A vertex model of recrystallization with stored energy implemented in GPU

• Published in: Materials research express Vol. 6; no. 5; pp. 55506 - 55530
• Main Authors: Sazo, Alejandro H J, Torres, Claudio E, Emelianenko, Maria, Golovaty, Dmitry
• Format: Journal Article
• Language: English
• Published: IOP Publishing 06.02.2019
• Subjects: grain growth; numerical simulation; Polycrystalline materials; recrystallization; stored energy; vertex model
• ISSN: 2053-1591; 2053-1591
• DOI: 10.1088/2053-1591/ab00f8

Recrystallization models and simulations have been the subject of much attention in materials community in the past decades due to this process having a significant effect on many technologically important materials characteristics. Statistical analysis performed close to the steady state requires large-scale simulations, which are often prohibitively expensive from computational point of view. Graphical Processing Unit (GPU)-based realizations provide a viable approach to addressing this challenge, yet they remain relatively under-explored in this context.In the present manuscript, we develop a fully-parallelizable matrix-free GPU-based algorithm for implementing a two-dimensional vertex model of recrystallization based on the stored energy formalism. Nucleation is assumed to take place at triple junctions and obeys a Metropolis-type criterion. We include a complete mathematical analysis of the nucleation model deriving conditions under which nucleation is successful. Stability analysis of the dynamics of a triple junction under the presence of bulk energy is provided. On the computational side, we propose a novel polling system for handling topological transitions to ensure robust GPU implementation. Single grain tests are performed for benchmarking purposes. Finally, a set of numerical experiments for large scale systems is presented to explore the effect of initial distributions of stored energy on several statistical characteristics.