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首页> 外文期刊>Computational Materials Science >Stochastic modeling of individual grain behavior during Ostwald ripening at ultra-high volume fractions of the coarsening phase
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Stochastic modeling of individual grain behavior during Ostwald ripening at ultra-high volume fractions of the coarsening phase

机译:粗化阶段超高体积分数下奥斯特瓦尔德熟化过程中单个晶粒行为的随机模型

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摘要

The evolution of grains during coarsening phenomena like Ostwald ripening is a focus of recent and ongoing research. In the present paper, a new and flexible model is proposed that describes the statistical evolution of the "typical" individual grain size as a function of neighborhood characteristics. The grain size evolution (GSE) model defines a stochastic process based on contemporary mathematical techniques and requires only few (natural) assumptions. It is fitted to time-resolved experimental data of a semisolid Al-Cu alloy, in which the coarsening phase has an ultra-high volume fraction V-v = 0.93. Evaluation shows that the model describes the experimental data quite closely. The nature of this modeling approach serves to improve the understanding of coarsening processes at the intermediate level between coarsening mechanisms and global statistical properties. Furthermore, the model enables predictive simulations to be performed, based on an extension of an existing 3D microstructure model (Spettl et al., 2015) to 4D. (C) 2016 Elsevier B.V. All rights reserved.
机译:诸如奥斯特瓦尔德(Ostwald)熟化等粗化现象期间晶粒的演变是近期和正在进行的研究的重点。在本文中,提出了一种新的灵活模型,该模型描述了“典型”个体晶粒尺寸随邻域特征的统计演变。晶粒尺寸演化(GSE)模型基于现代数学技术定义了一个随机过程,并且只需要很少的(自然)假设即可。它适合于半固态Al-Cu合金的时间分辨实验数据,其中粗化相的超高体积分数V-v = 0.93。评估表明,该模型非常接近地描述了实验数据。这种建模方法的性质有助于提高对粗化过程和粗略统计特性之间的中间水平的粗化过程的理解。此外,基于现有3D微结构模型(Spettl等人,2015)对4D的扩展,该模型能够执行预测性仿真。 (C)2016 Elsevier B.V.保留所有权利。

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