CONDENSATION HEAT TRANSFER AND ENHANCEMENT OF NONAZEOTROPIC MIXTURES IN HORIZONTAL BUNDLE

Zhang Zhenguo; Wang Shiping; Geng Jianjun; Deng Songjiu(Chemical Engineering Research Institute of S C U T)

您当前的位置：

首页 >

文章列表页 >

CONDENSATION HEAT TRANSFER AND ENHANCEMENT OF NONAZEOTROPIC MIXTURES IN HORIZONTAL BUNDLE

更新时间：2025-12-16

- CONDENSATION HEAT TRANSFER AND ENHANCEMENT OF NONAZEOTROPIC MIXTURES IN HORIZONTAL BUNDLE
- CIESC Journal Issue 5, (1996)
- 作者机构：
  
  华南理工大学化工研究所!广州510641,华南理工大学化工研究所!广州510641,华南理工大学化工研究所!广州510641,华南理工大学化工研究所,广州,510641
- 作者简介：
- 基金信息：
- DOI：
  CLC： Q021
- Online First：25 October 1996，
  
  Published：25 October 1996
- 稿件说明：
移动端阅览
Zhang Zhenguo, Wang Shiping, Geng Jianjun, et al. CONDENSATION HEAT TRANSFER AND ENHANCEMENT OF NONAZEOTROPIC MIXTURES IN HORIZONTAL BUNDLE[J]. CIESC Journal, 1996, (5).
DOI：

Zhang Zhenguo, Wang Shiping, Geng Jianjun, et al. CONDENSATION HEAT TRANSFER AND ENHANCEMENT OF NONAZEOTROPIC MIXTURES IN HORIZONTAL BUNDLE[J]. CIESC Journal, 1996, (5). DOI：

摘要

Abstract

In the paper

condensation heat transfer of onazeotropic mixtures R_(1l)／R_(1l3)in in-line and staggered 3-row smooth tubes and petal shaped fin tubes was studied under natural convec tive condition.Experimental results showed that

either in-line or staggered

the effect of bun dle for nonazeotropic ixtures in petal-shaped fin tubes was very smaller

at the same condition

it averge condensation heat transfer coefficient is l.5-5 times larger than of smooth tubes.

关键词

Keywords

references

Views

145

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Analysis of flow field characteristics and mixing performance of different SK-type static mixers

Zn-doped PdO/ZrO2 with rich oxygen vacancies for the synthesis of diphenyl carbonate from CO2 and phenol

A hybrid modeling and optimization strategy for sequential simulated moving bed

Hydrolysis reaction mechanism of CNCl on the surface of composite metal CuCr2O4

Prediction model for COD in petrochemical wastewater influent based on temporal-spatial dual-stream convolutional neural network

Related Author

No data

Related Institution

No data

⁰