Flow boiling heat transfer analysis of Al 2 O 3 and TiO 2 nanofluids in horizontal tube using artificial neural network (ANN) Manish Dadhich 1 • Om Shankar Prajapati 1 • Nirupam Rohatgi 2 Received: 17 April 2019 / Accepted: 7 August 2019 / Published online: 16 August 2019 Ó Akade ´miai Kiado ´, Budapest, Hungary 2019 Abstract 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 TiO 2 and Al 2 O 3 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 Al 2 O 3 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 Al 2 O 3 nanoparticles had better HTC than nanofluid made from TiO 2 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 TiO 2 and Al 2 O 3 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. Keywords Nanofluids Á Heat transfer coefficient Á Mass flux Á Heat flux Á Concentration Á Correlation Á Artificial neural network List of symbols C Concentration of nanofluids C p Specific heat at constant pressure (J kg -1 K -1 ) d h The hydraulic diameter of the tube m F Two-phase multiplier h Boiling heat transfer coefficient (kW m -2 K -1 ) h LG Latent heat of vaporization (J kg -1 ) k Thermal conductivity (Wm -1 K -1 ) _ m Total mass flux of the liquid and vapor flowing (kg m -2 s -1 ) m Mass of nanoparticle gm M Number of independent variables Nu Nusselt number Pr Prandtl number Dp sat p wall À p sat ð Þ (Pa) q Heat flux (kW m -2 ) dR Uncertainties associated with the dependent variables Re Reynolds number R 2 Correlation coefficient S Nucleate boiling suppression factor DT sat T wall À T sat ð Þ (K) x Vapor quality X tt Martinelli parameter dX j Uncertainties associated with the independent variables Greek symbols a Convective heat transfer coefficient (kW m -2 K -1 ) l Dynamic viscosity (kg m -1 s -1 ) q Density (kg m -3 ) r Surface tension (Nm -1 ) / Nanoparticles volume concentration & Manish Dadhich manish.ddh1988@gmail.com 1 Department of Mechanical Engineering, University Teaching Department, Rajasthan Technical University, Kota, Rajasthan, India 2 Department of Mechanical Engineering, Malaviya National Institute of Technology, Jaipur, Rajasthan, India 123 Journal of Thermal Analysis and Calorimetry (2020) 139:3197–3217 https://doi.org/10.1007/s10973-019-08674-y