Application of fractional model computations for a mathematical study of unsteady Williamson ternary-Hybrid nanofluid flow with heat transfer past a stretching orthogonal surface

This study introduces a comprehensive computational investatgtion of unsteady mixed convection and heat transport for a non-Newtonian Williamson ternary-hybrid nanofluid flow through an orthogonal stretching surface. To overcome the limitations of classical models, a novel fractional calculus framework is employed to capture the fluid's memory and hereditary properties. The advanced nanofluid consists of a synergistic blend of molybdenum disulfide (MoS₂), multi-walled carbon nanotubes (MWCNTs), and graphene oxide (GO) dispersed in water, engineered for superior thermal performance. The governing fractional differential equations are derived applying similarity transformations and solved numerically. The results demonstrate a remarkable enhancement from the ternary hybrid composition: compared to conventional hybrid nanofluids, the heat transport rate (Nusselt number) is increased by up to 21.5 %, while the skin friction coefficient increases by 16.3 % under assisting flow conditions. Higher Prandtl numbers and nanoparticle concentrations further amplify this performance gain. The inclusion of GO nanoparticles is shown to significantly thin the thermal and velocity boundary layers, thereby intensifying convective efficiency. Furthermore, the fractional order parameter (α) provides precise control over the memory intensity, revealing its profound influence on the flow and temperature fields. The model is rigorously validated against established literature. These findings underscore the superior predictive capability of the fractional approach and the significant potential of the designed ternary-hybrid nanofluid for high-efficiency thermal management systems.