Optimization through Taguchi and artificial neural networks on thermal performance of a radiator using graphene based coolant

As a typical coolant in a radiator, a combination of water and ethylene glycol is often used. Since it has a lower thermal conductivity, the addition of nano particles can increase the performance of the coolant which aids in dwindling the weight and size of the radiator. This paper deals with effect of various input variables like flow rate, inlet temperature of the coolant and Vol% of nano particles (NP) on a radiator’s heat transfer parameters like heat transfer rate (Q), convective heat transfer co-efficient (h), Reynolds number (Re), Nusselt number (Nu) and friction factor (f_f) were estimated using optimization methods through Taguchi, ANOVA and ANN. Initially Experimental trials were carried to evaluate heat transfer parameters of a radiator under forced convection by changing inlet temperature, flow rate and NP addition of a coolant. Base fluid water and EG were taken in the ratio of 70:30. To that, Nano particles of graphene were dispersed in the base fluid in the range of 0.1–0.3 Vol%. Coolant inlet temperature, flow rate and addition of nano particles were considered as input parameters (I/P). TheL₂₇ orthogonal array was used as Design of Experiments (DOE). Q, h, Nu, Re and f_fwere selected as performance variables. In addition to that, an ANOVA test through Minitab 15 was employed to evaluate the performance parameters in order to estimate each variable and its contribution in percentage. It was established that parameters like Q, h and Nu were largely influenced by the inlet temperature of a coolant, where Re and f_f were impacted by flow rate. Estimation of heat transfer parameters like Q, h, Nu, Re and f_f were done by using MATLAB through Artificial Neural Networks (ANN) and were found to be in good agreement with experimental data.