Papers - Fukagata, Koji

Numerical modeling of spark path with stretching and short circuit in three-dimensional flow

R. Arai, Y. Nabae, R. Uekusa, H. Murakami, and K. Fukagata

SAE Technical Paper  ( 2021-01-1164 )  2021.09

Research paper (international conference proceedings), Joint Work, Last author, Accepted

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Sparse identification of nonlinear dynamics with low-dimensionalized flow representations

K. Fukami, T. Murata, K. Zhang, and K. Fukagata

J. Fluid Mech. 926   A10 2021.09

Research paper (scientific journal), Joint Work, Last author, Accepted

• Access to Document (DOI)

Experimental velocity data estimation for imperfect particle images using machine learning

M. Morimoto, K. Fukami, and K. Fukagata

Phys. Fluids 33 ( 8 ) 087121 2021.08

Research paper (scientific journal), Joint Work, Last author, Corresponding author, Accepted

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Convolutional neural networks for fluid flow analysis: toward effective metamodeling and low dimensionalization

M. Morimoto, K. Fukami, K. Zhang, A. G. Nair, and K. Fukagata

Theor. Comput. Fluid Dyn. 35   633 - 658 2021.08

Research paper (scientific journal), Joint Work, Last author, Accepted

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A structured adaptive mesh refinement strategy with a sharp interface direct-forcing immersed boundary method for moving boundary problems

M. Badri Ghomizad, H. Kor, and K. Fukagata

J. Fluid Sci. Technol. 16 ( 2 ) JFST0014 2021.06

Research paper (scientific journal), Joint Work, Last author, Corresponding author, Accepted

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Adjoint-based sensitivity analysis for airfoil flow control aiming at lift-to-drag ratio improvement

M. Ohashi, K. Fukagata, and N. Tokugawa

AIAA J. 59   4437 - 4448 2021.04

Research paper (scientific journal), Joint Work, Corresponding author, Accepted

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A sharp interface direct-forcing immersed boundary method using the moving least square approximation

M. Badri Ghomizad, H. Kor, and K. Fukagata

J. Fluid Sci. Technol. 16 ( 2 ) JFST0013 2021.04

Research paper (scientific journal), Joint Work, Last author, Corresponding author, Accepted

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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, and K. Fukagata

Phys. Fluids 33   025116 2021.02

Research paper (scientific journal), Joint Work, Accepted

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Machine-learning-based spatio-temporal super resolution reconstruction of turbulent flows

K. Fukami, K. Fukagata, and K. Taira

J. Fluid Mech. 909   A9 2021.02

Research paper (scientific journal), Joint Work, Accepted

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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, and K. Fukagata

Fluid Dyn. Res. 52   065501 2020.11

Research paper (scientific journal), Joint Work, Accepted

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Probabilistic neural networks for fluid flow surrogate modeling and data recovery

R. Maulik, K. Fukami, N. Ramachandra, K. Fukagata, and K. Taira

Phys. Rev. Fluids  5   104401 2020.10

Research paper (scientific journal), Joint Work, Accepted

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Convolutional neural network based hierarchical autoencoder for nonlinear mode decomposition of fluid field data

K. Fukami, T. Nakamura, and K. Fukagata

Phys. Fluids 32   095110 2020.09

Research paper (scientific journal), Joint Work, Accepted

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Turbulent friction drag reduction on Clark-Y airfoil by passive uniform blowing

S. Hirokawa, M. Ohashi, K. Eto, K. Fukagata, and N. Tokugawa

AIAA J. 58   4178 - 4180 2020.09

Research paper (scientific journal), Joint Work, Accepted

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Machine-learning-based reduced-order modeling for unsteady flows around bluff bodies of various shapes

K. Hasegawa, K. Fukami, T. Murata, and K. Fukagata

Theor. Comput. Fluid Dyn. 34   367 - 383 2020.05

Research paper (scientific journal), Joint Work, Accepted

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Resolvent analysis of turbulent channel flow with manipulated mean velocity profile

R. Uekusa, A. Kawagoe, Y. Nabae, and K. Fukagata

J. Fluid Sci. Technol. 15 ( 3 ) JFST0014 2020.04

Research paper (scientific journal), Joint Work, Accepted

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Parametric study toward optimization of blowing and suction locations for improving lift-to-drag ratio on a Clark-Y airfoil

M. Ohashi, Y. Morita, S. Hirokawa, K. Fukagata, and N. Tokugawa

J. Fluid Sci. Technol. 15 ( 2 ) JFST0008 2020.03

Research paper (scientific journal), Joint Work, Accepted

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Experimental investigation on friction drag reduction on an airfoil by passive blowing

S. Hirokawa, K. Eto, K. Fukagata, and N. Tokugawa

J. Fluid Sci. Technol. 15 ( 2 ) JFST0011 2020.03

Research paper (scientific journal), Joint Work, Accepted

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Assessment of supervised machine learning methods for fluid flows

K. Fukami, K. Fukagata, and K. Taira

Theor. Comput. Fluid Dyn. 34   497 - 519 2020.02

Research paper (scientific journal), Joint Work, Accepted

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Prediction of drag reduction effect by streamwise traveling wave-like wall deformation in turbulent channel flow at practically high Reynolds numbers

Y. Nabae, K. Kawai, and K. Fukagata

Int. J. Heat Fluid Flow 82   108550 2020.01

Research paper (scientific journal), Accepted

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Nonlinear mode decomposition with convolutional neural networks for fluid dynamics

T. Murata, K. Fukami, and K. Fukagata,

J. Fluid Mech. 882   A13 2020.01

Research paper (scientific journal), Joint Work, Accepted

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