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

• 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
• ISSN: 1388-6150; 1588-2926
• DOI: 10.1007/s10973-019-08740-5

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.