Numerical analysis of catalyst particle deposition characteristics in a flue gas turbine with an improved particle motion and deposition model

沉积(地质) 颗粒沉积 烟气 粒子(生态学) 机械 粒径 材料科学 化学 航程(航空) 物理 复合材料 地质学 古生物学 海洋学 有机化学 物理化学 沉积物
作者
Liuxi Cai,Jiawei Yao,Yanfang Hou,Shun-sen Wang,Yun Li,Zhenping Feng
出处
期刊:Proceedings of the Institution of Mechanical Engineers, Part A: Journal of Power and Energy [SAGE]
卷期号:237 (8): 1790-1807
标识
DOI:10.1177/09576509231188183
摘要

To more accurately understand and predict the deposition behavior of catalyst particles in the flue gas turbine cascade, test and numerical combined study is performed in this paper. Based on the systematic analysis of the deposition process and physical mechanism of the catalyst particles, the traditional DRW model, critical velocity particle deposition model and removal model were corrected with the user defined function custom function and validated with the actual deposition morphology. On this basis, the effects of the particle Stokes number and flue gas parameters on the particle deposition characteristics of the flue gas turbine cascade were detailed investigated. The results show that the revised DRW model, critical velocity and removal model can more accurately predict the deposition location and deposition rate of particles in the turbine cascade. With the increase in the Stokes number of particles, the average particle impact rate on the blade surface gradually increased, while the average deposition rate showed a trend of first increasing and then decreasing. The average deposition rate of particles in the rotor blade surface is roughly twice as high as that in the stator surface. With the increase of the flue gas expansion ratio, the deposition rate of particles less than 3 μm gradually increases, while the deposition rate of particles greater than 3 μm tends to decrease. In addition, the change in the flue gas expansion ratio has no obvious effect on the particle deposition distribution in different size ranges.
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
刚刚
Xxxnnian完成签到,获得积分20
1秒前
fancy发布了新的文献求助10
1秒前
apple完成签到,获得积分10
1秒前
1秒前
oldlee发布了新的文献求助10
2秒前
斜杠武发布了新的文献求助10
2秒前
毕业就好发布了新的文献求助10
2秒前
wusanlinshi完成签到,获得积分20
3秒前
娜行发布了新的文献求助10
3秒前
大雄完成签到,获得积分10
3秒前
kai发布了新的文献求助10
4秒前
科研通AI5应助老西瓜采纳,获得10
4秒前
核弹完成签到 ,获得积分10
4秒前
kevin完成签到,获得积分10
5秒前
Chem is try发布了新的文献求助10
5秒前
皖医梁朝伟完成签到 ,获得积分10
5秒前
汉堡包应助野性的南蕾采纳,获得10
5秒前
5秒前
便宜小师傅完成签到 ,获得积分10
6秒前
霏冉完成签到,获得积分10
6秒前
7秒前
Grayball应助包容的剑采纳,获得10
7秒前
董小天天完成签到,获得积分10
7秒前
7秒前
华仔应助qym采纳,获得10
7秒前
琅琊为刃完成签到,获得积分10
8秒前
酷波er应助hhh采纳,获得10
8秒前
8秒前
小巧的香氛完成签到 ,获得积分10
9秒前
9秒前
9秒前
zxcv23发布了新的文献求助10
9秒前
没有名称发布了新的文献求助10
9秒前
10秒前
10秒前
zier完成签到 ,获得积分10
11秒前
阡陌完成签到,获得积分10
11秒前
华仔应助毕业就好采纳,获得10
11秒前
liyi发布了新的文献求助10
11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 3527304
求助须知:如何正确求助?哪些是违规求助? 3107454
关于积分的说明 9285518
捐赠科研通 2805269
什么是DOI,文献DOI怎么找? 1539827
邀请新用户注册赠送积分活动 716708
科研通“疑难数据库(出版商)”最低求助积分说明 709672