Browsing by Author "Font García, Bernat"

Now showing items 1-4 of 4

    • A data-driven wall-shear stress model for LES using gradient boosted decision trees 

      Radhakrishnan, Sarath; Adu Gyamfi, Lawrence; Miró Jané, Arnau; Font García, Bernat; Calafell Sandiumenge, Joan; Lehmkuhl Barba, Oriol (Springer Nature, 2021)
      Conference report
      Open Access
      With the recent advances in machine learning, data-driven strategies could augment wall modeling in large eddy simulation (LES). In this work, a wall model based on gradient boosted decision trees is presented. The model ...

    • Active flow control for three-dimensional cylinders through deep reinforcement learning 

      Suárez Morales, Pol; Alcántara Ávila, Francisco; Miró Jané, Arnau; Rabault, Jean; Font García, Bernat; Lehmkuhl Barba, Oriol; Vinuesa Moltiva, Ricardo (European Research Community on Flow, Turbulence, and Conbustion (ERCOFTAC), 2023)
      Conference report
      Open Access
      This paper presents for the first time successful results of active flow control with multiple independently controlled zero-net-mass-flux synthetic jets. The jets are placed on a three-dimensional cylinder along its span ...

    • Data-driven wall modeling for LES involving non-equilibrium boundary layer effects 

      Radhakrishnan, Sarath; Calafell Sandiumenge, Joan; Miró Jané, Arnau; Font García, Bernat; Lehmkuhl Barba, Oriol (2024-08-26)
      Article
      Open Access
      Purpose Wall-modeled large eddy simulation (LES) is a practical tool for solving wall-bounded flows with less computational cost by avoiding the explicit resolution of the near-wall region. However, its use is limited in ...

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

      Varela Martínez, Pau; Suárez Morales, Pol; Alcántara Ávila, Francisco; Miró Jané, Arnau; Rabault, Jean; Font García, Bernat; García Cuevas, Luis Miguel; Lehmkuhl Barba, Oriol; Vinuesa Moltiva, Ricardo (Multidisciplinary Digital Publishing Institute (MDPI), 2022-12-02)
      Article
      Open Access
      The increase in emissions associated with aviation requires deeper research into novel sensing and flow-control strategies to obtain improved aerodynamic performances. In this context, data-driven methods are suitable for ...