Grain growth prediction based on data assimilation by implementing 4DVar on multi-phase-field model

Abstract

We propose a method to predict grain growth based on data assimilation by using a four-dimensional variational method (4DVar). When implemented on a multi-phase-field model, the proposed method allows us to calculate the predicted grain structures and uncertainties in them that depend on the quality and quantity of the observational data. We confirm through numerical tests involving synthetic data that the proposed method correctly reproduces the true phase-field assumed in advance. Furthermore, it successfully quantifies uncertainties in the predicted grain structures, where such uncertainty quantifications provide valuable information to optimize the experimental design.

Keywords

• Grain growth
• data assimilation
• Bayesian statistics
• prediction method
• phase field model
• uncertainty quantification