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 (7)：

2024 - 2027 Development of fundamental technologies towards realization of digital twin in large-scale gas-solid-liquid three-phase flow systems
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
2017 - 2020 Development of an innovative powder molding simulator for high-precision die design
2018 - 2020 Demonstration of powder compaction simulation technology for high-precision mold design

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Papers (72)：

Shuo Li, Mikio Sakai. Advanced graph neural network-based surrogate model for granular flows in arbitrarily shaped domains. Chemical Engineering Journal. 2024. 500. 157349
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

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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 (77)：

Advancements in the discrete element method: paving the way for the future of manufacturing
(4th International Workshops on Advances in Computational Mechanics 2024)
Recent Breakthroughs in the Discrete Element Method for Industrial Applications
(Advances in Particle Technology Workshop 2024 2024)
Development of core technologies for a simulation-based digital twin for continuous manufacturing
(Japan Society of Pharmaceutical Machinery and Engineering 2024)
Development and industrial application of the advanced discrete element method
(16th World Congress on Computational Mechanics 2024)
Advancing Discrete Element Method Simulation: A Comprehensive Verification and Validation Study
(International Powder and Nanotechnology Forum 2024 2024)

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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 (12)：

2024/04 - 現在 Computational Science and Engineering Division, Atomic Energy Society of Japan Chairman
2024 - 現在 Powder Technology Editorial Board Member
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

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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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