.png){kind=icon}
返回
Multiscale finite-difference-diffusion Monte-Carlo method for simulating dendritic solidification
.png){kind=icon}
OA
AI
DOI：10.1006/jcph.2000.6634.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
We present a novel hybrid computational method to simulate accurately dendritic solidification in the low undercooling limit where the dendrite tip radius is one or more orders of magnitude smaller than the characteristic spatial scale of variation of the surrounding thermal or solutal diffusion field. The first key feature of this method is an efficient multiscale diffusion Monte Carlo (DMC) algorithm which allows off-lattice random walkers to take longer and concomitantly rarer steps with increasing distance away from the solid-liquid interface. As a result, the computational cost of evolving the large-scale diffusion field becomes insignificant when compared to that of calculating the interface evolution. The: second key feature is that random walks are only permitted outside of a thin liquid layer surrounding the interface. Inside this layer and in the solid, the diffusion equation is solved using a standard finite difference algorithm that is interfaced with the DMC algorithm using the local conservation law for the diffusing quantity. Here we combine this algorithm with a previously developed phase-field formulation of the interface dynamics and demonstrate that it can accurately simulate three-dimensional dendritic growth in a previously unreachable range of low undercoolings that is of direct experimental relevance. (C) 2000 Academic Press.
Keyword:
dendritic growth
solidification
phase transformations
moving boundary problem
Stefan problem
multiscale algorithm
adaptive meshing
Monte Carlo method
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.8
论文数:
1.6W
被引数:
7.4W
机构
暂无机构信息
引用论文
.png){kind=icon}
Cyclic peptide unguisin A is an anion receptor with high affinity for phosphate and pyrophosphate
PREAI
分享收藏
查看更多内容