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J-GLOBAL ID：201601021359777079   Update date: Apr. 16, 2025

Okabayashi Kie

オカバヤシ キエ | Okabayashi Kie
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Affiliation and department：
Osaka University Graduate School of Engineering

About Osaka University Graduate School of Engineering


Research field  (4)： Aerospace engineering ,  Fluid engineering ,  Fluid engineering ,  Fluid engineering
Research keywords  (7)： Data science ,  Machine Learning ,  Flow Control ,  Multiphase Flow ,  Computational Fluid Dynamics ,  Cavitation ,  Turbulent Flow
Research theme for competitive and other funds  (14)：
  • 2022 - 2025 Development of data-driven cavitation model
  • 2023 - 2024 Development of data-driven cavitation turbulence model and the construction of the training dataset using data assimilation
  • 2022 - 2023 Development of data-driven cavitation model using CFD database of cavitating turbulent flow
  • 2019 - 2022 Unified Method for Unsteady Analysis of Cavitating Turbulent Flow
  • 2020 - 2021 Numerical study for the actual use of Miura-fold-type zigzag riblet
Show all
Papers (40)：
  • Shungo Okamura, Kie Okabayashi. Twin experiments for data assimilation of cavitating flow around a hydrofoil. International Journal of Multiphase Flow. 2025. 188. 105201-105201
  • Bahrul Jalaali, Kie Okabayashi. Multiscale Convolutional Neural Networks for Subgrid-scale Modeling in Large-Eddy Simulation. Physics of Fluids. 2025
  • Shota Akita, Kie Okabayashi, Shintaro Takeuchi. Envelope boundary conditions for the upper surface of two-dimensional canopy interacting with fluid flow. Microfluidics and Nanofluidics. 2024. 29. 7. 1-23
  • Taku Sakamoto, Kie Okabayashi. Optimization of fluid control laws through deep reinforcement learning using dynamic mode decomposition as the environment. AIP Advances. 2024. 14. 11. 115204
  • Kosei HINO, Kie OKABAYASHI. Estimation of 2D pressure and cavitation fields from sparse pseudo-pressure sensor point data using super-resolution machine learning. Transactions of the Japanese Society of Mechanical Engineers (in Japanese). 2024. 90. 937. 24-00115
ｍore...
MISC (54)：
  • Kie Okabayashi. Improvement of configuration and flow control of fluid machinery using deep reinforcement learning. Science of Machine. 2025. 77. 4. 243-252
  • Kie Okabayashi. Twin Experiments for Data Assimilation of Cavitating Flow around a Clark-Y11.7% Hydrofoil. 4th Asian Workshop on Hydraulic Machinery. 2025. 34-35
  • Kie Okabayashi. Development of Data-Driven Cavitation Model and Its Training Dataset. Turbomachinery. 2025. 53. 1. 19-25
  • Kie Okabayashi. Data-driven cavitation model and their training datasets. 2024. 14. 53-56
  • Shungo Okamura, Kie Okabayashi. Twin Experiment to Construct a Data Assimilation System for Cavitating Flow. Proc. of 21st Symposium on Cavitation. 2023. S2-4
ｍore...
Patents (3)：
  • Riblet structure and object
  • Drag Reduction Device
  • リブレット構造及び物体
Lectures and oral presentations  (39)：
  • Optimization of fluid control laws through deep reinforcement learning using dynamic mode decomposition as the environment
    (3rd Workshop on Data-Driven Fluid Dynamics 2025)
  • Estimation of 2D pressure field of a cavitating flow from pseudo-sensor point data using super-resolution machine learning
    (38th CFD Symposium, No. OS4-2-4-02 2024)
  • Extension of two-dimensional cavitation flow around a wing to three-dimensional flow using super-resolution machine learning with the mass conservation as a constraint
    (38th CFD Symposium, No. OS4-2-1-01 2024)
  • The multiscale-based data-driven subgrid-scale model with physics constraints for enhanced prediction of unresolved scales in turbulent flow
    (77th APS Annual Meeting of the Division of Fluid Dynamics 2024)
  • Boundary conditions for the envelope of canopy interacting with two dimensional laminar flow
    (77th APS Annual Meeting of the Division of Fluid Dynamics 2024)
ｍore...
Education (3)：
  • 2008 - 2011 Osaka University Graduate School of Engineering Department of Mechanical Engineering
  • 2007 - 2008 Osaka University Graduate School of Engineering Department of Mechanical Engineering
  • 2003 - 2007 Osaka University School of Engineering
Professional career (1)：
  • 博士(工学) (大阪大学)
Work history (3)：
  • 2016/10 - 現在 Osaka University Graduate School Dept. Mechanical Eng. Assistant Professor
  • 2011/04 - 2016/09 Japan Aerospace Exploration Agency Researcher
  • 2009/04 - 2011/03 Japan Society of the Promotion of Science Research Fellowship for Young Scientists (DC2)
Committee career (6)：
  • 2023/08 - 現在 Symposium on Cavitation Executive Committee Member
  • 2022/05 - 現在 Turbomachinery Society of Japan Representative
  • 2017/05 - 現在 The Japan Society of Mechanical Engineers Subcommittee on exploring various functions of shear flow and its application science and technology
  • 2020/04 - 2024/03 Turbomachinery Society of Japan Subcomittee on accurate prediction of performance and innovative design of turbomachinery
  • 2019/08 - 2022/08 The Japanese Society for Multiphase Flow Editorial Committee
Show all
Awards (6)：
  • 2023/10 - Asian Fluid Machinery Committee (AFMC) Young Engineer Award
  • 2023/09 - Turbomachinery Society of Japan Challenge Award
  • 2022/08 - The Japanese Society of Multiphase Flow Best Presentation Award Preliminary Study on Learning Mode of Data-driven Cavitation Model
  • 2019/09 - Korean Society for Fluid Machinery 15th Asian International Conference on Fluid Machinery, Best Paper Award Large-eddy Simulation of Cavitating Turbulent Flow around a Clark-Y11.7% Hydrofoil
  • 2007/07 - 日本混相流学会 学生優秀講演賞
Show all
Association Membership(s) (5)：
Turbomachinery Society of Japan ,  THE JAPAN SOCIETY FOR AERONAUTICAL AND SPACE SCIENCES ,  THE JAPAN SOCIETY OF MECHANICAL ENGINEERS ,  THE JAPANESE SOCIETY FOR MULTIPHASE FLOW ,  The Japan Society of Fluid Mechanics
※ Researcher’s information displayed in J-GLOBAL is based on the information registered in researchmap. For details, see here.

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