| Wall turbulence at high friction Reynolds numbers S Hoyas, M Oberlack, F Alcántara-Ávila, SV Kraheberger, J Laux Physical Review Fluids 7 (1), 014602, 2022 | 93 | 2022 |
| DNS of thermal channel flow up to Reτ= 2000 for medium to low Prandtl numbers F Alcántara-Ávila, S Hoyas, MJ Pérez-Quiles International journal of heat and mass transfer 127, 349-361, 2018 | 55 | 2018 |
| Turbulence statistics of arbitrary moments of wall-bounded shear flows: A symmetry approach M Oberlack, S Hoyas, SV Kraheberger, F Alcántara-Ávila, J Laux Physical Review Letters 128 (2), 024502, 2022 | 51 | 2022 |
| Direct numerical simulation of thermal channel flow for F Alcántara-Ávila, S Hoyas, MJ Pérez-Quiles Journal of Fluid Mechanics 916, A29, 2021 | 49 | 2021 |
| Deep reinforcement learning for flow control exploits different physics for increasing Reynolds number regimes P Varela, P Suárez, F Alcántara-Ávila, A Miró, J Rabault, B Font, ... Actuators 11 (12), 359, 2022 | 35 | 2022 |
| Direct numerical simulation of thermal channel flow for medium–high Prandtl numbers up to Reτ= 2000 F Alcántara-Ávila, S Hoyas International Journal of Heat and Mass Transfer 176, 121412, 2021 | 35 | 2021 |
| Effective control of two-dimensional Rayleigh–Bénard convection: Invariant multi-agent reinforcement learning is all you need C Vignon, J Rabault, J Vasanth, F Alcántara-Ávila, M Mortensen, ... Physics of Fluids 35 (6), 2023 | 27 | 2023 |
| A code for simulating heat transfer in turbulent channel flow F Lluesma-Rodríguez, F Alcantara-Avila, MJ Pérez-Quiles, S Hoyas Mathematics 9 (7), 756, 2021 | 22 | 2021 |
| Turbulent channel flow at Reτ = 10000 S Hoyas, M Oberlack, S Kraheberger, F Alcantara-Avila APS Division of Fluid Dynamics Meeting Abstracts, H19. 001, 2019 | 12 | 2019 |
| Evidences of persisting thermal structures in Couette flows F Alcántara-Ávila, S Gandía-Barberá, S Hoyas International Journal of Heat and Fluid Flow 76, 287-295, 2019 | 12 | 2019 |
| Active flow control for three-dimensional cylinders through deep reinforcement learning P Suárez, F Alcántara-Ávila, A Miró, J Rabault, B Font, O Lehmkuhl, ... arXiv preprint arXiv:2309.02462, 2023 | 8 | 2023 |
| Predicting coherent turbulent structures via deep learning D Schmekel, F Alcántara-Ávila, S Hoyas, R Vinuesa Frontiers in Physics 10, 888832, 2022 | 8 | 2022 |
| Active flow control of a turbulent separation bubble through deep reinforcement learning B Font, F Alcántara-Ávila, J Rabault, R Vinuesa, O Lehmkuhl Journal of Physics: Conference Series 2753 (1), 012022, 2024 | 7 | 2024 |
| Stratification effect on extreme-scale rolls in plane Couette flows S Gandía-Barberá, F Alcántara-Ávila, S Hoyas, V Avsarkisov Physical Review Fluids 6 (3), 034605, 2021 | 6 | 2021 |
| Active flow control for drag reduction through multi-agent reinforcement learning on a turbulent cylinder at P Suárez, F Alcantara-Avila, A Miró, J Rabault, B Font, O Lehmkuhl, ... arXiv preprint arXiv:2405.17655, 2024 | 3 | 2024 |
| Flow control of three-dimensional cylinders transitioning to turbulence via multi-agent reinforcement learning P Suárez, F Álcantara-Ávila, J Rabault, A Miró, B Font, O Lehmkuhl, ... arXiv preprint arXiv:2405.17210, 2024 | 2 | 2024 |
| Validation of symmetry-induced high moment velocity and temperature scaling laws in a turbulent channel flow F Alcántara-Ávila, LM García-Raffi, S Hoyas, M Oberlack Physical Review E 109 (2), 025104, 2024 | 1 | 2024 |
| Multi-agent reinforcement learning for the control of three-dimensional Rayleigh-B\'enard convection J Vasanth, J Rabault, F Alcántara-Ávila, M Mortensen, R Vinuesa arXiv preprint arXiv:2407.21565, 2024 | | 2024 |
| Advanced deep-reinforcement-learning methods for flow control: group-invariant and positional-encoding networks improve learning speed and quality J Jeon, J Rabault, J Vasanth, F Alcántara-Ávila, S Baral, R Vinuesa arXiv preprint arXiv:2407.17822, 2024 | | 2024 |
| Deep reinforcement learning for active flow control in a turbulent separation bubble B Font, F Alcántara-Ávila, J Rabault, R Vinuesa, O Lehmkuhl | | 2024 |
