Zobrazeno 1 - 10
of 424
pro vyhledávání: '"VINUESA, Ricardo"'
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
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
Inverse problems have many applications in science and engineering. In Computer vision, several image restoration tasks such as inpainting, deblurring, and super-resolution can be formally modeled as inverse problems. Recently, methods have been deve
Externí odkaz:
http://arxiv.org/abs/2407.20784
Autor:
Jeon, Joogoo, Rabault, Jean, Vasanth, Joel, Alcántara-Ávila, Francisco, Baral, Shilaj, Vinuesa, Ricardo
Flow control is key to maximize energy efficiency in a wide range of applications. However, traditional flow-control methods face significant challenges in addressing non-linear systems and high-dimensional data, limiting their application in realist
Externí odkaz:
http://arxiv.org/abs/2407.17822
Are score function estimators an underestimated approach to learning with $k$-subset sampling? Sampling $k$-subsets is a fundamental operation in many machine learning tasks that is not amenable to differentiable parametrization, impeding gradient-ba
Externí odkaz:
http://arxiv.org/abs/2407.16058
A phenomenological description is presented to explain the intermediate and low-frequency/large-scale contributions to the wall-shear-stress (${\tau}_w$) and wall-pressure (${p}_w$) spectra of canonical turbulent boundary layers, which are well known
Externí odkaz:
http://arxiv.org/abs/2406.15733
Autor:
Ekelund, Jonah, Vinuesa, Ricardo, Khotyaintsev, Yuri, Henri, Pierre, Delzanno, Gian Luca, Markidis, Stefano
Artificial Intelligence (AI) has the potential to revolutionize space exploration by delegating several spacecraft decisions to an onboard AI instead of relying on ground control and predefined procedures. It is likely that there will be an AI/ML Pro
Externí odkaz:
http://arxiv.org/abs/2406.14297
This study aims to overcome the conventional deep-learning approaches based on convolutional neural networks, whose applicability to complex geometries and unstructured meshes is limited due to their inherent mesh dependency. We propose novel approac
Externí odkaz:
http://arxiv.org/abs/2406.03789