190615.pngKoji Fukagata
Professor, Department of Mechanical Engineering /
Chair, Center for Applied and Computational Mechanics
Keio University

Official profile by Keio Univ. is here.

Japanese version is here.

Major Research Fields

  • Fluid Mechanics / Flow Control / Turbulence

Degrees

  • TeknD, Kungl. Tekniska Högskolan (KTH), Sweden.
  • Ph.D. (Engineering), The University of Tokyo, Japan.

Biography

  • May 1970: Born in Tokyo
  • Mar. 1994: B.Eng., Dept. of Quantum Engineering and Systems Science, The Univ. of Tokyo
  • Jun. 1997: TeknL, Dept. of Mechanics / FaxénLaboratoriet, Kungl. Tekniska Högskolan (KTH), Stockholm
  • Apr. 2000: TeknD, Dept. of Mechanics / FaxénLaboratoriet, KTH, Stockholm
  • Sep. 2000: Ph.D., Dept. of Quantum Engineering and Systems Science, The Univ. of Tokyo
  • Oct. 2000: Postdoctoral fellow, Mechanial Engineering Laboratory, AIST
  • Apr. 2001: Postdoctoral fellow, Inst. for Energy Utilization, AIST
  • Apr. 2003: Research Associate, Dept. of Mechanical Engineering, The Univ. of Tokyo
  • Apr. 2007: Assistant Professor, Dept. of Mechanical Engineering, Keio University
  • Apr. 2011: Associate Professor, Dept. of Mechanical Engineering, Keio University
  • Apr. 2015: Professor, Dept. of Mechanical Engineering, Keio University
  • Apr. 2019: Chair, Center for Applied and Computational Mechanics, Keio University

Recent Papers (Publications list is here, Google Scholar Profile is here)

  1. M. Badri Ghomizad, H. Kor, and K. Fukagata,
    "A sharp interface direct-forcing immersed boundary method using the moving least square approximation,"
    J. Fluid Sci. Technol. 16, JFST0013 (2021).

  2. M. Ohashi, K. Fukagata, and N. Tokugawa,
    "Adjoint-based sensitivity analysis for airfoil flow control aiming at lift-to-drag ratio improvement,"
    AIAA J. (2021). https://doi.org/10.2514/1.J060415

  3. T. Nakamura, K. Fukami, K. Hasegawa, Y. Nabae, and K. Fukagata,
    "Convolutional neural network and long short-term memory based reduced order surrogate for minimal turbulent channel flow,"
    Phys. Fluids 33, 025116 (2021).

  4. K. Fukami, K. Fukagata, and K. Taira,
    "Machine-learning-based spatio-temporal super resolution reconstruction of turbulent flows,"
    J. Fluid Mech. 909, A9 (2021).

  5. K. Hasegawa, K. Fukami, T. Murata, and K. Fukagata,
    "CNN-LSTM based reduced order modeling of two-dimensional unsteady flows around a circular cylinder at different Reynolds numbers,"
    Fluid Dyn. Res. 52, 065501 (2020).

  6. R. Maulik, K. Fukami, N. Ramachandra, K. Fukagata, and K. Taira,
    "Probabilistic neural networks for fluid flow surrogate modeling and data recovery,"
    Phys. Rev. Fluids 5, 104401 (2020).

  7. K. Fukami, T. Nakamura, and K. Fukagata,
    "Convolutional neural network based hierarchical autoencoder for nonlinear mode decomposition of fluid field data,"
    Phys. Fluids 32, 095110 (2020).
  8. S. Hirokawa, M. Ohashi, K. Eto, K. Fukagata, and N. Tokugawa,
    "Turbulent friction drag reduction on a Clark-Y airfoil by passive uniform blowing,"
    AIAA J. 58, 4178-4180 (2020).

  9. 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).

  10. 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. 34, 367-383 (2020).

  11. K. Fukami, K. Fukagata, and K. Taira,
    "Assessment of supervised machine learning methods for fluid flows,"
    Theor. Comput. Fluid Dyn. 34, 497519 (2020).

  12. 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).

  13. 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).

  14. 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).

  15. T. Murata, K. Fukami, and K. Fukagata,
    "Nonlinear mode decomposition with convolutional neural networks for fluid dynamics,"
    J. Fluid Mech. 882, A13 (2020).

Honors and Awards

  • October 2020: JSME-TED Best Conference Paper Award (PRTEC2019) (Co-awardees: Kai Fukami, Yusuke Nabae, Ken Kawai)
  • December 2019: JACM Fellows Award, Japan Association of Computational Mechanics
  • December 2019: Best Oral Presentaion Award, COMSOL Conference 2019 Tokyo
  • October 2018 and October 2020: Thermal Engineering Contribution Certificate of Merit, Japan Society for Mechanical Engineers
  • June 2014: Best Poster Paper Award, Int. Symp. on Electrohydrodynamics 2014 (Co-awardees: Yosuke Anzai, Hiroshi Naito)
  • November 2013: Fluids Engineering Frontier Certificate of Merit, Japan Sociery of Mechanical Engineers Fluids Engineering Division
  • February 2013: Award for Outstanding Paper in Fluid Mechanics, Japan Society of Fluid Mechanics Co-awardees: Kaoru Iwamoto, Nobuhide Kasagi)
  • February 2008: Award for Distinguished Young Researcher in Fluid Mechanics (Ryumon Award), Japan Society of Fluid Mechanics
  • October 2006: Fluids Engineering Contribution Certificate of Merit, Japan Society for Mechanical Engineers
  • March 2003: The Best Presentation Award, The 4th International Symposium on Smart Control of Turbulence

Academic Society

  • Japan Society of Mechanical Engineers (JSME), Member
  • Japan Society of Fluid Mechanics (JSFM), Member, Director (2019-2020), Fellow (2021-)
  • Japanese Society for Multiphase Flow (JSMF), Member
  • Japanese Association for Computational Mechanics (JACM), Member
  • American Physical Society (APS), Member
  • European Mechanics Society (EUROMECH), Member
  • European Research Community for Flow Turbulence and Combustion (ERCOFTAC), Research Group Member

Editorship

Organization of International Conferences

Advisory Board/International Scientific Committee Member

Contact Information

  • Address: Department of Mechanical Engineering, Keio University, Hiyoshi 3-14-1, Kohoku-ku, Yokohama 223-8522, Japan
  • E-mail: fukagata [huh]mech.keio.ac.jp

Last-modified: 2021-04-30 (Fri) 02:10:59 (8d)
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