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.

Graphical Abstract

Keywords:

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

Classification:

• 60 New topics / Others
• 404 Materials informatics / Genomics
• 403 CALPHAD / Phase field methods
• 400 Modeling / Simulations
• 500 Characterization

Notes

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the Council for Science, Technology and Innovation (CSTI), the Cross-ministerial Strategic Innovation Promotion Program (SIP) ‘Structural Materials for Innovation’ (Funding agency: JST).