063/5.0189662
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1063/5.0189662
The optimal control of flow and fluid–structure interaction (FSI) systems often requires an accurate model of the controlled system. However, for strongly nonlinear systems, acquiring an accurate dynamic model is a significant challenge. In this study, we employ the deep reinforcement learning (DRL) method, which does not rely on an accurate model of the controlled system, to address the control of transonic buffet (unstable flow) and transonic buffeting (structural vibration). DRL uses a deep neural network to describe the control law and optimizes it based on data obtained from interaction between control law and flow or FSI system. This study analyzes the mechanism of transonic buffet and transonic buffeting to guide the design of control system. Aiming at the control of transonic buffet, which is an unstable flow system, the control law optimized by DRL can quickly suppress fluctuating load of buffet by taking the lift coefficient as feedback signal. For the frequency lock-in phenomenon in transonic buffeting flow, which is an unstable FSI system, we add the moment coefficient and pitching displacement to feedback signal to observe pitching vibration mode. The control law optimized by DRL can also effectively eliminate or reduce pitching vibration displacement of airfoil and buffet load. The simulation results in this study show that DRL can adapt to the control of two different dynamic modes: typical forced response and FSI instability under transonic buffet, so it has a wide application prospect in the design of control laws for complex flow or FSI systems.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1063/5.0189662
- https://pubs.aip.org/aip/pof/article-pdf/doi/10.1063/5.0189662/18703634/016143_1_5.0189662.pdf
- OA Status
- bronze
- Cited By
- 10
- References
- 60
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4391147628
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4391147628Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1063/5.0189662Digital Object Identifier
- Title
-
Adaptive control of transonic buffet and buffeting flow with deep reinforcement learningWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-01-01Full publication date if available
- Authors
-
Kai Ren, Chuanqiang Gao, Neng Xiong, Weiwei ZhangList of authors in order
- Landing page
-
https://doi.org/10.1063/5.0189662Publisher landing page
- PDF URL
-
https://pubs.aip.org/aip/pof/article-pdf/doi/10.1063/5.0189662/18703634/016143_1_5.0189662.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
bronzeOpen access status per OpenAlex
- OA URL
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https://pubs.aip.org/aip/pof/article-pdf/doi/10.1063/5.0189662/18703634/016143_1_5.0189662.pdfDirect OA link when available
- Concepts
-
Transonic, Aeroelasticity, Aerodynamics, Airfoil, Control theory (sociology), Lift coefficient, Physics, Aerospace engineering, Engineering, Computer science, Turbulence, Mechanics, Control (management), Artificial intelligence, Reynolds numberTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
10Total citation count in OpenAlex
- Citations by year (recent)
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2025: 6, 2024: 4Per-year citation counts (last 5 years)
- References (count)
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60Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| primary_location.raw_source_name | Physics of Fluids |
| primary_location.landing_page_url | https://doi.org/10.1063/5.0189662 |
| publication_date | 2024-01-01 |
| publication_year | 2024 |
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