Deep reinforcement learning for flow control exploits different physics for increasing reynolds number regimes

HIGHLIGHTS

• who: Pau Varela and colleagues from the Universitat Politu00e8cnica de Valu00e8ncia, Valencia, Spain have published the article: Deep Reinforcement Learning for Flow Control Exploits Different Physics for Increasing Reynolds Number Regimes, in the Journal: Actuators 2022, 11, 359. of /2022/
• what: In this work a range beyond those previously considered was studied and compared with results obtained using classical flow-control methods. The aim of this work is, indeed, to demonstrate the capabilities of the HPC resources available today to perform aerodynamic optimization with DRL-based control at much higher Reynolds numbers than the ones . . .
  

   

  

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