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

摘要: The current study presents an artificial neural network model used to predict the boiling heat transfer coefficient of different coating thicknesses a graphene-coated copper surface in pool experimental setup for deionized water. characterization has been carried out structure, morphology and behavior. investigations are evaluate coefficient, flux wall superheat various nano-coated surfaces experimentally, obtained results compared with those reported studies existing empirical correlations. After that, these outputs such as current, flux, using MATLAB-based thickness, roughness voltage input variables. admirable accuracies predicted optimal observation each test case.