Computational model for predicting the dynamic dissolution and evolution behaviors of gases in liquids

溶解 传质 饱和(图论) 热力学 氧气 体积流量 冷凝 机械 流量(数学) 化学 物理 物理化学 数学 组合数学 有机化学
作者
Zhipeng Ren,Deyou Li,Hongjie Wang,Jintao Liu,Yong Li
出处
期刊:Physics of Fluids [American Institute of Physics]
卷期号:34 (10) 被引量:15
标识
DOI:10.1063/5.0118794
摘要

Dynamic gas–liquid mass transfer behaviors are widely encountered in the chemical, environmental, and engineering fields. Referring to the Singhal full cavitation model, Henry's law, and Zhou's experiments, we innovatively developed a computational model for dissolved and released mass-transfer to revolutionize the independent unidirectional gas-to-liquid or liquid-to-gas theory. From a new perspective, coupled dissolution and evolution mechanisms were defined similar to how condensation and evaporation were redefined, where dissolution and release mass-transfer prediction methods that can be applied to three-dimensional calculations were integrated for the first time. The dissolved gas saturation concentration was the criterion for determining the direction of mass transfer. According to the theoretical derivation, the driving forces behind the dissolution and evolution are the remaining undissolved gas and real-time solution concentration, respectively. We confirmed the validity of the proposed dynamic model using an unsteady simulation after a grid independence study and an experimental verification of dissolved oxygen concentration in plug-discharge flow. The difference in dissolved oxygen concentration between simulations of this computational model and experiments could be low as 2.0%. A higher dissolved oxygen concentration was distributed in the flow separation and throat gas–liquid blocking zones, indicating that a surge in the flow velocity led to an increased mass transfer rate. In addition, a parametric study was conducted to consider the impact of the oxygen volume fraction and initial dissolved oxygen concentration on the real-time concentration.
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
故意的傲玉应助圈圈采纳,获得10
刚刚
1秒前
522完成签到,获得积分10
1秒前
1秒前
kbj发布了新的文献求助10
1秒前
2秒前
老西瓜发布了新的文献求助10
2秒前
人各有痣完成签到,获得积分10
2秒前
后知后觉发布了新的文献求助10
2秒前
xiaoxiao发布了新的文献求助30
2秒前
2秒前
3秒前
3秒前
英姑应助哈哈呀采纳,获得10
4秒前
4秒前
hurry完成签到,获得积分10
4秒前
Hungrylunch应助陈玉婷采纳,获得20
4秒前
领导范儿应助hu970采纳,获得10
5秒前
new_vision发布了新的文献求助10
5秒前
拼搏翠桃完成签到,获得积分10
6秒前
糖糖科研顺利呀完成签到 ,获得积分10
6秒前
6秒前
阿秋完成签到,获得积分10
6秒前
Pangsj发布了新的文献求助10
7秒前
hhh发布了新的文献求助10
7秒前
好运藏在善良里完成签到,获得积分10
7秒前
情怀应助奋斗映寒采纳,获得10
7秒前
8秒前
CodeCraft应助牧海冬采纳,获得10
8秒前
zxcv23完成签到,获得积分10
8秒前
9秒前
小离发布了新的文献求助10
9秒前
yug完成签到,获得积分10
9秒前
坟里唱情歌完成签到 ,获得积分10
10秒前
kbj完成签到,获得积分10
10秒前
哈哈哈哈完成签到,获得积分10
10秒前
11秒前
11秒前
11秒前
科研雷锋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