A New Bubble Image Model Based on the Recognition of Bubble Flow

气泡 流量(数学) 液体气泡 机械 计算机科学 物理
作者
Guohui Li,Xue Liu,Yang Liu
出处
期刊:Chemical Engineering & Technology [Wiley]
标识
DOI:10.1002/ceat.202400009
摘要

Abstract In this study, a new ellipse‐fitting algorithm is proposed to achieve the reconstruction of bubble shapes in bubbly flow captured by a high‐speed camera in the gas–liquid two‐phase column reactor. Bubble flow patterns and geometric parameters in the experimental images are recognized and identified successfully, represented by means of the topological parameters. Three logical steps are carried out in detail. First, the area threshold and the circularity factors are established to identify the bubbles whether belonging to a single bubble or not. The overlapping bubbles in images can be separated from single bubbles based on a watershed segmentation algorithm. Second, a single bubble image and an overlapping bubble image are combined into one image. After that, statistical analysis for the size distributions and ellipse area bubbles is performed for further analysis and discussion. The advantage of this algorithm is that it can make use of a set of major and minor axes of an ellipse to capture the ellipse parameters more effectively. Simulation results are well agreed with experimental measurements. Moreover, it can be used to detect many ellipse‐like bubbles that are dispersed in high‐speed camera images, indicating that it is a better strategy for the recognition and identification of bubbly turbulent flow accurately.

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
蓝天发布了新的文献求助10
1秒前
林北bei发布了新的文献求助10
1秒前
sonia0720完成签到,获得积分10
2秒前
青柠大大发布了新的文献求助20
2秒前
3秒前
柒柒玖关注了科研通微信公众号
4秒前
谦让的南蕾完成签到,获得积分10
4秒前
Kitty完成签到,获得积分10
4秒前
4秒前
5秒前
6秒前
6秒前
6秒前
6秒前
6秒前
6秒前
6秒前
WilliamYuan应助高高难破采纳,获得10
6秒前
6秒前
6秒前
6秒前
今后应助科研通管家采纳,获得10
6秒前
6秒前
xzy998应助科研通管家采纳,获得10
7秒前
7秒前
bkagyin应助科研通管家采纳,获得10
7秒前
7秒前
领导范儿应助科研通管家采纳,获得10
7秒前
Ava应助科研通管家采纳,获得10
7秒前
Ava应助科研通管家采纳,获得10
7秒前
隐形曼青应助科研通管家采纳,获得10
7秒前
tRNA完成签到,获得积分10
7秒前
AcademicElite完成签到,获得积分10
8秒前
彭于晏应助王琰采纳,获得10
9秒前
nbhb发布了新的文献求助10
10秒前
kakaable发布了新的文献求助30
11秒前
NexusExplorer应助Dora采纳,获得10
11秒前
慕青应助锦墨人生采纳,获得30
12秒前
podo完成签到,获得积分10
12秒前
隐形曼青应助jiaying采纳,获得10
14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
PowerCascade: A Synthetic Dataset for Cascading Failure Analysis in Power Systems 2000
Picture this! Including first nations fiction picture books in school library collections 1000
Signals, Systems, and Signal Processing 610
Unlocking Chemical Thinking: Reimagining Chemistry Teaching and Learning 555
Photodetectors: From Ultraviolet to Infrared 500
信任代码:AI 时代的传播重构 450
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 物理 内科学 复合材料 催化作用 物理化学 光电子学 电极 细胞生物学 基因 无机化学
热门帖子
关注 科研通微信公众号,转发送积分 6357030
求助须知:如何正确求助?哪些是违规求助? 8171592
关于积分的说明 17205313
捐赠科研通 5412728
什么是DOI,文献DOI怎么找? 2864768
邀请新用户注册赠送积分活动 1842216
关于科研通互助平台的介绍 1690446