Predictive framework to evaluate ternary nanocomposite over surface subjected to novel physical perspective

Area of Science:

• * Nanofluidics and Heat Transfer
• * Materials Science and Engineering
• * Computational Fluid Dynamics

Background:

• * Advanced thermal fluids are crucial for modern technology and large-scale industrial processes.
• * Nanoparticles enhance fluid thermal conductivity, leading to improved efficiency in thermal engineering systems.
• * Ternary nanofluids, suspensions of three distinct nanoparticles, offer a novel approach to optimize thermal performance.

Purpose of the Study:

• * To optimize a working system by incorporating ternary nanoparticles (graphene oxide, copper, silver) in kerosene flow along an extended surface.
• * To analyze the thermal and momentum transport characteristics of this ternary nanofluid.
• * To investigate the influence of a magnetic field and radiative heat transfer on system performance.

Main Methods:

• * Mathematical modeling of transport equations using similarity transformations to convert PDEs into ODEs.
• * Numerical simulation employing shooting and Runge-Kutta methods for result computation.
• * Application of a machine learning (Levenberg-Marquardt algorithm) for parameter prediction.

Main Results:

• * Monoparticle dispersion improved momentum profiles more than hybrid or ternary nanoparticles, while ternary nanofluids enhanced thermal distribution.
• * The skin friction coefficient increased by up to 74% due to the magnetic field.
• * Quadratic thermal radiation intensified the heat flux coefficient by approximately 5% compared to linear radiation.

Conclusions:

• * Ternary nanofluids, specifically GO-Cu-Ag in kerosene, offer significant potential for enhancing thermal transport in engineering systems.
• * Magnetic fields and quadratic radiation effects play crucial roles in optimizing heat transfer and fluid dynamics.
• * The study provides valuable insights for designing efficient thermal management systems using advanced nanofluids.