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Nusselt number estimation using a GBR-GSO-based machine learning predictive model in alumina and titania nanofluids in a boiling process

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Abstract

In current study, a flow boiling experimental examination has been performed on water, water-TiO2 nanofluid, and water-Al2O3 nanofluid and Gradient Boost Regression model was established to predict the Nusselt number of TiO2 and Al2O3 nanofluids. Nusselt number was calculated by varying heat flux, Reynolds number, and volumetric concentration. Good agreement was shown by water results acquired from experiments with the correlation of Chen and CFD. Results demonstrated that the Nusselt number of TiO2 and Al2O3 nanofluid was larger than water. Increment in Nusselt number was noticed with the increment in concentration, heat flux, and Reynolds number. It was seen that Al2O3 nanofluid was superior to TiO2 nanofluid. Highest improvement in the Nusselt number was 37% shown by Al2O3 nanofluid when compared to water. Correlations of Nusselt number were developed for both nanofluids. GBR-GSO machine learning model showed a good agreement with the experimental Nusselt number.

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Abbreviations

\({c}_{\rm l}\) :

Specific heat capacity of liquid, J Kg−1 K−1

C:

Concentration

\({D}_{\mathrm{h}}\) :

Hydraulic diameter of the tube, m

F:

Two-phase multiplier

h:

Heat transfer coefficient, Wm−2 K−1

\({H}_{\mathrm{fg}}\) :

Enthalpy of vaporization, Jkg−1

k:

Turbulent kinetic energy, m2s−2

\({k}_{\mathrm{l}}\) :

Thermal conductivity of the liquid, Wm−1 K−1

\(\dot{m}\) :

Mass flux, kgm−2 s−1

m:

Nanoparticle mass, kg

Nu:

Nusselt number

Pr:

Prandtl number

Q:

Heat, J

q:

Heat flux, Wm−2

R2 :

Coefficient of determination

Re:

Reynolds number

S:

Suppression factor

Sq :

Use defined source term

v:

Velocity, ms−1

x:

Vapor quality

Xtt :

Martinelli parameter

CAD:

Computer-aided design

CFD:

Computational fluid dynamics

EG:

Ethylene glycol

EPE:

Expanded poly ethylene

GBR:

Gradient boost regressor

GSO:

Grid search optimization

HTC:

Heat transfer coefficient

MWCNT:

Multi-walled carbon nano tube

MAE:

Mean absolute error

MSE:

Mean square error

RANS:

Reynolds averaged navier–stokes

RMSE:

Root mean square error

RNG:

Re-normalisation group

RPI:

Rensselaer polytechnic institute

SS:

Stainless steel

SST:

Shear stress transport

UVM:

Ultrasonic vibration machine

bf:

Base fluid

C:

Convective

cb:

Convective boiling

E:

Evaporative

FZ:

Forster-Zuber’s

g:

Vapor phase

l:

Liquid

nb:

Nucleate boiling

np:

Nanoparticle

Q:

Quenching

tp:

Two-phase

\(\Delta P\) :

Difference in wall and saturation pressure, Pa

\(\Delta T\) :

Difference in wall and saturation temperature, K

\(\alpha\) :

Volume fraction

\(\varepsilon\) :

Rate of dissipation of turbulent kinetic energy, m2s−3

\(\mu\) :

Dynamic viscosity, kgm−1 s−1

\(\nu\) :

Kinematic viscosity, m2 s−1

\(\rho\) :

Density, kgm−3

\(\sigma\) :

Surface tension, Nm−1

\(\tau\) :

Stress–strain tensor

\(\varnothing\) :

Nanofluid volumetric concentration

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Acknowledgements

The authors wish to thank the University Department, RTU, Kota, and MNIT, Jaipur.

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Authors and Affiliations

  1. Maxbrain Technologies Private Limited, Jaipur, Rajasthan, India

    Manish Dadhich & Vikas Sharma

  2. Department of Computer Engineering, National Institute of Technology, Arunachal Pradesh, Itanagar, India

    Shekhar & Koj Sambyo

  3. Department of Computer Applications, JECRC University, Jaipur, Rajasthan, India

    Shekhar

  4. Department of Electrical Engineering, Poornima College of Engineering, Jaipur, Rajasthan, India

    Gaurav Jain

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  1. Manish Dadhich
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Contributions

MD: conceptualization, investigation, writing-original draft preparation. S: methodology. KS: supervision. VS: writing-reviewing and editing. GJ: formal analysis.

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Correspondence to Manish Dadhich.

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Dadhich, M., Shekhar, Sambyo, K. et al. Nusselt number estimation using a GBR-GSO-based machine learning predictive model in alumina and titania nanofluids in a boiling process. J Therm Anal Calorim 148, 14225–14242 (2023). https://doi.org/10.1007/s10973-023-12619-x

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