Development of a new correlation and post processing of heat transfer coefficient and pressure drop of functionalized COOH MWCNT nanofluid by artificial neural network
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Author list: Esfe M.H., Wongwises S., Esfandeh S., Alirezaie A.
Publisher: Bentham Science Publishers
Publication year: 2018
Journal: Current Nanoscience (1573-4137)
Volume number: 14
Issue number: 2
Start page: 104
End page: 112
Number of pages: 9
ISSN: 1573-4137
eISSN: 1875-6786
Languages: English-Great Britain (EN-GB)
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Abstract
Background: Because of nanofluids applications in improvement of heat transfer rate in heating and cooling systems, many researchers have conducted various experiments to investigate nanofluid's characteristics more accurate. Thermal conductivity, electrical conductivity, and heat transfer are examples of these characteristics. Method: This paper presents a modeling and validation method of heat transfer coefficient and pressure drop of functionalized aqueous COOH MWCNT nanofluids by artificial neural network and proposing a new correlation. In the current experiment, the ANN input data has included the volume fraction and the Reynolds number and heat transfer coefficient and pressure drop considered as ANN outputs. Results: Comparing modeling results with proposed correlation proves that the empirical correlation is not able to accurately predict the experimental output results, and this is performed with a lot more accuracy by the neural network. The regression coefficient of neural network outputs was equal to 99.94% and 99.84%, respectively, for the data of relative heat transfer coefficient and relative pressure drop. The regression coefficient for the provided equation was also equal to 97.02% and 77.90%, respectively, for these two parameters, which indicates this equation operates much less precisely than the neural network. Conclusion: So, relative heat transfer coefficient and pressure drop of nanofluids can also be modeled and estimated by the neural network, in addition to the modeling of nanofluid’s thermal conductivity and viscosity executed by different scholars via neural networks. © 2018 Bentham Science Publishers.
Keywords
MWCNT
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