Effects of Pipe Diameter and Stokes Number on Erosion in Elbows

腐蚀 计算流体力学 斯托克斯数 管道运输 工作(物理) 流量(数学) 机械 粒子(生态学) 地质学 岩土工程 材料科学 机械工程 工程类 雷诺数 湍流 物理 海洋学 古生物学
作者
Soroor Karimi,Alireza Asgharpour,Elham Fallah,Siamack A. Shirazi
标识
DOI:10.1115/fedsm2020-20318
摘要

Abstract Large diameter pipes and elbows are vastly used in industry especially in mining and oil and gas production. Solid particle erosion is a common issue in these pipelines, and it is important to predict it to avoid failures. Currently, laboratory experiments reported in the literature are limited to diameters less than 4 inches. Therefore, there is not much experimental data available for large diameter elbows. However, the erosion can be predicted by CFD simulations and applying erosion equations in such elbows. The goal of this project is to examine the effects of elbow diameter and Stokes number on erosion patterns and magnitude for various flow conditions for elbow diameters of 2, 4, 8, and 12 inches. The approach of this work is to first perform CFD simulations of liquid-solid and gas-solid flows in 2-inch and 4-inch elbows, respectively, and evaluate the results by available experimental data. Then CFD simulations are carried for 2, 4, 8, and 12-inch standard elbows for various Stokes numbers corresponding to gas dominant flows with the velocity of 30 m/s, and liquid dominant flows with the velocities of 6 m/s. For gas dominant flows erosion in air and for liquid dominant flows erosion in water is investigated. All these simulations are carried for four particle sizes of 25, 75, 150, and 300 microns. The results indicate that Stokes number and diameter of elbows have significant effects on erosion patterns as well as magnitudes in this geometry. This work will have various applications, including validating mechanistic models of erosion predictions in elbows and developing an Artificial Intelligence (machine learning) algorithm to predict erosion for various flow conditions. Such algorithms are limited to the range of conditions they are trained for. Therefore, it is important to expand the database these codes are accessing. Overall, the CFD database of large diameter elbows will reduce the computational costs in the future.
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
hamburgeeHH发布了新的文献求助20
刚刚
星辰大海应助angang1994采纳,获得10
1秒前
汉堡包应助Siren采纳,获得10
2秒前
美丽梦桃发布了新的文献求助10
2秒前
Li应助鑫炜赵采纳,获得30
2秒前
科研通AI6.1应助yan采纳,获得10
3秒前
3秒前
Jerry发布了新的文献求助10
3秒前
4秒前
哒mao关注了科研通微信公众号
4秒前
4秒前
zzt关闭了zzt文献求助
4秒前
Hello应助白白采纳,获得10
5秒前
赘婿应助桑榆采纳,获得10
5秒前
喜东东发布了新的文献求助10
6秒前
nielu完成签到,获得积分10
6秒前
6秒前
罗鑫完成签到,获得积分10
7秒前
你嵙这个期刊没买应助NWP采纳,获得10
7秒前
敖江风云完成签到,获得积分10
8秒前
科研通AI6.1应助無羡采纳,获得10
8秒前
9秒前
9秒前
9秒前
9秒前
9秒前
skylar发布了新的文献求助10
9秒前
10秒前
10秒前
10秒前
10秒前
李爱国应助科研通管家采纳,获得10
11秒前
11秒前
脑洞疼应助科研通管家采纳,获得30
11秒前
11秒前
mmyhn应助科研通管家采纳,获得20
11秒前
11秒前
隐形曼青应助科研通管家采纳,获得10
11秒前
Ava应助科研通管家采纳,获得30
11秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Kinesiophobia : a new view of chronic pain behavior 5000
Molecular Biology of Cancer: Mechanisms, Targets, and Therapeutics 3000
First commercial application of ELCRES™ HTV150A film in Nichicon capacitors for AC-DC inverters: SABIC at PCIM Europe 1000
Handbook of pharmaceutical excipients, Ninth edition 800
Signals, Systems, and Signal Processing 610
Digital and Social Media Marketing 600
热门求助领域 (近24小时)
化学 医学 生物 材料科学 工程类 有机化学 内科学 生物化学 物理 计算机科学 纳米技术 遗传学 基因 复合材料 化学工程 物理化学 病理 催化作用 免疫学 量子力学
热门帖子
关注 科研通微信公众号,转发送积分 5993312
求助须知:如何正确求助?哪些是违规求助? 7446290
关于积分的说明 16069199
捐赠科研通 5135574
什么是DOI,文献DOI怎么找? 2754289
邀请新用户注册赠送积分活动 1727538
关于科研通互助平台的介绍 1628814