Tusas: A fully implicit parallel approach for coupled phase-field equations

TL;DR

This paper introduces Tusas, a fully implicit, scalable computational framework for simulating microstructure evolution during metal solidification, enabling larger time steps and efficient parallel computation on heterogeneous hardware.

Contribution

The paper presents a novel fully-coupled, implicit phase-field simulation approach implemented in open-source Tusas, improving scalability and efficiency for modeling solidification in metals.

Findings

Enables larger time steps beyond explicit CFL limits.

Demonstrates strong parallel scalability on heterogeneous architectures.

Provides accurate, predictive microstructure simulations for additive manufacturing.

Abstract

We develop a fully-coupled, fully-implicit approach for phase-field modeling of solidification in metals and alloys. Predictive simulation of solidification in pure metals and metal alloys remains a significant challenge in the field of materials science, as microstructure formation during the solidification process plays a critical role in the properties and performance of the solid material. Our simulation approach consists of a finite element spatial discretization of the fully-coupled nonlinear system of partial differential equations at the microscale, which is treated implicitly in time with a preconditioned Jacobian-free Newton-Krylov method. The approach allows time steps larger than those restricted by the traditional explicit CFL limit and is algorithmically scalable as well as efficient due to an effective preconditioning strategy based on algebraic multigrid and block factorization. We implement this approach in the open-source Tusas framework, which is a general, flexible tool developed in C++ for solving coupled systems of nonlinear partial differential equations. The performance of our approach is analyzed in terms of algorithmic scalability and efficiency, while the computational performance of Tusas is presented in terms of parallel scalability and efficiency on emerging heterogeneous architectures. We demonstrate that modern algorithms, discretizations, and computational science, and heterogeneous hardware provide a robust route for predictive phase-field simulation of microstructure evolution during additive manufacturing.