Thermal analysis of a viscoelastic Maxwell hybrid nanofluid with graphene and polythiophene nanoparticles: Insights from an artificial neural network model

Abstract

The utilization of solar radiation by converting them into thermal energy is discussed in this paper. Nanoparticles improve the ability of heat transfer therefore, it is beneficial in the use of solar thermal systems and energy storage devices. The novel mixture of nanoparticles Graphene and Polythiophene in base fluid, which has high thermodynamic properties for the improvement of thermal effect with electromagnetic effect by using Maxwell fluid model is discussed. Polyvinyl alcohol water is taken as base fluid flowing through a moveable flat plat. The governing partial differential equations are transformed into ordinary differential equations. The semi-analytical technique, homotopy analysis method is used to obtain the solution of the ordinary differential equations. The velocity is enhanced with magnetic and electric field strength. The increase of the Prandtl number, Eckert number and chemical reaction parameter, exceeds the thermal effect which produces more entropy generation and heat enhancement. The results show that the hybrid nanofluid with this Novel mixture is highly thermodynamic with higher entropy and rapid thermal augmentation which can be used in energy production and energy storage devices. A novel intelligent numerical computing technique multi-layer perceptron with feed-forward back-propagation, an artificial neural networking method with the Levenberg-Marquard algorithm is used in this model. The data is gathered for the neural networking method training, validation, and testing. The efficiency of the model is obtained and mean square error is obtained by artificial neural networking.