Optimization of laminar convective heat transfer of oil-in-water nanoemulsion fluids in a toroidal duct

This study presents numerical simulations, back propagation artificial neural networks and genetic algorithms for optimizing laminar convective heat transfer of oil-in-water nanoemulsion fluids having non-Fourier heat conduction characteristics. Firstly, a numerical study has been conducted on laminar flow and forced convective heat transfer of oil-in-water nanoemulsion fluids in toroidal ducts using Eulerian-Lagrangian two-phase approach. New correlations of drag coefficient, effective thermal conductivity and effective viscosity were adopted to improve the accuracy of simulation. Numerical results show that convective heat transfer can be enhanced by oil nanodroplets with thermal conductivity lower than that of the base fluid. Then regression models and artificial neural network models were developed based on simulation results for predicting convective heat transfer performances of nanoemulsions, considering effects of cross-sectional aspect ratio, Reynolds number, oil nanodroplet diameter and concentration. Artificial neural network models can predict mean Nusselt number and pressure drop better than the regression model. Finally, genetic algorithms was used to optimize convective heat transfer of nanoemulsions considering droplet migration. It can be found that low cross-sectional aspect ratio of width to height is beneficial for thermal performance factor. For single-objective optimization, mean Nusselt number reaches the maximum 32.3 at aspect ratio of 0.9677 and thermal performance factor reaches the maximum 1.305 at aspect ratio of 0.3935 under certain conditions. Pareto optimal set was obtained for two-objective optimization. This study would be useful for the optimal design of convective heat transfer of emulsions in toroidal ducts.