Experimental investigation and optimization of pool boiling heat transfer enhancement over graphene-coated copper surface

The current study presents an artificial neural network model used to predict the boiling heat transfer coefficient of different coating thicknesses of a graphene-coated copper surface in the pool boiling experimental setup for deionized water. The surface characterization has been carried out to st...

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Bibliographic Details
Published in:Journal of thermal analysis and calorimetry 2020-05, Vol.140 (3), p.1393-1411
Main Authors: Gajghate, Sameer S., Barathula, Sreeram, Das, Sudev, Saha, Bidyut B., Bhaumik, Swapan
Format: Article
Language:English
Subjects:
Analytical Chemistry
Artificial neural networks
Boiling
Chemistry
Chemistry and Materials Science
Coating
Coatings
Copper
Copper products
Correlation analysis
Deionization
Graphene
Graphite
Heat flux
Heat transfer
Heat transfer coefficients
Inorganic Chemistry
Measurement Science and Instrumentation
Morphology
Neural networks
Optimization
Physical Chemistry
Polymer Sciences
Surface properties
Surface roughness
Temperature
Thickness
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Summary:The current study presents an artificial neural network model used to predict the boiling heat transfer coefficient of different coating thicknesses of a graphene-coated copper surface in the pool boiling experimental setup for deionized water. The surface characterization has been carried out to study the structure, morphology and surface behavior. The investigations are carried out to evaluate the boiling heat transfer coefficient, heat flux and wall superheat for various thicknesses of nano-coated surfaces experimentally, and the obtained results are compared with those of the reported studies and existing empirical correlations. After that, these results are compared with the outputs such as current, heat flux, wall superheat and boiling heat transfer coefficient obtained using a MATLAB-based artificial neural network model with coating thickness, surface roughness and voltage as input variables. The admirable accuracies are obtained with the predicted optimal model outputs with experimental observation in each test case.
ISSN:1388-6150
1588-2926
DOI:10.1007/s10973-019-08740-5