A Monte Carlo algorithm for single phase normal grain growth with improved accuracy and efficiency AITranslate

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DOI：https://doi.org/10.1016/S0927-0256(02)00361-0

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A modified two-dimensional Monte Carlo algorithm for single phase normal grain growth is proposed, which was inspired by physical grain growth mechanisms and was designed using object-oriented techniques. It leads to improved agreement of the simulation results with theoretical grain growth models, exhibits a significantly higher computational efficiency and reduces the influence of the seed on the simulation results. It was previously believed that the numerical finite size effects, which cause a lower grain growth exponent in the small grain size regime, are likely to dominate in the early stages of Monte Carlo simulations. In contrast to this conclusion, the modified algorithm proposed here correctly reproduces the theoretical predictions even in the small grain size regime and thus strongly supports the theoretical work on the relationship between self-similarity and grain growth kinetics. It can therefore be concluded that the grain boundary movement mechanism as modeled using the Monte Carlo technique inherently results in parabolic grain growth kinetics.