Recent advances in applying deep reinforcement learning for flow control: Perspectives and future directions
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 …
past decade and has provided effective control strategies in high-dimensional and non …
A review on deep reinforcement learning for fluid mechanics: An update
In the past couple of years, the interest of the fluid mechanics community for deep
reinforcement learning techniques has increased at fast pace, leading to a growing …
reinforcement learning techniques has increased at fast pace, leading to a growing …
Deep reinforcement learning for flow control exploits different physics for increasing Reynolds number regimes
The increase in emissions associated with aviation requires deeper research into novel
sensing and flow-control strategies to obtain improved aerodynamic performances. In this …
sensing and flow-control strategies to obtain improved aerodynamic performances. In this …
Effective control of two-dimensional Rayleigh–Bénard convection: Invariant multi-agent reinforcement learning is all you need
Rayleigh–Bénard convection (RBC) is a recurrent phenomenon in a number of industrial
and geoscience flows and a well-studied system from a fundamental fluid-mechanics …
and geoscience flows and a well-studied system from a fundamental fluid-mechanics …
Dynamic feature-based deep reinforcement learning for flow control of circular cylinder with sparse surface pressure sensing
This study proposes a self-learning algorithm for closed-loop cylinder wake control targeting
lower drag and lower lift fluctuations with the additional challenge of sparse sensor …
lower drag and lower lift fluctuations with the additional challenge of sparse sensor …
[HTML][HTML] Thermodynamics-informed neural network for recovering supercritical fluid thermophysical information from turbulent velocity data
Recent research has highlighted the potential of supercritical fluids under high-pressure
transcritical conditions to achieve microconfined turbulence as a result of the thermophysical …
transcritical conditions to achieve microconfined turbulence as a result of the thermophysical …
Machine-learning flow control with few sensor feedback and measurement noise
A comparative assessment of machine-learning (ML) methods for active flow control is
performed. The chosen benchmark problem is the drag reduction of a two-dimensional …
performed. The chosen benchmark problem is the drag reduction of a two-dimensional …
Unsteady cylinder wakes from arbitrary bodies with differentiable physics-assisted neural network
This work describes a hybrid predictive framework configured as a coarse-grained surrogate
for reconstructing unsteady fluid flows around multiple cylinders of diverse configurations …
for reconstructing unsteady fluid flows around multiple cylinders of diverse configurations …
Active flow control for bluff body drag reduction using reinforcement learning with partial measurements
Active flow control for drag reduction with reinforcement learning (RL) is performed in the
wake of a two-dimensional square bluff body at laminar regimes with vortex shedding …
wake of a two-dimensional square bluff body at laminar regimes with vortex shedding …
Establishment and validation of a relationship model between nozzle experiments and CFD results based on convolutional neural network
T Yu, X Wu, Y Yu, R Li, H Zhang - Aerospace Science and Technology, 2023 - Elsevier
The acquisition of experimental data in a supersonic wind tunnel often faces challenges of
complexity and high costs. Furthermore, there are limitations in the control of experimental …
complexity and high costs. Furthermore, there are limitations in the control of experimental …