Abstract:
Nanofluids are modern heat transfer fluids which can significantly increase the
thermal performance of a thermal system. It enhances the thermal conductivity of
working fluids due to adding solid nanoparticles to the base fluid. In order to use
nanofluids widely in industrial applications knowing the thermophysical properties of
these new heat transfer fluids are essential. In this research, the GA-PNN and FCMANFIS
methods are employed to present models for thermophysical properties of
nanofluids. Furthermore, modified NSGA-II technique has been used to optimise the
convective heat transfer of nanofluids in a turbulent flow regime.
In recent years considerable correlations have been suggested by different
researchers for thermophysical properties of nanofluids based on the experimental
and theoretical works, which a large number of those correlations are failed to
predict the thermophysical properties of nanofluids for a wide range of particle size,
temperature and nanoparticle volume concentrations. In this thesis, experimental
data available in literature have been used to propose models for thermophysical
properties of nanofluids to overcome this problem by using artificial intelligencebased
techniques. Two models based on FCM-ANFIS and GA-PNN techniques have
been proposed for the thermal conductivity and viscosity of nanofluids. To show the accuracy of the proposed models, the predicted result has been compared with
experimental data as well as well-cited correlations in literature. Furthermore, the
convective heat transfer of nanofluids was studied and different models based on
artificial intelligence techniques have been proposed to model the Nusselt number
and pressure drop of nanofluids in a turbulent regime. Finally, a multi-objective
optimisation technique was used to optimise the convective heat transfer
characteristics and pressure drop of nanofluids to find the best design point base on
the Pareto front of the results. The predictions of the models for all cases agreed with
the experimental data much better than the available correlations.
