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J-GLOBAL ID:201401076852309231   Update date: Apr. 26, 2024

Sakai Mikio

サカイ ミキオ | Sakai Mikio
Affiliation and department:
Job title: Full Professor
Homepage URL  (1): https://dem.t.u-tokyo.ac.jp/index.html
Research field  (5): Fluid engineering ,  Chemical reaction and process system engineering ,  Computational science ,  Transfer phenomena and unit operations ,  Nuclear engineering
Research keywords  (22): Simulation-based digital twin ,  Cyber Physical System ,  digital twin ,  Continuous Manufacturing of Pharmaceuticals ,  Phase change ,  Multi-physics ,  Discrete Element Method ,  Computational Mechanics ,  粉体 ,  Powder Technology ,  Particle Technology ,  Computational Granular Dynamics ,  製剤 ,  液架橋力 ,  自由表面流 ,  表面張力 ,  個別要素法 ,  数値流体力学 ,  粉体工学 ,  混相流 ,  Dscrete Element Method ,  粒子法
Research theme for competitive and other funds  (6):
  • 2021 - 2025 Development of innovative simulation technology for the realization of continuous production of pharmaceuticals
  • 2021 - 2024 New frontiers in discontinuum mechanics model: coarse-grained DEM for powder systems
  • 2018 - 2020 高精度金型設計のための粉体成形シミュレーション技術の実証
  • 2017 - 2019 高精度金型設計のための革新的粉体成形シミュレータの開発
  • 2013 - 2014 コンピュータシミュレーションを用いた微粒子を含む高粘性流体のレオロジー特性評価
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Papers (71):
  • Yuki Tsunazawa, Nobukazu Soma, Motoyuki Iijima, Junich Tatami, Takamasa Mori, Mikio Sakai. Validation study on a coarse-grained DEM-CFD simulation in a bead mill. Powder Technology. 2024. 440. 119743
  • Shuo LI, GuangTao Duan, Mikio Sakai. On reduced-order modeling of gas-solid flows using deep learning. Physics of Fluids. 2024. 36. 033340
  • Guangtao Duan, Shuo Li, Mikio Sakai. Feasibility Analysis of a POD-Based Reduced Order Model with Application in Eulerian-Lagrangian Simulations. Ind. Eng. Chem. Res. 2024. 63. 1. 780-796
  • Kai-en Yang, Shuo Li, Guangtao Duan, Mikio Sakai. On Fostering Predictions in Data-Driven Reduced Order Model for Eulerian-Lagrangian Simulations: Decision of Sufficient Training Data. Journal of Chemical Engineering of Japan. 2024. 57. 1. 2316155
  • Shintaro Kajiwara, Mikio Sakai. Numerical investigation on a bimodal mixing system of solid-liquid mixture in an industrial mixing cooker. Advanced Powder Technology. 2024. 35. 1. 104300
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MISC (2):
  • Existing Numerical Simulation Technologies for Powder Processes and their Evolution. 2014. 78. 3. 186-189
  • 越塚誠一, 酒井幹夫, 柴田和也. 最新の粒子法シミュレーションに関する研究紹介. 応用数理. 2010. 20. 3. 257-259
Books (2):
  • 混相流の数値シミュレーション
    丸善出版 2015
  • Numerical simulation of granular flows
    2012
Lectures and oral presentations  (72):
  • Digital transformation in manufacturing using computational granular dynamics
    (POWTEX 2023 2023)
  • On digital twin for a powder process
    (International Powder and Nanotechnology Forum, Tokyo 2023)
  • What technologies are essential in development of the DEM-based digital twin?
    (9th International Conference on Discrete Element Methods (DEM9) 2023)
  • On a simulation-based digital twin towards the realization of smart manufacturing in the powder industry
    (The Asian Pacific Confederation of Chemical Engineering (APCChE) 2023 2023)
  • Development of innovative numerical models for the construction of a digital twin of powder processes and their industrial applications
    (36th Fall Symposium, The Ceramic Society of Japan 2023)
more...
Work history (4):
  • 2023 - 現在 Imperial College London Visiting Professor
  • 2023 - 現在 The University of Tokyo The Graduate School of Engineering
  • 2019 - 現在 University of Surrey Visiting Professor
  • 2016 - 2023 Imperial College London Visiting Reader
Committee career (10):
  • 2024/04 - 現在 Computational Science and Engineering Division, Atomic Energy Society of Japan Chairman
  • 2023 - 現在 Chemical Engineering Science Editor
  • 2021/04 - 現在 Association of Powder Process Industry and Engineering Chairperson for AI Technical Committee
  • 2018 - 現在 Granular Matter Editor
  • 2017 - 現在 日本粉体工業技術協会 粉体シミュレーション技術利用分科会 コーディネータ
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Awards (10):
  • 2023 - Japan Association for Computational Mechanics The JACM Computational Mechanics Award
  • 2023 - The Society of Chemical Engineers, Japan. The SCEJ Award for Outstanding Research Achievement
  • 2022 - Computational Science and Engineering Division, AESJ Outstanding Achievement Award
  • 2019 - SPTJ Technical Award
  • 2019 - The Information Center of Particle Technology IP Award
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Association Membership(s) (8):
日本粉体工業技術協会 ,  AIChE ,  THE JAPAN SOCIETY FOR COMPUTATIONAL ENGINEERING AND SCIENCE ,  ATOMIC ENERGY SOCIETY OF JAPAN ,  THE JAPAN SOCIETY FOR INDUSTRIAL AND APPLIED MATHEMATICS ,  THE JAPANESE SOCIETY FOR MULTIPHASE FLOW ,  THE SOCIETY OF POWDER TECHNOLOGY, JAPAN ,  THE SOCIETY OF CHEMICAL ENGINEERS, JAPAN
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