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Prediction of Al2O3–water nanofluids pool boiling heat transfer coefficient at low heat fluxes by using response surface methodology

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Abstract

Boiling heat transfer coefficient is one of the most efficient factors on the amount of transferred heat by boiling flow. New nanofluids have been extensively utilized for enhancing the performance of boiling process. Despite many experimental investigations around the pool boiling heat transfer coefficient of nanofluid, the precise mathematical scheme for the evaluation of this factor is of scarce up to now. The purpose of this research is prediction of heat transfer coefficient of Al2O3–water nanofluids in a nucleate pool boiling at low heat fluxes. The apparatus has been built to study the heat transfer coefficient in a nucleate pool boiling. Al2O3 nanoparticles are scattered into the pure water, and stability treatments are performed for the nanofluids. In the numerical simulation, the Eulerian two-phase method is applied and empirical correlations are utilized to predict bubble parameters. Since the concentration of nanoparticles in the nanofluid is low, it is considered as a homogenous liquid. Finally, a predictive equation is proposed for the heat transfer coefficient of nanofluid by using the response surface methodology. The investigated variables have a distance from the center of boiling surface, applied heat flux, nucleation site density, frequency of bubble, and bubble departure diameter. Statistical parameters reveal that the accuracy of model is suitable. Also results of response surface method demonstrate that nucleation site density and bubble departure diameter have the most and least effect on the heat transfer coefficient, respectively.

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Abbreviations

A c :

Natural convection surface (m2)

A q :

Quenching surface (m2)

ANOVA:

Analysis a variance

C D :

Drag coefficient

C p :

Heat capacity (kJ kg−1 k−1)

d bw :

Bubble departure diameter (mm)

DI:

Deionized

E :

Total energy of phase (j)

F :

Body force (N)

F :

Bubble departure frequency (1/s)

h fg :

Latent heat (kJ kg−1)

HPF:

Heat flux partitioning

I :

Electric current (Amp)

N a :

Active nucleation site density (1/m2)

P :

Pressure (Pa)

Pr :

Prantle number

q :

Applied heat flux (kW m−2)

q c :

Natural convection heat flux (kW m−2)

q e :

Evaporation heat flux (kW m−2)

q q :

Quenching heat flux (kW m−2)

r :

Radius of heater (mm)

Re :

Reynolds number

T :

Temperature (K)

T b :

Bulk temperature of liquid (K)

T w :

Temperature of boiling surface (K)

t :

Time (ms)

t g :

Bubble growth time (ms)

t w :

Bubble waiting time (ms)

V :

Voltage (V)

bf:

Base fluids

l:

Liquid

nf:

Nanofluids

np:

Nanoparticles

s:

Solid

sat:

Saturated

Γkj :

Interfacial mass transfer (kg m−3 s−1)

α :

Volume fraction of phases

θ :

Liquid contact angle (°)

λ :

Thermal conductivity (kW m−1 k−1)

µ :

Dynamic viscosity (kg m−1 s−1)

ρ :

Density (kg m−3)

σ :

Surface tension (N m−1)

φ :

Volume fraction of nanoparticles

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  1. Department of Chemical, Petroleum, and Gas Engineering, Semnan University, Semnan, Iran

    Hadi Salehi & Faramarz Hormozi

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  1. Hadi Salehi
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Salehi, H., Hormozi, F. Prediction of Al2O3–water nanofluids pool boiling heat transfer coefficient at low heat fluxes by using response surface methodology. J Therm Anal Calorim 137, 1069–1082 (2019). https://doi.org/10.1007/s10973-018-07993-w

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