Shinji Sakane

Heterogeneity in homogeneous nucleation from billion-atom molecular dynamics simulation of solidification of pure metal

Can completely homogeneous nucleation occur? Large scale molecular dynamics simulations performed on a graphics-processing-unit rich supercomputer can shed light on this long-standing issue. Here, a billion-atom molecular dynamics simulation of homogeneous nucleation from an undercooled iron melt reveals that some satellite-like small grains surrounding previously formed large grains exist in the middle of the nucleation process, which are not distributed uniformly. At the same time, grains with a twin boundary are formed by heterogeneous nucleation from the surface of the previously formed grains. The local heterogeneity in the distribution of grains is caused by the local accumulation of the icosahedral structure in the undercooled melt near the previously formed grains. This insight is mainly attributable to the multi-graphics processing unit parallel computation combined with the rapid progress in high-performance computational environments.Nucleation is a fundamental physical process, however it is a long-standing issue whether completely homogeneous nucleation can occur. Here the authors reveal, via a billion-atom molecular dynamics simulation, that local heterogeneity exists during homogeneous nucleation in an undercooled iron melt.

Ultra-large-scale phase-field simulation study of ideal grain growth

Grain growth, a competitive growth of crystal grains accompanied by curvature-driven boundary migration, is one of the most fundamental phenomena in the context of metallurgy and other scientific disciplines. However, the true picture of grain growth is still controversial, even for the simplest (or ‘ideal’) case. This problem can be addressed only by large-scale numerical simulation. Here, we analyze ideal grain growth via ultra-large-scale phase-field simulations on a supercomputer for elucidating the corresponding authentic statistical behaviors. The performed simulations are more than ten times larger in time and space than the ones previously considered as the largest; this computational scale gives a strong indication of the achievement of true steady-state growth with statistically sufficient number of grains. Moreover, we provide a comprehensive theoretical description of ideal grain growth behaviors correctly quantified by the present simulations. Our findings provide conclusive knowledge on ideal grain growth, establishing a platform for studying more realistic growth processes.Grain growth: Simulations elucidate statistical behaviorGrain growth under ideal conditions is simulated by phase-field simulations in ultra-large time and space scales to elucidate the statistical behaviors. A team led by Tomohiro Takaki at Kyoto Institute of Technology in Japan performed large scale phase-field simulations to study ideal grain growth behavior. The time and space scales used in the simulations are more than ten times larger than those in previous reports, enabling them to reach a true steady-state with a statistically significant number of grains. A comprehensive theoretical description was derived to understand the ideal grain growth behavior based on the simulations. The knowledge provided by these findings may offer a model to understand the effects of complicated factors present in real materials and thus establish a platform to study more realistic grain growth phenomena in the future.

GPU-accelerated 3D phase-field simulations of dendrite competitive growth during directional solidification of binary alloy

Phase-field method has emerged as the most powerful numerical scheme to simulate dendrite growth. However, most phase-field simulations of dendrite growth performed so far are limited to two-dimension or single dendrite in three-dimension because of the large computational cost involved. To express actual solidification microstructures, multiple dendrites with different preferred growth directions should be computed at the same time. In this study, in order to enable large-scale phase-field dendrite growth simulations, we developed a phase-field code using multiple graphics processing units in which a quantitative phase-field method for binary alloy solidification and moving frame algorithm for directional solidification were employed. First, we performed strong and weak scaling tests for the developed parallel code. Then, dendrite competitive growth simulations in three-dimensional binary alloy bicrystal were performed and the dendrite interactions in three-dimensional space were investigated.

Correlation between three-dimensional and cross-sectional characteristics of ideal grain growth: large-scale phase-field simulation study

Grain growth is one of the most fundamental phenomena affecting the microstructure of polycrystalline materials. In experimental studies, three-dimensional (3D) grain growth is usually investigated by examining two-dimensional (2D) cross sections. However, the extent to which the 3D microstructural characteristics can be obtained from cross-sectional observations remains unclear. Additionally, there is some disagreement as to whether a cross-sectional view of 3D grain growth can be fully approximated by 2D growth. In this study, by employing the multi-phase-field method and parallel graphics processing unit computing on a supercomputer, we perform large-scale simulations of 3D and 2D ideal grain growth with approximately three million initial grains. This computational scale supports the detailed comparison of 3D, cross-sectional, and 2D grain structures with good statistical reliability. Our simulations reveal that grain growth behavior in a cross section is very different from those in 3D and fully 2D spaces, in terms of the average and distribution of the grain sizes, as well as the growth kinetics of individual grains. On the other hand, we find that the average grain size in 3D can be estimated as being around 1.2 times that observed in a cross section, which is in good agreement with classical theory in the stereology. Furthermore, based on the Saltykov–Schwartz method, we propose a predictive model that can estimate the 3D grain size distribution from the cross-sectional size distribution.