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

  • Sameer S. GajghateEmail author
  • Sreeram Barathula
  • Sudev Das
  • Bidyut B. Saha
  • Swapan Bhaumik
Article
  • 68 Downloads

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.

Keywords

Nucleate pool boiling heat transfer Heat transfer coefficient Critical heat flux Graphene Dip coating Artificial neural network 

List of symbols

HTC

Heat transfer coefficient (h) (kW m−2 K−1)

CHF

Critical heat flux (W m−2)

Tn, To, Tp

Temperature of copper heating block (°C)

Tq, Tr, Ts

Calculated temperature of sample at different sections (°C)

Tl

Temperature of base fluid (°C)

Ts

Surface temperature of specimen (°C)

T

Wall superheat (°C)

Qpq, Qqr, Qrs

Heat flux at various sections (W m−2)

Kpq, Kqr, Krs

Thermal conductivity of copper material at various sections (W m−1 K−1)

Apq, Aqr, Ars

Cross-sectional area at various sections (m2)

xpq, ∆xrs, ∆xrs

Thickness of surface at various sections (m)

k

Thermal conductivity of the working fluid (W m−1 K−1)

Pr

Prandtl number \(\left( {\frac{{\mu C_{\text{p}} }}{K}} \right)\)

Cp

Specific heat of the working fluid (kJ kg−1 K−1)

hfg

Latent heat of vaporization of the working fluid (kJ kg−1)

q

Pool boiling heat transfer (W m−2)

I

Current (A)

AI

Artificial intelligence

ANN

Artificial neural networks

GEP

Gene expression programming

SVR

Support vector regression

IVR-ERVC

In-vessel retention through extend reaction vessel cooling

CNT

Carbon nanotubes

PEG

Polyethylene glycol

Cu

Copper

XRD

X-ray diffraction

AFM

Atomic force microscopy

SEM

Scanning electron microscopy

DI

De-ionized

d

Interplanar spacing

h, k, l

Miller indices

a, c

Lattice constants

g

Acceleration due to gravity (m s−2)

MLP

Multilayer perceptron

Trainlm

Levenberg–Marquardt backpropagation

Trainscg

Scaled conjugate gradient backpropagation

Trainbfg

BFGS quasi-Newton backpropagation

Traingda

Gradient descent with adaptive learning rate backpropagation

Trainsig

Hyperbolic tangent sigmoid transfer function

Logsig

Log-sigmoid

MATLAB

Matrix laboratory

MSE

Mean square error

R

Regression coefficient

MAPE

Mean absolute percentage error

n

Total number of output data

Greek words

µ

Viscosity (N s m−2)

σ

Surface tension (N m−2)

ρ

Density (kg m−3)

λ

Wavelength (m)

Subscript

s

Surface

l

Liquid

v

Vapour

exp

Experimental values

opt

Predicted values

Notes

Compliance with ethical standards

Conflict of interest

The author declares that they have no conflict of interest.

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Copyright information

© Akadémiai Kiadó, Budapest, Hungary 2019

Authors and Affiliations

  1. 1.Mechanical Engineering DepartmentNational Institute of Technology AgartalaJiraniaIndia
  2. 2.Chemical Engineering DepartmentNational Institute of Technology CalicutKozhikodeIndia
  3. 3.International Institute for Carbon-Neutral Energy Research (WPI-I2CNER)Kyushu UniversityFukuokaJapan

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