|About this Abstract
||2019 TMS Annual Meeting & Exhibition
||Computational Approaches for Big Data, Artificial Intelligence and Uncertainty Quantification in Computational Materials Science
||M-6: Material Parameter Estimation for Phase-field Model of Binary Alloy Solidification Using EnKF-based Data Assimilation
||Kazuki Takahashi, Akinori Yamanaka, Kengo Sasaki
|On-Site Speaker (Planned)
Phase-field model have been used to simulate dendrite formation during solidification of alloys. However, unknown and immeasurable parameters influence the simulation results. On the other hand, advanced experimental techniques, e.g. X-ray tomography, enables us to directly observe spatiotemporal evolution of dendrites. In order to utilize the direct observation data to estimate the parameters used for the phase-field models, we have developed data assimilation (DA) methodology based on the ensemble Kalman filter (EnKF). In this study, we apply the EnKF-based DA to two-dimensional phase-field simulation of dendrite growth in Al-Cu alloy and estimate the unknown parameter; the diffusion coefficient of Cu atom in liquid phase. The results of numerical experiments show that the EnKF-based DA method not only estimate the true value of the parameter, but also improve the accuracy of the dendrite morphology prediction.
||Planned: Supplemental Proceedings volume