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

    研究成果: Contribution to journalArticle査読

    10 被引用数 (Scopus)

    抄録

    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.

    本文言語英語
    ページ(範囲)1393-1411
    ページ数19
    ジャーナルJournal of Thermal Analysis and Calorimetry
    140
    3
    DOI
    出版ステータス出版済み - 5 1 2020

    All Science Journal Classification (ASJC) codes

    • Condensed Matter Physics
    • Physical and Theoretical Chemistry

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