Prof. TAKAO TSUMURAYAJapan
Kumamoto University
| 2023/01 to present | | Associate Professor |
| 2004 - 2009 | | Ph.D. in Science, Hiroshima Univeristy, |
| 2009 - 2011 | | Postdoctoral Fellow, Northwestern University, IL, USA |
| 2011 - 2015 | | Postdoctoral Fellow, RIKEN, Japan |
| 2015/04 - 2017/12 | | ICYS Researcher, National Institure for Materials Science, Tsukuba, Japan |
| 2018/01 - 2022/12 | | Assistant Professor, Kumamoto University, Japan |
Computational Materials Science, Condensed Matter Physics, Density Functional Theory, First-Principles Calculations, Cluster Expansion, Machine Learning Interatomic Potentials, Mg based alloys, Ti based alloys
Takao Tsumuraya is an Associate Professor at Kumamoto University, Japan. His research focuses on computational materials science using density functional theory, cluster expansion, and thermodynamic modeling to study phase stability, diffusion, and structural transformations in alloys. He received his Ph.D. from Hiroshima University in 2009 and worked at Northwestern University, RIKEN, and NIMS before joining Kumamoto University in 2018. He was promoted to Associate Professor in 2023.
Multiscale Simulations of Melt Spinning in Mg–Zn–Y Alloys Using Machine-Learning Potentials
TBA TBA
Multiscale Simulation on Functional Materials and Their Applications/TBA
Weight reduction is one of the most important strategies for improving fuel efficiency and reducing CO2 emissions in aircraft and automobiles. Mg-based alloys are promising lightweight metals, and Mg–Zn–Y alloys containing long-period stacking order (LPSO) structures exhibit high strength and ductility. Rapid solidification (RS) using a single-roller melt-spinning process is an effective method to control microstructure and mechanical properties in these alloys. However, dominant parameters governing microstructure formation during rapid solidification have not yet been clarified.
In this study, we perform multiscale simulations of melt spinnig in Mg-Zn–Y alloys by combining molecular dynamics (MD) and computational fluid dynamics (CFD). Machine-learning interatomic potantials were developed based on first-principles MD simulations, and the temperature-dependent viscosity was evaluated using the Green-Kubo formula. The calculated viscosity was then used as an input parameter for CFD simulations of melt spinning process. The ribbon thickness for different roll speeds was successfully reproduced in good greement with experimental results. Furthermore, we clarified how variations in flow velocity in RS ribbons influence microstructure formation.