Koji Fukagata

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Citations	8015	5610
h-index	40	33
i10-index	98	73

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Nobuhide KasagiUniversity of TokyoVerified email at thtlab.t.u-tokyo.ac.jp
Kai FukamiUniversity of California, Los AngelesVerified email at g.ucla.edu
Kunihiko TairaUniversity of California, Los AngelesVerified email at seas.ucla.edu
Yukinori KametaniDept. Mechanical Engineering Informatics, Meiji UniversityVerified email at iis.u-tokyo.ac.jp
Kaoru IwamotoTokyo University of Agriculture and TechnologyVerified email at cc.tuat.ac.jp
Masaki MorimotoKeio UniversityVerified email at kflab.jp
Philipp SchlatterProfessor in Fluid Mechanics, FAU Erlangen-Nürnberg and KTH StockholmVerified email at fau.de
Shinnosuke ObiKeio UniversityVerified email at mech.keio.ac.jp
Ramis ÖrlüProfessor at OsloMet and Researcher at KTH StockholmVerified email at oslomet.no
Yuji SuzukiThe University of Tokyo, Department of Mechanical Engineering, ProfessorVerified email at mesl.t.u-tokyo.ac.jp
Yoshitsugu NakaMeiji University, JapanVerified email at meiji.ac.jp
Petros KoumoutsakosHarvard UniversityVerified email at seas.harvard.edu
Nesar RamachandraComputational Scientist, Argonne National LaboratoryVerified email at anl.gov
Mitul LuharAssociate Professor, Aerospace and Mechanical Engineering, University of Southern CaliforniaVerified email at usc.edu
Philippe ChatelainInstitute of mechanics, materials and civil engineering, Université catholique de LouvainVerified email at uclouvain.be
Stefan KernStadtwerke MünchenVerified email at swm.de
P Henrik AlfredssonProfessor emeritus of Fluid Physics, FLOW, Dept. Eng. Mech., KTH Royal Institute of TechnologyVerified email at mech.kth.se
Chainarong ChaktranondDepartment of Mechanical Engineering, Faculty of Engineering, Thammasat University, Rangsit CampusVerified email at engr.tu.ac.th
Antonio SegaliniAssociate professor/DocentVerified email at geo.uu.se
Tomoaki OKUDAKeio UniversityVerified email at applc.keio.ac.jp

Koji Fukagata

Keio University - Department of Mechanical Engineering

Verified email at mech.keio.ac.jp - Homepage

Fluid Mechanics Fluid Dynamics Flow Control Turbulence


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
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	808	2002
Super-resolution reconstruction of turbulent flows with machine learning K Fukami, K Fukagata, K Taira Journal of Fluid Mechanics 870, 106-120, 2019	698	2019
Nonlinear mode decomposition with convolutional neural networks for fluid dynamics T Murata, K Fukami, K Fukagata Journal of Fluid Mechanics 882, A13, 2020	329	2020
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	325	2006
Machine-learning-based spatio-temporal super resolution reconstruction of turbulent flows K Fukami, K Fukagata, K Taira Journal of Fluid Mechanics 909, A9, 2021	274	2021
Highly energy-conservative finite difference method for the cylindrical coordinate system K Fukagata, N Kasagi Journal of Computational Physics 181 (2), 478-498, 2002	234	2002
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	224	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	213	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	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	189	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	184	2020
Synthetic turbulent inflow generator using machine learning K Fukami, Y Nabae, K Kawai, K Fukagata Physical Review Fluids 4 (6), 064603, 2019	167	2019
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	164	2021
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	153	2007
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	148	2015
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	144	2021
Numerical simulation of flow around a circular cylinder having porous surface H Naito, K Fukagata Physics of Fluids 24 (11), 2012	137	2012
Probabilistic neural networks for fluid flow surrogate modeling and data recovery R Maulik, K Fukami, N Ramachandra, K Fukagata, K Taira Physical Review Fluids 5 (10), 104401, 2020	124	2020
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	123	2020
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	114	2012

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