About this Abstract |
| Meeting |
2026 TMS Annual Meeting & Exhibition
|
| Symposium
|
Frontiers in Solidification X
|
| Presentation Title |
Data Assimilation Integrating Phase-field Simulations and X-ray Imaging for Accurate Prediction of Dendrite Growth Morphology |
| Author(s) |
Ayano Yamamura, Shinji Sakane, Hideyuki Yasuda, Tomohiro Takaki |
| On-Site Speaker (Planned) |
Ayano Yamamura |
| Abstract Scope |
Accurate prediction of dendrite growth is essential for improving the quality of cast products. Phase-field (PF) simulation can complement experimental observations by providing high-resolution insights into solidification phenomena. However, the lack of accurate input material properties remains a major challenge. In this study, we aim to estimate these material properties from time-varying solidification morphologies obtained through X-ray imaging, using sequential data assimilation based on the PF method−referred to as PF data assimilation. This approach also shows promise for accurately reconstructing the evolving 3D dendrite structure. To address the high computational cost of the PF data assimilation, we introduced the local ensemble transform Kalman filter and implemented adaptive mesh refinement accelerated by parallel GPU computing. The fundamental performance of the developed approach was evaluated through twin experiments, and the PF data assimilation was further applied to actual X-ray transmission images captured during dendrite growth in an Al-Cu alloy. |
| Proceedings Inclusion? |
Planned: |