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

Ghosh, Supriyo; Newman, Christopher Kyle; Francois, Marianne M.

doi:10.1016/j.jcp.2021.110734

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

Journal Article · Thu Sep 30 00:00:00 EDT 2021 · Journal of Computational Physics

DOI:https://doi.org/10.1016/j.jcp.2021.110734· OSTI ID:1864991

Ghosh, Supriyo ^[1]; Newman, Christopher Kyle ^[1]; Francois, Marianne M. ^[1]

Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

In this study, 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 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.

View Accepted Manuscript (DOE)

Research Organization:: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)

Sponsoring Organization:: USDOE National Nuclear Security Administration (NNSA); USDOE Office of Science (SC)

Grant/Contract Number:: 89233218CNA000001

OSTI ID:: 1864991

Report Number(s):: LA-UR-20-23977

Journal Information:: Journal of Computational Physics, Journal Name: Journal of Computational Physics Vol. 448; ISSN 0021-9991

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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