On 19^th Mar 2024, Kai-en, YANG, Sakai-lab, Department of Nuclear Engineering and Management, (D2）, received “Outstanding Student Award” at SCEJ 89^th Annual Meeting.

〈Name of award and short explanation about the award〉

Outstanding Student Award

This is the outstanding poster presentation award in the SCEJ 89^th Annual Meeting.

〈About awarded research〉

Title: [Featured Poster Presentation] A Multi-timescale Data-driven Reduced Order Model for Fast Predictive Eulerian- Lagrangian Simulations

In this study, a multi-timescale reduced order model (MT-ROM) is proposed for fast predictive Eulerian-Lagrangian simulations. This model uses a posteriori error estimate to avail the advantages of the data-driven and can decide the optimal training data for data-driven ROM without the physical and temporal constraints. As a result, a series of bead mills are simulated using fast predictive MT-ROM to demonstrate its effectiveness, the acceleration is up to 5600 times comparing to the conventional DEM-CFD method with relative errors maintained under 5%. It has tremendous potential to realize the digital twin of powder process, therefore fosters the digital transformation in chemical engineering.

〈Your impression & future plan〉

It is truly my pleasure to be awarded the Outstanding Student Award in SCEJ 89^th annual meeting, where many outstanding chemical engineering studies were reported. I’d like to express my highest appreciation to all the Sakai Lab members, especially Prof. Mikio Sakai, Dr. Guangtao Duan, and Dr. Shuo Li. Their continued support and advice helped me to achieve this outstanding outcome.

Focusing on the digitalization of powder process, I will commit myself to the research aiming at realizing the digital twin of multi-phase powder process through data-driven reduced order model.