Kai-en, YANG (D1) , Department of Nuclear Engineering and Management, received Encouragement Award at SCEJ Regional Meeting in Yamagata 2023

On 9th August 2023, Kai-en, YANG (D1), Sakai Labo, Department of Nuclear Engineering and Management, received Encouragement Award at SCEJ Regional Meeting in Yamagata 2023.

Encouragement Award

It was given to the good presentations in at SCEJ Regional Meeting in Yamagata 2023.

About awarded research

Title: A Novel Technique for Determination of Proper Sampling Interval for Data-driven ROM based Bead Mill Simulations

Data-driven Reduced Order Model (ROM) is a promising surrogate model for Eulerian-Lagrangian simulation in a bead mill. It holds significant importance in realizations of digital twin for digital transformations in chemical industries. However, due to the violence and complexity of fluid-solid flows, the ROM accuracy of particle phase can be easily deteriorated under a relatively large sampling interval. Conventionally, trial-and-error is used to determine a proper sampling interval, which hinders its industrial applications. To address this issue, in this study, a novel technique is proposed. By employing the proposed technique, ROM exhibits a sophisticated balance between efficiency and accuracy, and the capability of accurately predicting the violent fluid-solid flows without a trial-and-error. This technique is potential to solve the predicament of decision-making for sufficient training data in a temporal perspective.

Your impression & future plan

It is my pleasure to be awarded this award in SCEJ Regional Meeting in Yamagata 2023, where numerous chemical engineering research was presented. My highest appreciation belongs to all the Sakai Lab members, especially Prof. Sakai, Dr. Guangtao Duan, and Dr. Shuo Li. Focusing on the data-driven approach, I will continue to conduct research related to digital transformation of granular systems in various engineering field.