Contribution of Reynolds stress distribution to the skin friction in wall-bounded flows K Fukagata, K Iwamoto, N Kasagi Physics of fluids 14 (11), L73-L76, 2002 | 776 | 2002 |
Super-resolution reconstruction of turbulent flows with machine learning K Fukami, K Fukagata, K Taira Journal of Fluid Mechanics 870, 106-120, 2019 | 593 | 2019 |
A theoretical prediction of friction drag reduction in turbulent flow by superhydrophobic surfaces K Fukagata, N Kasagi, P Koumoutsakos Physics of Fluids 18 (5), 051703, 2006 | 317 | 2006 |
Nonlinear mode decomposition with convolutional neural networks for fluid dynamics T Murata, K Fukami, K Fukagata Journal of Fluid Mechanics 882, A13, 2020 | 300 | 2020 |
Highly energy-conservative finite difference method for the cylindrical coordinate system K Fukagata, N Kasagi Journal of Computational Physics 181 (2), 478-498, 2002 | 232 | 2002 |
Machine-learning-based spatio-temporal super resolution reconstruction of turbulent flows K Fukami, K Fukagata, K Taira Journal of Fluid Mechanics 909, A9, 2021 | 228 | 2021 |
Microelectromechanical systems–based feedback control of turbulence for skin friction reduction N Kasagi, Y Suzuki, K Fukagata Annual Review of Fluid Mechanics 41, 231-251, 2009 | 219 | 2009 |
Assessment of supervised machine learning methods for fluid flows K Fukami, K Fukagata, K Taira Theoretical and Computational Fluid Dynamics 34, 497-519, 2020 | 192 | 2020 |
Direct numerical simulation of spatially developing turbulent boundary layers with uniform blowing or suction Y Kametani, K Fukagata Journal of Fluid Mechanics 681, 154-172, 2011 | 176 | 2011 |
Machine-learning-based reduced-order modeling for unsteady flows around bluff bodies of various shapes K Hasegawa, K Fukami, T Murata, K Fukagata Theoretical and Computational Fluid Dynamics 34, 367-383, 2020 | 170 | 2020 |
Convolutional neural network based hierarchical autoencoder for nonlinear mode decomposition of fluid field data K Fukami, T Nakamura, K Fukagata Physics of Fluids 32 (9), 2020 | 167 | 2020 |
Synthetic turbulent inflow generator using machine learning K Fukami, Y Nabae, K Kawai, K Fukagata Physical Review Fluids 4 (6), 064603, 2019 | 153 | 2019 |
Numerical simulation of gas–liquid two-phase flow and convective heat transfer in a micro tube K Fukagata, N Kasagi, P Ua-arayaporn, T Himeno International Journal of Heat and Fluid Flow 28 (1), 72-82, 2007 | 150 | 2007 |
Convolutional neural network and long short-term memory based reduced order surrogate for minimal turbulent channel flow T Nakamura, K Fukami, K Hasegawa, Y Nabae, K Fukagata Physics of Fluids 33 (2), 2021 | 145 | 2021 |
Effect of uniform blowing/suction in a turbulent boundary layer at moderate Reynolds number Y Kametani, K Fukagata, R Örlü, P Schlatter International Journal of Heat and Fluid Flow 55, 132-142, 2015 | 138 | 2015 |
Numerical simulation of flow around a circular cylinder having porous surface H Naito, K Fukagata Physics of Fluids 24 (11), 2012 | 127 | 2012 |
Global field reconstruction from sparse sensors with Voronoi tessellation-assisted deep learning K Fukami, R Maulik, N Ramachandra, K Fukagata, K Taira Nature Machine Intelligence 3 (11), 945-951, 2021 | 113 | 2021 |
Relaminarization of turbulent channel flow using traveling wave-like wall deformation R Nakanishi, H Mamori, K Fukagata International Journal of Heat and Fluid Flow 35, 152-159, 2012 | 113 | 2012 |
Friction drag reduction achievable by near-wall turbulence manipulation at high Reynolds numbers K Iwamoto, K Fukagata, N Kasagi, Y Suzuki Physics of Fluids 17 (1), 011702-011702-4, 2005 | 113 | 2005 |
CNN-LSTM based reduced order modeling of two-dimensional unsteady flows around a circular cylinder at different Reynolds numbers K Hasegawa, K Fukami, T Murata, K Fukagata Fluid Dynamics Research 52 (6), 065501, 2020 | 108 | 2020 |