A computationally efficient and mechanically compatible multi-phase-field model applied to coherently stressed three-phase solids
Sourav Chatterjee (1,2), Daniel Schwen (3), Nele Moelans (1) ((1), Department of Materials Engineering, KU Leuven, Leuven, Belgium, (2), Department of Materials Science, Engineering, University of Florida,, Gainesville, FL, USA, (3) Computational Mechanics, Materials Department,

TL;DR
This paper introduces a computationally efficient multi-phase-field model with improved mechanical compatibility for simulating microstructure evolution in multi-phase alloys, validated against analytical solutions and applied to Ni-Al and Al-Cr-Ni systems.
Contribution
It extends the rank-one homogenization scheme to multi-phase systems and analytically solves compatibility equations for enhanced efficiency and accuracy.
Findings
The model accurately predicts microstructures in stressed three-phase alloys.
It maintains consistent results regardless of interface width.
The partial rank-one scheme shows improved convergence over traditional methods.
Abstract
Engineering alloys generally exhibit multi-phase microstructures. For simulating their microstructure evolution during solid-state phase transformation, CALPHAD-guided multi-phase-field models coupled with micro-mechanics have proven to be a reliable simulation tool. Nevertheless, their efficiency and accuracy still depend on the homogenization scheme used to interpolate the elastic properties in the interfacial regions. In this paper, we present a phase-field model for multi-phase and multi-component solids using a partial rank-one homogenization scheme that enforces static and kinematic compatibilities in the interfacial regions. To this end, we first extend the rank-one homogenization scheme to multi-phase systems. Moreover, for computational efficiency, we analytically solve the static compatibility equations for linear elastic three-phase solids. For quantitative accuracy, a…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
