35 citations · 77 across the 14 of their papers we have counts for
15 papers
Scaling WaterLily.jl with MPI and an improved geometric multigrid solver
Bernat Font, Marin Lauber, Tzu-Yao Huang +1
We present recent performance-oriented developments in WaterLily, a scale-resolving incompressible flow solver written in pure Julia that runs seamlessly on CPUs and GPUs of any ve…
Acceleration of an algebraic multigrid pressure solver using graph neural networks
Eric Chillón, Artur K. Lidtke, Nguyen Anh Khoa Doan +1
Solving the pressure-Poisson equation remains the primary computational bottleneck in incompressible unstructured flow solvers primarily due to the inherent sensitivity of traditio…
High-lift Wing Separation Control via Bayesian Optimization and Deep Reinforcement Learning
Ricard Montalà, 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-edd…
Can Explicit Subgrid Models Enhance Implicit LES Simulations? A Very High-Order Solver Perspective
Gonzalo Rubio, Gerasimos Ntoukas, Miguel Chávez-Módena +5
High-order discontinuous Galerkin (DG) methods offer excellent accuracy for turbulent-flow simulations and are increasingly attractive on GPU-oriented architectures, where high pol…
Deep Reinforcement Learning in Action: Real-Time Control of Vortex-Induced Vibrations
Hussam Sababha, Bernat Font, Mohammed Daqaq
This study showcases an experimental deployment of deep reinforcement learning (DRL) for active flow control (AFC) of vortex-induced vibrations (VIV) in a circular cylinder at a hi…
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…
Deep-reinforcement-learning-based separation control in a two-dimensional airfoil
Xavier Garcia, Arnau Miró, Pol Suárez +5
The aim of this study is to discover new active-flow-control (AFC) techniques for separation mitigation in a two-dimensional NACA 0012 airfoil at a Reynolds number of 3000. To find…
Towards Active Flow Control Strategies Through Deep Reinforcement Learning
Ricard Montalà, Bernat Font, Pol Suárez +3
This paper presents a deep reinforcement learning (DRL) framework for active flow control (AFC) to reduce drag in aerodynamic bodies. Tested on a 3D cylinder at Re = 100, the DRL a…
WaterLily.jl: A differentiable and backend-agnostic Julia solver for incompressible viscous flow around dynamic bodies
Gabriel D. Weymouth, Bernat Font
Integrating computational fluid dynamics (CFD) solvers into optimization and machine-learning frameworks is hampered by the rigidity of classic computational languages and the slow…
Active flow control for drag reduction through multi-agent reinforcement learning on a turbulent cylinder at
P. Suárez, F. Álcantara-Ávila, A. Miró +4
This study presents novel drag reduction active-flow-control (AFC) strategies} for a three-dimensional cylinder immersed in a flow at a Reynolds number based on freestream velocity…
Flow control of three-dimensional cylinders transitioning to turbulence via multi-agent reinforcement learning
P. Suárez, F. Alcántara-Ávila, J. Rabault +4
Designing active-flow-control (AFC) strategies for three-dimensional (3D) bluff bodies is a challenging task with critical industrial implications. In this study we explore the pot…
Active flow control of a turbulent separation bubble through deep reinforcement learning
Bernat Font, Francisco Alcántara-Ávila, Jean Rabault +2
The control efficacy of classical periodic forcing and deep reinforcement learning (DRL) is assessed for a turbulent separation bubble (TSB) at on the upstream region be…
Active flow control for three-dimensional cylinders through deep reinforcement learning
Pol Suárez, Francisco Alcántara-Ávila, Arnau Miró +4
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…
Deep reinforcement learning for flow control exploits different physics for increasing Reynolds-number regimes
Pau Varela, Pol Suárez, Francisco Alcántara-Ávila +6
Deep artificial neural networks (ANNs) used together with deep reinforcement learning (DRL) are receiving growing attention due to their capabilities to control complex problems. T…
Deep learning of the spanwise-averaged Navier-Stokes equations
Bernat Font, Gabriel D. Weymouth, Vinh-Tan Nguyen +1
Simulations of turbulent fluid flow around long cylindrical structures are computationally expensive because of the vast range of length scales, requiring simplifications such as d…