About this Abstract |
Meeting |
2022 TMS Annual Meeting & Exhibition
|
Symposium
|
Computational Thermodynamics and Kinetics
|
Presentation Title |
Development of a Twin Experiment-validated Data Assimilation System for Dendrite Growth with Melt Convection Using Phase-field Lattice Boltzmann Method |
Author(s) |
Ayano Yamamura, Shinji Sakane, Munekazu Ohno, Tomohiro Takaki |
On-Site Speaker (Planned) |
Ayano Yamamura |
Abstract Scope |
Predicting the formation process of equiaxial structures accurately is critical for macrosegregation control in alloy casting. However, the equiaxial structure formation is poorly understood because it involves the motion of equiaxial crystals in the liquid phase. Recently, high-performance computing using phase-field lattice Boltzmann model and time-resolved X-ray tomography have been used to elucidate the equiaxial structure formation process. However, even with these state-of-the-art technologies, major issues such as the lack of material properties for simulations and spatiotemporal resolution in experiments persist. The purpose of this study is to combine experimental observation and simulation through data assimilation to develop a highly accurate prediction method for the formation process of an equiaxial structure. As a first step, a data assimilation system for dendrite growth with melt convection of a binary alloy is developed and validated using twin experiments. |
Proceedings Inclusion? |
Planned: |