Artificial intelligence based evaluation of the tri-hybridized (WS₂+Fe₃O₄+CuS) flow subjected to the thermal performance in porous systems - Sohail Ahmad; Hessa A. Alsalmah (Results in Engineering)
The thermal and flow dynamics of a ternary nanofluid composed of tungsten disulfide (WS₂), magnetite (Fe₃O₄), and copper sulfide (CuS) nanoparticles suspended in thermal oil is comparatively interpreted in the present work. The system operates within a porous medium emphasizing the effects of the ternary nano-composition under induced magnetic field environment. The model Navier-Stokes system represents the fluid flow in porous systems under varying flow regimes incorporating both Darcy (linear flow resistance) and Forchheimer (nonlinear inertial effects) terms. Porous materials enhance heat transfer by increasing the surface area for conduction and convection. Advanced computational and neural network techniques, including a radial basis function (RBF) neural network and successive over-relaxation (SOR) methods, are employed to model and simulate the system's thermal and hydrodynamic performance. Results reveal that the ternary nano-composition WS₂+Fe₃O₄+CuS outperforms traditional binary and single-component nanofluids in terms of thermal performance within porous systems. The rate of heat transfer is noticed to be low when volume fractions of magnetite and copper sulfide particles are taken large. Normal and streamwise velocities are decreasing functions of the Forchheimer as well as porosity parameter. The nano-structured fluid flows through Darcy-Forchheimer porous media are eminent for enhancing heat transfer in applications like heat exchangers, solar collectors, geothermal systems, and enhanced oil recovery.