ISSN: 0256-1115 (print version) ISSN: 1975-7220 (electronic version)
Copyright © 2024 KICHE. All rights reserved

Articles & Issues

Language
English
Conflict of Interest
In relation to this article, we declare that there is no conflict of interest.
Publication history
Received April 23, 2022
Accepted April 9, 2024
articles This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/bync/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright © KIChE. All rights reserved.

All issues

Experimental, Price-Performance and Artifi cial Neural Network (ANN) Analysis of MWCNT-CuO/ Water-EG (50-50) Nanofl uid as a Coolant/ Antifreeze Working Fluid

Nanofl uid Advanced Research Team 1Department of Mechanical Engineering, Khomeinishahr Branch , Islamic Azad University
toghraee@iaukhsh.ac.ir
Korean Journal of Chemical Engineering, June 2024, 41(6), 1679-1689(11), https://doi.org/10.1007/s11814-024-00173-7

Abstract

In this study, the thermal conductivity (TC) of CuO–MWCNT/water–EG hybrid nanofl uid (NF) was experimentally measured.

The experiments were conducted at temperatures ranging from 30 to 50 °C, and SVFs of 0.02–1%. The results indicate

that the relative TC increases with any increase in temperature and SVF. As the SVF of nanoparticles increases, the relative

TC increases more steeply. To have an advantageous NF price–performance, the analysis of this NF was conducted and compared

with NFs enriched by single nanoparticles like COOH–MWCNT and CuO nanoparticles. An equation was provided

to determine the thermal performance of the NF versus its price. This applied equation shows the thermal performance of

the NF versus its prime cost and allows users to select the best options in terms of price and performance for application

in various pieces of equipment. According to results for hybrid suspension, fl uids with SVFs of lower than 0.25% and also

1% are the advantageous ones. Also, NFs samples that only contains CuO nanoparticles are not economical in any SVFs.

About the NFs containing MWCNT nanoparticles, no SVF was economic except the highest SVF because of its sharp TC

enhancement. On the other hand, empirical data were modeled and estimated using the artifi cial neural network (ANN) and

the empirical correlation. The modeling results show that the ANN was more accurate in estimating the experimental results.

The Korean Institute of Chemical Engineers. F5, 119, Anam-ro, Seongbuk-gu, 233 Spring Street Seoul 02856, South Korea.
TEL. No. +82-2-458-3078FAX No. +82-507-804-0669E-mail : kiche@kiche.or.kr

Copyright (C) KICHE.all rights reserved.

- Korean Journal of Chemical Engineering 상단으로