On 15^th May 2024, Kai-en, YANG, Sakai-lab, Department of Nuclear Engineering and Management, (D2）, received “Outstanding Presentation Award” at 2024 SPTJ Spring Meeting International Symposium on Powder Technology: “Realization of Simulation-Based Digital Twin for Powder Processes”.

〈Name of award and short explanation about the award〉

Outstanding Presentation Award

It was given to the outstanding young researcher oral presentation in the 2024 SPTJ Spring Meeting International Symposium.

〈About awarded research〉

Title: Decision of Sufficient Training Data for Improving Predictability of Data-driven Reduced Order Model for Eulerian-Lagrangian Simulations

In this study, sufficient training data for improving predictability of data-driven reduced order model (ROM) for Eulerian-Lagrangian simulations was studied. In specific, we proposed a technique for the decision of sufficient training data based on a posteriori error estimation. The results showed that, by applying this technique to a typical solid-fluid flows simulation of a bead mill, remarkable predictability was acquired using data-driven ROM without trial-and-error process. This technique has strong potential to contribute to various data-driven surrogate models for multi-phase simulations.

〈Your impression & future plan〉

It is truly my pleasure to be awarded the Outstanding Presentation Award in 2024 SPTJ Spring Meeting International Symposium, where many outstanding powder simulation technologies and studies were presented. I’d like to express my highest appreciation to the committee composed of world leading researchers, and all the Sakai Lab members, especially Prof. Sakai, Dr. Guangtao Duan, and Dr. Shuo Li. Their continued support and advice are essential to this outstanding achievement.

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.