Flow boiling heat transfer analysis of Al2O3 and TiO2 nanofluids in horizontal tube using artificial neural network (ANN)

  • Manish DadhichEmail author
  • Om Shankar Prajapati
  • Nirupam Rohatgi


A nanofluid is a suspension of nanometer-sized particles in a base fluid. In the last decade, flow boiling of nanofluid has gained much attention. However, only a few correlations on flow boiling are available. In this paper, an experimental study for HTC (heat transfer coefficient) of water-based TiO2 and Al2O3 nanofluids flowing in an annulus has been carried out at 1 bar. The volumetric concentration of the nanofluid was varied from 0.05 to 0.20%, and heat flux and the mass flux were varied from 6.25 to 143.2 kW m−2 and 338 to 1014 kg m−2 s−1, respectively. It was observed that HTC for both the nanofluids was greater than that of the base fluid water, and it increased with increase in the concentration of the nanoparticles, the heat flux and the mass flux. The highest HTC was obtained for Al2O3 nanofluid at 0.20% concentration for the heat flux of 143.2 kW m−2 and mass flux of 1014 kg m−2 s−1. It was found that nanofluid made from Al2O3 nanoparticles had better HTC than nanofluid made from TiO2 nanoparticles. The HTC ratios, i.e., the ratio of HTC of the nanofluid to the HTC of the base fluid, also increased with the increase in concentration, heat flux and mass flux. In the later part of the paper, new correlations were developed for predicting HTC for TiO2 and Al2O3 nanofluids. Finally, an ANN model was developed to predict the heat transfer coefficient. Experimental values were found to be in good agreement with ANN predictions.


Nanofluids Heat transfer coefficient Mass flux Heat flux Concentration Correlation Artificial neural network 

List of symbols


Concentration of nanofluids


Specific heat at constant pressure (J kg−1 K−1)


The hydraulic diameter of the tube m


Two-phase multiplier


Boiling heat transfer coefficient (kW m−2 K−1)


Latent heat of vaporization (J kg−1)


Thermal conductivity (Wm−1 K−1)


Total mass flux of the liquid and vapor flowing (kg m−2 s−1)


Mass of nanoparticle gm


Number of independent variables


Nusselt number


Prandtl number

\(\Delta p_{\text{sat}}\)

\(\left( {p_{\text{wall}} - p_{\text{sat}} } \right)\) (Pa)


Heat flux (kW m−2)


Uncertainties associated with the dependent variables


Reynolds number


Correlation coefficient


Nucleate boiling suppression factor

\(\Delta T_{\text{sat}}\)

\(\left( {T_{\text{wall}} - T_{\text{sat}} } \right)\) (K)


Vapor quality


Martinelli parameter


Uncertainties associated with the independent variables

Greek symbols


Convective heat transfer coefficient (kW m−2 K−1)


Dynamic viscosity (kg m−1 s−1)


Density (kg m−3)


Surface tension (Nm−1)


Nanoparticles volume concentration



Base fluid


Convective boiling


Forster and Zuber


Gas phase


Specific parameter counter


Liquid phase


Liquid–gas phase


Nucleate boiling









Artificial neural network


Computer-aided design


Critical heat flux (kW m−2)


Data acquisition


Direct current (ampere)




Expanded polyethylene


Hard disk drive


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


Mean square error


Onset of nucleate boiling


Random access memory


Stainless steel


Ultrasonic vibration machine



The authors express their gratitude to Malaviya National Institute of Technology, Jaipur and University Teaching Department, Rajasthan Technical University, Kota, for their support in carrying out this work.


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Copyright information

© Akadémiai Kiadó, Budapest, Hungary 2019

Authors and Affiliations

  1. 1.Department of Mechanical Engineering, University Teaching DepartmentRajasthan Technical UniversityKotaIndia
  2. 2.Department of Mechanical EngineeringMalaviya National Institute of TechnologyJaipurIndia

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