JSPS KAKENHI — Early-Career Scientists (Grant No. 25K21215)

Development of a multiscale reduced-order modeling framework and its applications to large-scale gas–solid flow systems

Role: Principal Investigator (PI)

An advanced reduced-order model for real-time and high-accuracy computation of industrial gas-solid flow systems

K. Yang, S. Li, M. Sakai · Physics of Fluids, Vol. 37, Issue 12, 2025

Development and validation of a multi-timescale reduced-order model for high-speed simulations of solid-fluid systems

K. Yang, S. Li, M. Sakai · Physics of Fluids, Vol. 37, Issue 8, 2025

Advanced graph neural network-based surrogate model for granular flows in arbitrarily shaped domains

S. Li, M. Sakai · Chemical Engineering Journal, Vol. 500, 2024

On fostering predictions in data-driven reduced order model for Eulerian-Lagrangian simulations

K. Yang, S. Li, G. Duan, M. Sakai · Journal of Chemical Engineering of Japan, Vol. 57, Issue 1, 2024

Feasibility Analysis of a POD-Based Reduced Order Model with Application in Eulerian-Lagrangian Simulations

G. Duan, S. Li, M. Sakai · Industrial & Engineering Chemistry Research, Vol. 63, 2024

On reduced-order modeling of gas-solid flows using deep learning

S. Li, G. Duan, M. Sakai · Physics of Fluids, Vol. 36, Issue 3, 2024

On POD-based modal analysis in simulations of granular flows

S. Li, G. Duan, M. Sakai · Powder Technology, Vol. 413, 2023

Development of a reduced-order model for large-scale Eulerian-Lagrangian simulations

S. Li, G. Duan, M. Sakai · Advanced Powder Technology, Vol. 33, Issue 8, 2022

POD-based identification approach for powder mixing mechanism in Eulerian-Lagrangian simulations

S. Li, G. Duan, M. Sakai · Advanced Powder Technology, Vol. 33, Issue 1, 2022

Numerical investigation on the mixing mechanism in a cross-torus paddle mixer using the DEM-CFD method

S. Li, S. Kajiwara, M. Sakai · Powder Technology, Vol. 377, 2021

Towards digital twins for powder processes with AI-accelerated surrogate models

S. Li, M. Sakai · ICCCI-2025, Fuji-Yoshida, Japan

An AI-accelerated multiscale surrogate modelling framework for CFD-DEM simulations

S. Li, M. Sakai · DEM-10, Himeji, Japan

A data-driven multiscale surrogate model for CFD-DEM simulations

S. Li, M. Sakai · WCCM-2024, Vancouver, Canada

A graph neural network based surrogate model for granular flows with complex boundary geometries

S. Li, G. Duan, M. Sakai · DEM-9, Erlangen, Germany