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10 papers · 1 filter
Recent advances in applying deep reinforcement learning for flow control: perspectives and future directions
Colin Vignon, Jean Rabault, Ricardo Vinuesa
Deep reinforcement learning (DRL) has been applied to a variety of problems during the past decade, and has provided effective control strategies in high-dimensional and non-linear…
Effective control of two-dimensional Rayleigh--Bénard convection: invariant multi-agent reinforcement learning is all you need
Colin Vignon, Jean Rabault, Joel Vasanth +3
Rayleigh-Bénard convection (RBC) is a recurrent phenomenon in several industrial and geoscience flows and a well-studied system from a fundamental fluid-mechanics viewpoint. Howeve…
DRLinFluids -- An open-source python platform of coupling Deep Reinforcement Learning and OpenFOAM
Qiulei Wang, Lei Yan, Gang Hu +5
We propose an open-source python platform for applications of Deep Reinforcement Learning (DRL) in fluid mechanics. DRL has been widely used in optimizing decision-making in nonlin…
Comparative analysis of machine learning methods for active flow control
Fabio Pino, Lorenzo Schena, Jean Rabault +1
Machine learning frameworks such as Genetic Programming (GP) and Reinforcement Learning (RL) are gaining popularity in flow control. This work presents a comparative analysis of th…
Grid turbulence measurements with an acoustic Doppler current profiler
Trygve. K. Løken, David Roger Lande-Sudall, Atle Jensen +1
The motivation for this study is to investigate the abilities and limitations of a Nortek Signature1000 acoustic Doppler current profiler (ADCP) regarding fine-scale turbulence mea…
Turbulent kinetic energy dissipation from colliding ice floes
Trygve K. Løken, Aleksey Marchenko, Thea J. Ellevold +2
Increased knowledge about wave attenuation processes in sea ice, and hence atmosphere-wave-ice-ocean energy transfer, is necessary to improve sea ice dynamics models used for clima…