Cnn-based flow control device modelling on aerodynamic airfoils

HIGHLIGHTS

  • who: Koldo Portal-Porras from the University of have published the article: CNN-based flow control device modelling on aerodynamic airfoils, in the Journal: Scientific Reports Scientific Reports
  • what: As demonstrated by Ballesteros-Coll et_al6, this model is suitable for this kind of problems, since a global relative error of 3.784% of this model in comparison with the fully-resolved model was obtained in that study. The aim of using neural_networks to predict flows is to reduce the computational time required to run CFD simulations. In the present work, the implementation of flow control . . .

     

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