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Welcome to Keio Univ. Fukagata Laboratory!

Fukagata laboratory, Department of Mechanical Engineering, Keio University, is a relatively new laboratory, born in 2007.

We perform theoretical, numerical, and experimental studies on flow control and optimization, such as turbulence control (e.g., turbulent friction drag reduction) and suppression of vortex shedding from a body.

Our research is conducted in collaboration with other laboratories.

News

2020-05-18Out now!S. Hirokawa, M. Ohashi, K. Eto, K. Fukagata, and N. Tokugawa, "Turbulent friction drag reduction on Clark-Y airfoil by passive uniform blowing," AIAA J. (2020). https://doi.org/10.2514/1.J059627
2020-05-15Out now!M. Morimoto, K. Fukami, K. Hasegawa, T. Murata, H. Murakami, and K. Fukagata, [SI] Focus in CFD33:"Improvement of PIV by data augmentation based on machine learning," Nagare 39, 84-87 (2020). (in Japanese)
2020-05-06Out now!K. Hasegawa, K. Fukami, T. Murata, and K. Fukagata, "Machine-learning-based reduced-order modeling for unsteady flows around bluff bodies of various shapes," Theor. Comput. Fluid Dyn. (2020). https://doi.org/10.1007/s00162-020-00528-w
2020-04-27Out now!R. Uekusa, A. Kawagoe, Y. Nabae, and K. Fukagata, "Resolvent analysis of turbulent channel flow with manipulated mean velocity profile," J. Fluid Sci. Technol. 15, JFST0014 (2020).
2020-03-31Out now!S. Hirokawa, K. Eto, K. Fukagata, and N. Tokugawa, "Experimental investigation on friction drag reduction on an airfoil by passive blowing,"J. Fluid Sci. Technol. 15, JFST0011 (2020).
2020-03-31Out now!M. Ohashi, Y. Morita, S. Hirokawa, K. Fukagata, and N. Tokugawa, "Parametric study toward optimization of blowing and suction locations for improving lift-to-drag ratio on a Clark-Y airfoil,"J. Fluid Sci. Technol. 15, JFST0008 (2020).
2020-03-23200323-morimoto.jpgMorimoto (B4) received the Certificate of Merit from the Chair of Department of Mechanical Engineering, Keio University.
2020-03-23200323-miura.jpgMiura (B4) received The Best Bachelor Thesis Presentation Award from Department of Mechanical Engineering, Keio University.
2020-02-27Out now!K. Fukami, K. Fukagata, and K. Taira, "Assessment of supervised machine learning methods for fluid flows," Theor. Comput. Fluid Dyn. (2020). https://doi.org/10.1007/s00162-020-00518-y
2020-02-20Out now!K. Fukami, K. Fukagata, and K. Taira, "Machine-learned three-dimensional super-resolution analysis of turbulent channel flow," JSME-FED Newsletter, Feb. 2020, Art. 4 (2020).
2020-01-21Out now!Y. Nabae, K. Kawai, and K. Fukagata, "Prediction of drag reduction effect by streamwise traveling wave-like wall deformation in turbulent channel flow at practically high Reynolds numbers," Int. J. Heat Fluid Flow 82, 108550 (2020).

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Last-modified: 2020-05-24 (Sun) 01:15:45 (13d)
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