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Reinforcement learning for bluff body active flow control in experiments and simulations.
Fan, Dixia; Yang, Liu; Wang, Zhicheng; Triantafyllou, Michael S; Karniadakis, George Em.
Afiliação
  • Fan D; Department of Mechanical Engineering, Massachusetts Institute Technology, Cambridge, MA 02139.
  • Yang L; Sea Grant College Program, Massachusetts Institute of Technology, Cambridge, MA 02139.
  • Wang Z; Division of Applied Mathematics, Brown University, Providence, RI 02912.
  • Triantafyllou MS; Division of Applied Mathematics, Brown University, Providence, RI 02912.
  • Karniadakis GE; Department of Mechanical Engineering, Massachusetts Institute Technology, Cambridge, MA 02139; mistetri@mit.edu.
Proc Natl Acad Sci U S A ; 117(42): 26091-26098, 2020 10 20.
Artigo em Inglês | MEDLINE | ID: mdl-33020279
ABSTRACT
We have demonstrated the effectiveness of reinforcement learning (RL) in bluff body flow control problems both in experiments and simulations by automatically discovering active control strategies for drag reduction in turbulent flow. Specifically, we aimed to maximize the power gain efficiency by properly selecting the rotational speed of two small cylinders, located parallel to and downstream of the main cylinder. By properly defining rewards and designing noise reduction techniques, and after an automatic sequence of tens of towing experiments, the RL agent was shown to discover a control strategy that is comparable to the optimal strategy found through lengthy systematically planned control experiments. Subsequently, these results were verified by simulations that enabled us to gain insight into the physical mechanisms of the drag reduction process. While RL has been used effectively previously in idealized computer flow simulation studies, this study demonstrates its effectiveness in experimental fluid mechanics and verifies it by simulations, potentially paving the way for efficient exploration of additional active flow control strategies in other complex fluid mechanics applications.
Texto completo: Disponível Coleções: Bases de dados internacionais Base de dados: MEDLINE Idioma: Inglês Revista: Proc Natl Acad Sci U S A Ano de publicação: 2020 Tipo de documento: Artigo

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Texto completo: Disponível Coleções: Bases de dados internacionais Base de dados: MEDLINE Idioma: Inglês Revista: Proc Natl Acad Sci U S A Ano de publicação: 2020 Tipo de documento: Artigo
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