Akademik Veri Yönetim Sistemi
Numerical Heat Transfer; Part A: Applications, cilt.86, sa.15, ss.5375-5409, 2025 (SCI-Expanded)
This article, motivated by hybrid magnetic coating manufacturing developments, utilizes a neural network-based computational program to study the dynamics of hybrid magnetic nanofluids with entropy generation. A new physico-chemo-mathematical model has been presented to simulate the hybrid magnetic nano-coating flow along a stretching surface to a porous medium with viscous heating. A Rosseland flux model is used for radiation heat transfer and Darcy’s model for the isotropic porous medium. The stretching sheet is porous, and wall suction or injection are possible. A robust neural network has been deployed to optimize the physical parameters controlling the transport characteristics of hybrid nanofluids. Specifically, two hybrid nanoparticle combinations are addressed, namely graphite oxide (GO)-molybdenum disulfide ((Formula presented.)) and copper (Cu)-silicon dioxide ((Formula presented.)), both with engine oil as the base fluid. The dimensional boundary layer model is transformed via suitable scaling variables from a partial differential system into a dimensionless non-linear coupled ordinary differential system. The transformed boundary value problem is solved numerically with the BVP4C subroutine in the symbolic software MATLAB, which achieves exceptional accuracy. Validation with previous simpler studies is conducted and a good correlation is obtained. The neural network optimization analysis incorporates Bayesian regularization as the training algorithm. The Bejan entropy generation minimization (EGM) analysis shows that with increasing radiation parameter (Formula presented.) both entropy generation rate and Bejan number are increased. Furthermore, an elevation in Brinkman number (Formula presented.) leads to an upsurge in entropy generation rate and a downtrend in the Bejan number. The numerical solution of the boundary value problem reveals that with an increment in nanoparticle solid volume fraction (Formula presented.) magnetic parameter (Formula presented.) inverse permeability parameter (Formula presented.) surface injection parameter (Formula presented.) Eckert number (Formula presented.) and radiation parameter (Formula presented.) and with a decrement in suction parameter (Formula presented.) and Prandtl number (Formula presented.) there is a strong enhancement in temperature magnitude and thermal boundary layer thickness. With greater nanoparticle solid volume fraction (Formula presented.) magnetic parameter (Formula presented.) inverse permeability parameter (Formula presented.) suction parameter (Formula presented.) and a reduction in thermal buoyancy parameter (Formula presented.) strong flow deceleration is induced, and momentum boundary layer thickness is increased. The skin friction coefficient is substantially boosted with lower values of magnetic parameter (Formula presented.) inverse permeability parameter (Formula presented.) suction parameter (Formula presented.) and higher values of thermal buoyancy parameter (Formula presented.) There is a significant decrement also computed in Nusselt number with a greater radiation parameter (Formula presented.) The simulations provide a good benchmark for future extensions that may consider non-Newtonian behavior.