Grain growth kinetics in submicrometer-scale molecular dynamics simulation
Source abstract
Abstract
Abstract Grain growth kinetics under the anisotropic grain boundary properties is investigated by large-scale and long-time molecular dynamics (MD) simulations of contentious processes of nucleation, solidification and grain growth in a submicrometer-scale system. Microstructures obtained via homogeneous nucleation from undercooled melt iron consists of approximately 1500 grains and the number of grains decreases to one tenth of the number via the grain growth process. The grain growth exponent obtained from the MD simulation deviates from the ideal value since anisotropic effects in the grain boundary properties are inherently included in MD simulations. It is confirmed that the decrease of the reduced mobility (i.e., the product of the intrinsic grain boundary mobility and the grain boundary energy) is a dominant factor for the deviation from the ideal grain growth. The deviation from the Mackenzie function for the distribution of the disorientation angle between neighboring grains implies that the preferential selection of grain boundaries with small grain boundary energies occurs during the grain growth. This enhances the anisotropy in grain boundary properties and therefore decreases the reduced mobility of the grain boundary. Moreover, a multi-phase-field simulation starting from a MD configuration results in an ideal grain growth when a constant value of the reduced mobility is employed, which validates the discussion on the reduced mobility. The new insight in this study is achieved for the first time owing to a multi-graphics processing unit (GPU) parallel computation over 50 days for one case using 128 GPUs on the GPU-rich supercomputer.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Microstructure and mechanical properties · Microstructure and Mechanical Properties of Steels · Corrosion Behavior and Inhibition
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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