About this Abstract |
Meeting |
2022 TMS Annual Meeting & Exhibition
|
Symposium
|
Algorithm Development in Materials Science and Engineering
|
Presentation Title |
Parameter Estimation of Phase-field Model Based on Microstructure Data and Its Uncertainty Quantification by the Adjoint Method |
Author(s) |
Yuki Matsuura, Yuhki Tsukada, Toshiyuki Koyama |
On-Site Speaker (Planned) |
Yuki Matsuura |
Abstract Scope |
Data assimilation is a mathematical technique to estimate model states and parameters by integrating a simulation model with experimental data. We developed an adjoint model to integrate a phase-field model for spinodal decomposition with time-series measurement data of compositional field maps to estimate six material parameters (Gibbs energy parameters etc.) in the phase-field model. To confirm the effectiveness of the developed adjoint model, numerical tests called “twin experiments” were conducted using synthetic measurement data prepared in advance through phase-field simulation. In the twin experiments, the optimum estimates of six model parameters of interest were shown to coincide with true values. Furthermore, the effects of the standard deviation of measurement noise and the time interval of measurements on the uncertainties of optimum estimates of parameters were successfully quantified by the twin experiments. |
Proceedings Inclusion? |
Planned: |
Keywords |
Computational Materials Science & Engineering, ICME, Phase Transformations |