From the 1 of 10 linked papers with an AI index.
10 papers
Can Explicit Subgrid Models Enhance Implicit LES Simulations? A Very High-Order Solver Perspective
Gonzalo Rubio, Gerasimos Ntoukas, Miguel Chávez-Módena +5
The paper studies how explicit Vreman subgrid‑scale models interact with the inherent numerical dissipation of very high‑order discontinuous Galerkin methods for turbulent flow sim…
High-lift Wing Separation Control via Bayesian Optimization and Deep Reinforcement Learning
Ricard MontalÃ, Bernat Font, Oriol Lehmkuhl +2
This study investigates active flow control (AFC) of a 30P30N high-lift wing at a Reynolds number Re = 450,000 and angle of attack = 23 using wallresolved large-ed…
Leveraging unstructured grids for direct numerical simulations of wall turbulence
Amirreza Rouhi, Vishal Kumar, Wen Wu +2
Towards computational cost saving for direct numerical simulations (DNSs) of wall turbulence, we formulate an unstructured grid-generation framework, termed -grid, where the wa…
Deep Reinforcement Learning for Active Flow Control around a Three-Dimensional Flow-Separated Wing at Re = 1,000
R. MontalÃ, B. Font, P. Suárez +4
This study explores the use of deep reinforcement learning (DRL) for active flow control (AFC) to reduce flow separation on wings at high angles of attack. Concretely, here the DRL…
Discovering Flow Separation Control Strategies in 3D Wings via Deep Reinforcement Learning
R. MontalÃ, B. Font, P. Suárez +4
In this work, deep reinforcement learning (DRL) is applied to active flow control (AFC) over a threedimensional SD7003 wing at a Reynolds number of Re = 60,000 and angle of attack…
SmartFlow: A CFD-solver-agnostic deep reinforcement learning framework for computational fluid dynamics on HPC platforms
Maochao Xiao, Yuning Wang, Felix Rodach +15
Deep reinforcement learning (DRL) is emerging as a powerful tool for fluid-dynamics research, encompassing active flow control, autonomous navigation, turbulence modeling and disco…