Experimental and numerical investigation of heat transfer and flow of water-based graphene oxide nanofluid in a double pipe heat exchanger using different artificial neural network models

In this study, a water-based graphene oxide nanofluid was utilized in a counter-current flow double pipe heat exchanger (DPHE). The inner pipe carried the hot fluid (deionized water-based fluid). In the outer pipe, the cold fluid was flown, in which the experiments were performed once for deionized water-based fluid and once for graphene oxide nanofluid. These experiments were conducted at various inlet hot fluid temperatures of 35, 45, and 55 degrees C, volumetric concentrations of 0.01, 0.055, and 0.1% for nanofluid, and cold fluid flow rates of 14.4, 18.9, and 23.4 ml/s to evaluate the thermohydraulics of the DPHE. The results demonstrated that an increase in the flow rate and concentration of nanoparticles led to an improvement in the heat transfer coefficient (HTC) . It is also observed that inlet hot fluid temperature had a negligible effect on this coefficient compared to the concentration and flow rate. Additionally, the results showed that the friction factor and pressure drop of nanofluid were higher than those of the basefluid and their values were increased by increasing the concentration. The maximum increase compared to the basefluid was 72% for the friction factor and 111%, for the pressure drop. Besides, radial basis function neural network (RBFNN) and multi-layer perceptron neural network (MLPNN) models were designed for estimating the HTC. Both models accurately predicted the coefficient, but the RBFNN model exhibited higher accuracy than the MLPNN model. The results further indicated that the nanofluid outperformed the basefluid in terms of HTC. Due to the exceptionally high thermal conductivity of graphene nanoparticles, the HTC of the nanofluid was improved by up to 85% compared to the basefluid. The optimum value of HTC was achieved at a temperature of 55 degrees C, a volume concentration of 0.1%, and a flow rate of 23.4 ml/s.

Automated summary

In this study, a water-based graphene oxide nanofluid was used in a counter-current flow double pipe heat exchanger to evaluate its thermohydraulic performance. The results showed that an increase in flow rate and nanoparticle concentration improved the heat transfer coefficient, while the temperature of the hot fluid had a minimal effect on the coefficient. The friction factor and pressure drop of the nanofluid were higher than those of the basefluid, and increased with concentration. The RBFNN model exhibited higher accuracy in predicting the coefficient compared to the MLPNN model. The nanofluid outperformed the basefluid in terms of HTC, with an improvement of up to 85% due to the high thermal conductivity of graphene nanoparticles.