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

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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.

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Abbreviations

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)

T l :

Temperature of base fluid (°C)

T s :

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

C p :

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

h fg :

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

µ :

Viscosity (N s m−2)

σ :

Surface tension (N m−2)

ρ :

Density (kg m−3)

λ :

Wavelength (m)

s:

Surface

l:

Liquid

v:

Vapour

exp:

Experimental values

opt:

Predicted values

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Authors and Affiliations

  1. Mechanical Engineering Department, National Institute of Technology Agartala, Jirania, Tripura, 799046, India

    Sameer S. Gajghate, Sreeram Barathula & Swapan Bhaumik

  2. Chemical Engineering Department, National Institute of Technology Calicut, Kozhikode, Kerala, 673601, India

    Sudev Das

  3. International Institute for Carbon-Neutral Energy Research (WPI-I2CNER), Kyushu University, Fukuoka, 819-0385, Japan

    Bidyut B. Saha

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  1. Sameer S. Gajghate
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Correspondence to Sameer S. Gajghate.

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Gajghate, S.S., Barathula, S., Das, S. et al. Experimental investigation and optimization of pool boiling heat transfer enhancement over graphene-coated copper surface. J Therm Anal Calorim 140, 1393–1411 (2020). https://doi.org/10.1007/s10973-019-08740-5

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