Critical heat fluxes for subcooled flow boiling in optimised microchannels

IF 5.3 Q1 ENGINEERING, MECHANICAL International Journal of Hydromechatronics Pub Date : 2020-06-15 DOI:10.1504/ijhm.2020.10029839
D. O. Ariyo, T. Bello‐Ochende
{"title":"Critical heat fluxes for subcooled flow boiling in optimised microchannels","authors":"D. O. Ariyo, T. Bello‐Ochende","doi":"10.1504/ijhm.2020.10029839","DOIUrl":null,"url":null,"abstract":"Critical heat fluxes (departure from nucleate boiling, DNB) for optimised rectangular microchannels were computed by carrying out simulations using computational fluid dynamics (CFD, ANSYS) non-equilibrium boiling model (the model is an extension of heat flux partitioning model for modelling up to departure from nucleate boiling) for subcooled flow boiling, at velocity ranges of 1.0-1.5 and 1.5-2.0 m/s and heat fluxes of 300 to 500 W/cm2. The flow was highly subcooled at inlet temperature of 25°C using deionised water as the cooling fluid and aluminium as the heat sink material. The results showed that the microchannels can be operated beyond the heat fluxes that they were optimised, up to their critical heat fluxes before the onset of departure from nucleate boiling or CHF. The numerical code for heat flux partitioning model (RPI) was validated by the available experimental data and the agreement showed the capability of the model to predict accurately subcooled flow boiling in microchannels.","PeriodicalId":29937,"journal":{"name":"International Journal of Hydromechatronics","volume":"1 1","pages":""},"PeriodicalIF":5.3000,"publicationDate":"2020-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"13","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Hydromechatronics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1504/ijhm.2020.10029839","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, MECHANICAL","Score":null,"Total":0}
引用次数: 13

Abstract

Critical heat fluxes (departure from nucleate boiling, DNB) for optimised rectangular microchannels were computed by carrying out simulations using computational fluid dynamics (CFD, ANSYS) non-equilibrium boiling model (the model is an extension of heat flux partitioning model for modelling up to departure from nucleate boiling) for subcooled flow boiling, at velocity ranges of 1.0-1.5 and 1.5-2.0 m/s and heat fluxes of 300 to 500 W/cm2. The flow was highly subcooled at inlet temperature of 25°C using deionised water as the cooling fluid and aluminium as the heat sink material. The results showed that the microchannels can be operated beyond the heat fluxes that they were optimised, up to their critical heat fluxes before the onset of departure from nucleate boiling or CHF. The numerical code for heat flux partitioning model (RPI) was validated by the available experimental data and the agreement showed the capability of the model to predict accurately subcooled flow boiling in microchannels.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
优化微通道中过冷流沸腾的临界热流密度
利用计算流体动力学(CFD, ANSYS)非平衡沸腾模型(该模型是热流分配模型的扩展,用于模拟偏离核沸腾)对优化后的矩形微通道进行了过冷沸腾的临界热流密度(偏离核沸腾,DNB)的模拟,速度范围为1.0-1.5和1.5-2.0 m/s,热流密度为300至500 W/cm2。在进口温度为25°C时,使用去离子水作为冷却流体,铝作为散热材料,流体高度过冷。结果表明,微通道可以在超出优化热流密度的范围内运行,直至偏离核沸腾或CHF开始前的临界热流密度。实验数据验证了热流分配模型(RPI)数值代码的正确性,表明该模型能够准确预测微通道内过冷流动沸腾。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
CiteScore
7.60
自引率
0.00%
发文量
32
期刊最新文献
A comparative study of energy-efficient clustering protocols for WSN-internet-of-things A mayfly optimisation method to predict load settlement of reinforced railway tracks on soft subgrade with multi-layer geogrid Parameter optimization design of mixing and distributing system of vertical biaxial bladed mixer Research on singular point characteristics and parameter bifurcation of single DOF nonlinear autonomous bearing system of magnetic-liquid double suspension bearing An improved gated convolutional neural network for rolling bearing fault diagnosis with imbalanced data
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1