AI-based thermal management analysis of electronic devices using Cu–Fe3O4–SiO2 ternary hybrid nanofluid over a rotating disk with Hall current and dissipation

Abstract

Effective thermal management is critical challenge in advanced energy units and high-performance electronics, where escalating thermal dissipation need efficient energy strategies and sophisticated cooling mechanisms that can improve energy management. The study investigates enhance energy transition through ternary hybrid nanofluid (THNF), composed of nanoparticles of Copper (Cu), Iron magnetite (Fe 3 O 4 ) and Silica (SiO 2 ) with synthetic oil as the base, over rotating disk. Incorporating nanoparticles into conventional working fluids is recognized as an effective strategy to significantly enhance heat transfer ability. The analysis considers Hall and radiation effects, developing a mathematical model based on the assumption of compress less and purely radial flow in a curvilinear coordinate system. The system of partial differential equations (PDEs) is obtained and transformed into corresponding ordinary differential equations (ODEs) by conducting dimensionless analysis with appropriate similarity variables. Application of machine learning based Levenberg-Marquardt neural networks Algorithm (LMA) is employed for results. The findings reveal that thermo-radiation, heat dissipation, turbulence and rotation characteristics collectively contribute to increased energy transmission and enhanced heat transfer rates in tri-nanoparticle hybrid fluids. Particularly, the presence of Cu and Fe 3 O 4 nanoparticles in the base polymeric liquid significantly amplifies the rate of energy transmission. The study gives a detailed insight into velocity and temperature distribution with a valuable guidance for design of next-generation energy material and solutions.