no code implementations • 4 Sep 2023 • Pol Suárez, Francisco Alcántara-Ávila, Arnau Miró, Jean Rabault, Bernat Font, Oriol Lehmkuhl, R. Vinuesa
This paper presents for the first time successful results of active flow control with multiple independently controlled zero-net-mass-flux synthetic jets.
Multi-agent Reinforcement Learning reinforcement-learning +1
1 code implementation • 5 Apr 2023 • Colin Vignon, Jean Rabault, Joel Vasanth, Francisco Alcántara-Ávila, Mikael Mortensen, Ricardo Vinuesa
We show in a case study that MARL DRL is able to discover an advanced control strategy that destabilizes the spontaneous RBC double-cell pattern, changes the topology of RBC by coalescing adjacent convection cells, and actively controls the resulting coalesced cell to bring it to a new stable configuration.
no code implementations • 23 Feb 2022 • Fabio Pino, Lorenzo Schena, Jean Rabault, Miguel A. Mendez
Machine learning frameworks such as Genetic Programming (GP) and Reinforcement Learning (RL) are gaining popularity in flow control.
no code implementations • 17 Dec 2020 • Trygve K. Løken, Thea J. Ellevold, Reyna G. Ramirez de la Torre, Jean Rabault, Atle Jensen
This work is an important milestone towards performing detailed 2D flow measurements under the ice in the Arctic, which we anticipate will help perform much needed direct observations of the dynamics happening under sea ice.
Fluid Dynamics Atmospheric and Oceanic Physics
1 code implementation • 26 Apr 2020 • Hongwei Tang, Jean Rabault, Alexander Kuhnle, Yan Wang, Tongguang Wang
This paper focuses on the active flow control of a computational fluid dynamics simulation over a range of Reynolds numbers using deep reinforcement learning (DRL).
Fluid Dynamics
4 code implementations • 23 Aug 2019 • Jonathan Viquerat, Jean Rabault, Alexander Kuhnle, Hassan Ghraieb, Aurélien Larcher, Elie Hachem
Deep Reinforcement Learning (DRL) has recently spread into a range of domains within physics and engineering, with multiple remarkable achievements.
Computational Engineering, Finance, and Science
1 code implementation • 12 Aug 2019 • Paul Garnier, Jonathan Viquerat, Jean Rabault, Aurélien Larcher, Alexander Kuhnle, Elie Hachem
In this work, we conduct a detailed review of existing DRL applications to fluid mechanics problems.
4 code implementations • 25 Jun 2019 • Jean Rabault, Alexander Kuhnle
In the case of AFC trained with Computational Fluid Mechanics (CFD) data, it was found that the CFD part, rather than the training of the Artificial Neural Network, was the limiting factor for speed of execution.
Computational Physics
1 code implementation • 8 Jan 2019 • Jean Rabault, Graig Sutherland, Olav Gundersen, Atle Jensen, Aleksey Marchenko, Øyvind Breivik
Recent changes in the climate and extent of the sea ice, together with increased economic activity and research interest in these regions, are driving factors for new measurements of sea ice dynamics.
Atmospheric and Oceanic Physics
no code implementations • 5 Sep 2018 • Jean Rabault, Graig Sutherland, Atle Jensen, Kai H Christensen, Aleksey Marchenko
PIV data are also consistent with exponential wave amplitude attenuation, and a POD analysis reveals the existence of mean flows under the ice that are a consequence of the displacement and packing of the ice induced by the gradient in the wave-induced stress.
Fluid Dynamics
2 code implementations • 31 Aug 2018 • Jean Rabault, Ulysse Reglade, Nicolas Cerardi, Miroslav Kuchta, Atle Jensen
Here we show that Deep Reinforcement Learning can achieve a stable active control of the Karman vortex street behind a two-dimensional cylinder.
Fluid Dynamics
4 code implementations • 23 Aug 2018 • Jean Rabault, Miroslav Kuchta, Atle Jensen, Ulysse Reglade, Nicolas Cerardi
This is performed while using small mass flow rates for the actuation, on the order of 0. 5% of the mass flow rate intersecting the cylinder cross section once a new pseudo-periodic shedding regime is found.
Fluid Dynamics