Search Results for author:
Found 12 papers, 8 papers with code
Active flow control for three-dimensional cylinders through deep reinforcement learning
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
Effective control of two-dimensional Rayleigh--Bénard convection: invariant multi-agent reinforcement learning is all you need
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.
Comparative analysis of machine learning methods for active flow control
Machine learning frameworks such as Genetic Programming (GP) and Reinforcement Learning (RL) are gaining popularity in flow control.
Bringing optical fluid motion analysis to the field: a methodology using an open source ROV as camera system and rising bubbles as tracers
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
Robust active flow control over a range of Reynolds numbers using an artificial neural network trained through deep reinforcement learning
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
Direct shape optimization through deep reinforcement learning
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
A review on Deep Reinforcement Learning for Fluid Mechanics
In this work, we conduct a detailed review of existing DRL applications to fluid mechanics problems.
Accelerating Deep Reinforcement Learning of Active Flow Control strategies through a multi-environment approach
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
An Open Source, Versatile, Affordable Waves in Ice Instrument for Scientific Measurements in the Polar Regions
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
Experiments on wave propagation in grease ice: combined wave gauges and PIV measurements
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
Deep Reinforcement Learning achieves flow control of the 2D Karman Vortex Street
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
Artificial Neural Networks trained through Deep Reinforcement Learning discover control strategies for active flow control
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
