Welcome to Keio Univ. Fukagata Laboratory!

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

Our research interests are numerical simulation and mathematical modeling of complex heat and fluid flow phenomena including turbulent flows and development of advanced control methods for such flow phenomena. The research area is being expanded toward establishment of design methodology for thermo-fluids systems by integrating control theories, optimization methods, machine learning, and large-scale flow simulation techniques.

News

2022-12-16	Conference	Ishize (M1) and Omichi (B4) made presentations at 36th CFD Symposium (online).
2022-12-10		Suzuki (B4) received the Outstanding Presentation Award at 28th Inter-University Workshop on Turbulence Control (Tokyo).
2022-12-05	Conference	Fukagata gave a special lecture at 12th Syposium on Compuational Mechanics, Science Council of Japan (Tokyo).
2022-10-14		Fujima (M2) received Japan Society of Fluid Mechanics Outstanding Young Presenter Award.
2022-09-29		Fujima (M2) and Sato (M2) made presentations at JSFM Annual Meeting 2022 (Kyoto).
2022-09-12	Conference	Ishize (M1) and Miura (M1) made presentations at Mechanical Engineering Congress, 2022 Japan (MECJ-22) (Toyama).
2022-08-01	Conference	Fujima (M2), Matsuo (M2), Sato (M2), Chida (M1), and Miura (M1) made presentations at WCCM-APCOM 2022, Yokohama (Online).
2022-07-23	Out now!	M. Morimoto, K. Fukami, R. Maulik, R. Vinuesa, and K. Fukagata, "Assessments of epistemic uncertainty using Gaussian stochastic weight averaging for fluid-flow regression," Physica D 440, 133454 (2022).
2022-07-20	Conference	Okochi (M2) and Nabae (Alumni) made presentations at TSFP12, Osaka (Online).
2022-06-25	Out now!	M. Morimoto, K. Fukami, R. Maulik, R. Vinuesa, and K. Fukagata, Featured Research in CFD35: "Model-form uncertainty quantification in neural-network-based fluid-flow estimation," Nagare - J. Jpn. Soc. Fluid Mech. 41, 89-92 (2022).
2022-06-22	Out now!	Y. Nabae and K. Fukagata, "Drag reduction effect of streamwise traveling wave-like wall deformation with spanwise displacement variation in turbulent channel flow," Flow Turbul. Combust. 109, 1175–1194 (2022).
2022-05-31	Out now!	T. Nakamura and K. Fukagata, "Robust training approach of neural networks for fluid flow state estimations," Int. J. Heat Fluid Flow 96, 108977 (2022).
2022-05-26	Conference	Fukagata gave an invited lecture at ParCFD 2022 (Alba, Italy (Hybrid)).

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