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

Sameer S. Gajghate, Sreeram Barathula, Sudev Das, Bidyut B. Saha, Swapan Bhaumik

    Research output: Contribution to journalArticlepeer-review

    9 Citations (Scopus)

    Abstract

    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.

    Original languageEnglish
    Pages (from-to)1393-1411
    Number of pages19
    JournalJournal of Thermal Analysis and Calorimetry
    Volume140
    Issue number3
    DOIs
    Publication statusPublished - May 1 2020

    All Science Journal Classification (ASJC) codes

    • Condensed Matter Physics
    • Physical and Theoretical Chemistry

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