Numerical computation of unsteady saddle-point flow of water-based tetra-hybrid nanofluid with mass suction and entropy generation analysis

The rapid progress of highly efficient thermal systems necessitates the development of novel cooling and heat transfer fluids that exceed the constraints of traditional and basic nanofluids. An investigation into the heat transfer and unsteady three-dimensional saddle-point stagnation flow of a water-based tetra-hybrid nanofluid including graphene nanoplatelets (GNP), Al₂O₃, CuO, and TiO₂ is underway in this work. The model assumes transverse magnetic field, thermal radiation, viscous dissipation, suction, Joule heating, and entropy generation in incompressible laminar flow. The governing boundary layer equations are reformulated using similarity variables and solved numerically using MATLAB’s bvp4c solver. The analysis scrutinizes the impacts of unsteadiness, radiation, magnetic field intensity, mixed convection, nanoparticle volume fraction, Brinkman and Eckert numbers, and suction on velocity, temperature, skin friction, Nusselt number, and entropy generation. According to the results, the tetra-hybrid nanofluid has better skin friction and heat transfer than the mono and hybrid nanofluids because of its higher viscosity and effective thermal conductivity. Furthermore, entropy generation escalates with more robust dissipative and radiative processes. Overall, the study finds, tetra-hybrid nanofluids show promise for high-performance thermal management and energy-efficient systems.