Simulation of channel segregation using a two-phase columnar solidification model – Part I: Model description and verification

液相线 材料科学 层状结构 枝晶(数学) 相(物质) 过冷 机械 热力学 复合材料 合金 几何学 化学 物理 数学 有机化学
作者
Jun Li,Menghuai Wu,Jing Hao,A. Ludwig
出处
期刊:Computational Materials Science [Elsevier]
卷期号:55: 407-418 被引量:57
标识
DOI:10.1016/j.commatsci.2011.12.037
摘要

A numerical investigation on the formation of channel segregation using a two-phase columnar solidification model is presented in this two-part paper. Part I includes a model summary and model verification and Part II presents an in-depth discussion and parameter study on the formation mechanisms of channel segregation. The two phases considered in the model are the liquid melt and solid columnar phase. The morphology of the columnar dendrite trunks is approximated by step-wise growing cylinders with constant primary dendrite arm spacing. The columnar dendrites grow from the mold wall following the liquidus isotherm. The growth kinetics of the columnar trunks is governed by diffusion of the rejected solute surrounding the columnar trunks near the solid–liquid interface. The conservation equations for mass, momentum, species and enthalpy are solved for each phase. The permeability of the two-phase mushy zone is treated with the Blake–Kozeny approach. The model is applied in 2D and 3D simulations of segregation in a Sn–10 wt.% Pb benchmark ingot, as defined by Bellet et al. (2009) [1]. The 3D calculations show channel segregation patterns of predominantly discontinuous lamellar structure with a few rod-like channels. A series of 3D simulations with increasing thickness clarify that, with thickness greater than 0.05 m, the influence of the front and back mold walls on the center plane segregation becomes negligible. Thus, the segregation pattern on the center plane in the 3D case can be sufficiently reproduced by computationally inexpensive 2D calculations. Verification was made by comparison with published models and experimental results.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
高兴帅哥完成签到,获得积分10
刚刚
2秒前
aslink完成签到,获得积分10
2秒前
Amon完成签到,获得积分10
2秒前
啊娴仔发布了新的文献求助10
2秒前
camellia发布了新的文献求助10
2秒前
万能图书馆应助狂野觅云采纳,获得10
2秒前
充电宝应助zino采纳,获得10
3秒前
3秒前
小可发布了新的文献求助10
3秒前
英姑应助酷酷的起眸采纳,获得10
4秒前
Blue_Pig发布了新的文献求助10
4秒前
科研小白完成签到,获得积分10
5秒前
sooya发布了新的文献求助20
6秒前
6秒前
tiddler完成签到,获得积分10
6秒前
科研通AI2S应助滴滴采纳,获得10
6秒前
wgx完成签到,获得积分20
6秒前
7秒前
爱静静应助Keep采纳,获得10
7秒前
7秒前
7秒前
小马甲应助韭菜采纳,获得10
8秒前
MADKAI发布了新的文献求助10
8秒前
机智的白猫完成签到,获得积分10
8秒前
李健的小迷弟应助xxx采纳,获得10
8秒前
杜杜完成签到,获得积分10
8秒前
NexusExplorer应助新的心跳采纳,获得10
9秒前
10秒前
10秒前
10秒前
10秒前
10秒前
JamesPei应助小可采纳,获得10
10秒前
粗暴的醉卉完成签到,获得积分10
10秒前
10秒前
科研通AI5应助stt采纳,获得10
11秒前
sunzhiyu233发布了新的文献求助10
12秒前
12秒前
缓缓地安静关注了科研通微信公众号
13秒前
高分求助中
Continuum Thermodynamics and Material Modelling 3000
Production Logging: Theoretical and Interpretive Elements 2700
Social media impact on athlete mental health: #RealityCheck 1020
Ensartinib (Ensacove) for Non-Small Cell Lung Cancer 1000
Unseen Mendieta: The Unpublished Works of Ana Mendieta 1000
Bacterial collagenases and their clinical applications 800
El viaje de una vida: Memorias de María Lecea 800
热门求助领域 (近24小时)
化学 材料科学 生物 医学 工程类 有机化学 生物化学 物理 纳米技术 计算机科学 内科学 化学工程 复合材料 基因 遗传学 物理化学 催化作用 量子力学 光电子学 冶金
热门帖子
关注 科研通微信公众号,转发送积分 3527699
求助须知:如何正确求助?哪些是违规求助? 3107752
关于积分的说明 9286499
捐赠科研通 2805513
什么是DOI,文献DOI怎么找? 1539954
邀请新用户注册赠送积分活动 716878
科研通“疑难数据库(出版商)”最低求助积分说明 709759