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- Language
- English
- Conflict of Interest
- In relation to this article, we declare that there is no conflict of interest.
- Publication history
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Received April 23, 2022
Accepted April 9, 2024
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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.
Most Cited
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
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.
