| Applying deep reinforcement learning to active flow control in weakly turbulent conditions F Ren, J Rabault, H Tang Physics of Fluids 33 (3), 2021 | 128 | 2021 |
| Deep reinforcement learning in fluid mechanics: A promising method for both active flow control and shape optimization J Rabault, F Ren, W Zhang, H Tang, H Xu Journal of Hydrodynamics 32, 234-246, 2020 | 106 | 2020 |
| Improved lattice Boltzmann modeling of binary flow based on the conservative Allen-Cahn equation F Ren, B Song, MC Sukop, H Hu Physical Review E 94 (2), 023311, 2016 | 101 | 2016 |
| Active control of vortex-induced vibration of a circular cylinder using machine learning F Ren, C Wang, H Tang Physics of Fluids 31 (9), 2019 | 98 | 2019 |
| Active flow control using machine learning: A brief review F Ren, H Hu, H Tang Journal of Hydrodynamics 32, 247-253, 2020 | 82 | 2020 |
| Bluff body uses deep-reinforcement-learning trained active flow control to achieve hydrodynamic stealth F Ren, C Wang, H Tang Physics of Fluids 33 (9), 2021 | 43 | 2021 |
| A comparative analysis of the effective and local slip lengths for liquid flows over a trapped nanobubble H Hu, D Wang, F Ren, L Bao, NV Priezjev, J Wen International Journal of Multiphase Flow 104, 166-173, 2018 | 22 | 2018 |
| Terminal shape and velocity of a rising bubble by phase-field-based incompressible Lattice Boltzmann model F Ren, B Song, MC Sukop Advances in water resources 97, 100-109, 2016 | 18 | 2016 |
| A GPU-accelerated solver for turbulent flow and scalar transport based on the lattice Boltzmann method F Ren, B Song, Y Zhang, H Hu Computers & Fluids 173, 29-36, 2018 | 17 | 2018 |
| Drag reduction on micro-structured hydrophobic surfaces due to surface tension effect BW Song, F Ren, HB Hu, YH Guo | 14 | 2014 |
| Lattice Boltzmann simulation of liquid–vapor system by incorporating a surface tension term BW Song, F Ren, HB Hu, QG Huang Chinese Physics B 24 (1), 014703, 2015 | 12 | 2015 |
| Machine learning for flow control: Applications and development trends F Ren, C Gao, H Tang Acta Aeronautica et Astronautica Sinica 42 (4), 152-166, 2021 | 11 | 2021 |
| On the magnetic nanoparticle injection strategy for hyperthermia treatment Q Jiang, F Ren, C Wang, Z Wang, G Kefayati, S Kenjeres, K Vafai, Y Liu, ... International Journal of Mechanical Sciences 235, 107707, 2022 | 8 | 2022 |
| Lattice Boltzmann simulations of turbulent channel flow and heat transport by incorporating the Vreman model F Ren, B Song, H Hu Applied Thermal Engineering 129, 463-471, 2018 | 8 | 2018 |
| Applying reinforcement learning to mitigate wake-induced lift fluctuation of a wall-confined circular cylinder in tandem configuration Z Xie, H Hu, J Chen, J Song, T Lu, F Ren Physics of Fluids 35 (5), 2023 | 5 | 2023 |
| Reconstructing turbulent velocity information for arbitrarily gappy flow fields using the deep convolutional neural network F Zhang, H Hu, F Ren, H Zhang, P Du Physics of Fluids 34 (12), 2022 | 5 | 2022 |
| Enhancing propulsion performance of a flexible heaving foil through dynamically adjusting its flexibility C Wang, F Ren, H Tang Bioinspiration & biomimetics 14 (6), 064002, 2019 | 5 | 2019 |
| Wake recognition of a blunt body based on convolutional neural network D Xiangbo, C Shaoqiang, H Jingyao, Z Fan, H Haibao, R Feng Chinese Journal of Theoretical and Applied Mechanics 54 (1), 59-67, 2022 | 4 | 2022 |
| Liquid Metal Flows in Manifold Microchannel Heat Sinks H Hu, S Kuravi, F Ren, P Hsu ASME International Mechanical Engineering Congress and Exposition 46590 …, 2014 | 4 | 2014 |
| Enhancing heat transfer from a circular cylinder undergoing vortex induced vibration based on reinforcement learning F Ren, F Zhang, Y Zhu, Z Wang, F Zhao Applied Thermal Engineering 236, 121919, 2024 | 3 | 2024 |
Hui TangThe Hong Kong Polytechnic University在 polyu.edu.hk 的电子邮件经过验证
