Exploring the thermal attributes of nano-composition (GQDs+Bi2Se3+Ag) suspended in therminol VP-1: An artificial intelligence based approach

Abstract

Efficient thermal management is required in advanced engineering applications such as energy systems, electronics cooling, and industrial processes. The exceptional thermal properties of graphene quantum dots GQDs combined with the thermoelectric performance of bismuth selenide Bi₂Se₃ and the high conductivity of silver Ag provide significant advancements in heat transfer efficiency and thermal control systems. We explore, in this study, the novel thermal attributes of a ternary nano-composition consisting of GQDs + Bi₂Se₃+Ag particles suspended in Therminol VP-1. The incorporation of thermal radiation and activation energy offers insights into the temperature-sensitive processes. The analysis covers the features of three types of nano-compositions such as GQDs/Therminol VP-1, GQDs-Bi₂Se₃/Therminol VP-1 and, GQDs-Bi₂Se₃-Ag/Therminol VP-1. An order reduction approach is applied to streamline the mathematical modelling and computational efforts while preserving the system's accuracy. The analysis incorporates a machine learning technique based on recurrent neural network (RNN) to evaluate the nonlinear impacts of the physical parameters. The outcomes evidently disclose the fact that the volume concentration ${\Phi }_2$ of bismuth selenide and ${\Phi }_3$ of silver tend to elevate the temperature in usual, hybridized and tri-hybridized cases of nano-compositions. The heat transfer rate increased up to 24.5 % when the volume concentration ${\Phi }_2$ of bismuth selenide and ${\Phi }_3$ of silver increased up to 0.7 and 0.3 respectively. The activation energy $A_E$ substantially promoted the concentration in either case of nano-composition e.g., ternary, binary and pure nano-composition case.