This study aims to investigate a mixed convection stagnation-point flow and heat transfer of a ternary nanofluid over a vertical linearly stretching/shrinking sheet. The ternary nanofluid consists of aluminium oxide (Al2O3), copper oxide (CuO) and silver (Ag) nanoparticles dispersed in water (H2O). The effects of surface permeability and thermal radiation on flow and thermal characteristics are examined, with emphasis on heat transfer enhancement and parameter optimization.
The governing partial differential equations describing momentum and energy transport are transformed into a system of ordinary differential equations using a similarity transformation approach. The resulting equations are solved numerically in MATLAB using the bvp4c solver. To further evaluate the parameter influence and optimize thermal performance, response surface methodology and normalized sensitivity analysis are employed. A predictive correlation relating key parameters to the heat transfer response is also developed.
The analysis shows that ternary nanofluid yields the highest heat transfer rate compared to hybrid and mono-nanofluids under identical operating conditions. Dual solutions are observed in the shrinking regime which indicates multiple flow states. Furthermore, statistical analysis reveals the relative influence of Al2O3, CuO and Ag volume fractions on the heat transfer rate, where Ag exhibits the strongest influence on the heat transfer rate due to its high thermal conductivity, and the optimized combination of these ternary nanoparticles leads to maximum heat transfer performance.
The combined numerical and statistical framework offers a structured approach for improving thermal transport in nanofluid-based systems. The findings may assist in the design of enhanced cooling and thermal regulation applications where improved heat transfer efficiency is required, as well as contributes to energy efficiency efforts that aligned with sustainable development goals (SDG 7).
This work integrates similarity-based numerical modelling with statistical optimization and normalized sensitivity analysis for a ternary nanofluid system under mixed convection stagnation-point flow. The development of a predictive correlation and systematic parameter ranking also provides further information into multi-nanoparticle heat transfer behaviour.
