Zobrazeno 1 - 10
of 6 201
pro vyhledávání: '"Vinuesa, A."'
This study presents a transformer-based approach for fault-tolerant control in fixed-wing Unmanned Aerial Vehicles (UAVs), designed to adapt in real time to dynamic changes caused by structural damage or actuator failures. Unlike traditional Flight C
Externí odkaz:
http://arxiv.org/abs/2411.02975
For the last 140 years, the mechanisms of transport and dissipation of energy in a turbulent flow have not been completely understood due to the complexity of this phenomenon. The dissipation of energy due to turbulence is significative, and understa
Externí odkaz:
http://arxiv.org/abs/2410.23189
The increasing number of unmanned aerial vehicles (UAVs) in urban environments requires a strategy to minimize their environmental impact, both in terms of energy efficiency and noise reduction. In order to reduce these concerns, novel strategies for
Externí odkaz:
http://arxiv.org/abs/2409.17922
A gas bubble sitting at a liquid-gas interface can burst following the rupture of the thin liquid film separating it from the ambient, owing to the large surface energy of the resultant cavity. This bursting bubble forms capillary waves, a Worthingto
Externí odkaz:
http://arxiv.org/abs/2409.14897
The use of data-driven methods in fluid mechanics has surged dramatically in recent years due to their capacity to adapt to the complex and multi-scale nature of turbulent flows, as well as to detect patterns in large-scale simulations or experimenta
Externí odkaz:
http://arxiv.org/abs/2409.11992
Autor:
Cuéllar, Antonio, Güemes, Alejandro, Ianiro, Andrea, Flores, Óscar, Vinuesa, Ricardo, Discetti, Stefano
Publikováno v:
Cu\'ellar, A., G\"uemes, A., Ianiro, A., Flores, \'O., Vinuesa, R., Discetti, S.: Three-dimensional generative adversarial networks for turbulent flow estimation from wall measurements. J. Fluid Mech. 991, A1 (2024)
Different types of neural networks have been used to solve the flow sensing problem in turbulent flows, namely to estimate velocity in wall-parallel planes from wall measurements. Generative adversarial networks (GANs) are among the most promising me
Externí odkaz:
http://arxiv.org/abs/2409.06548
We conducted high-resolution large-eddy simulations (LESs) to explore the effects of opposition control (OC) on turbulent boundary layers (TBLs) over a wing at a chord-based Reynolds number (${Re}_c$) of 200,000. Two scenarios were studied: flow over
Externí odkaz:
http://arxiv.org/abs/2408.15588
Autor:
Deshpande, Rahul, Vinuesa, Ricardo
Publikováno v:
J. Fluid Mech. 997 (2024) A16
The present study investigates streamwise ($\overline{u^2}$) energy-transfer mechanisms in the inner and outer regions of turbulent boundary layers (TBLs). Particular focus is placed on the $\overline{u^2}$-production, its inter-component and wall-no
Externí odkaz:
http://arxiv.org/abs/2408.13539
The wall cycle in wall-bounded turbulent flows is a complex turbulence regeneration mechanism that remains not fully understood. This study explores the potential of deep reinforcement learning (DRL) for managing the wall regeneration cycle to achiev
Externí odkaz:
http://arxiv.org/abs/2408.06783
Multi-agent reinforcement learning for the control of three-dimensional Rayleigh-B\'enard convection
Autor:
Vasanth, Joel, Rabault, Jean, Alcántara-Ávila, Francisco, Mortensen, Mikael, Vinuesa, Ricardo
Deep reinforcement learning (DRL) has found application in numerous use-cases pertaining to flow control. Multi-agent RL (MARL), a variant of DRL, has shown to be more effective than single-agent RL in controlling flows exhibiting locality and transl
Externí odkaz:
http://arxiv.org/abs/2407.21565