| Summary: | This study introduces a novel computational technique aimed at enhancing fluid heat transfer capabilities through the integration of hybridized nanoparticles into a fluid matrix, resulting in a graphene–copper water-based hybrid nanofluid. The research focuses on modeling and solving the complex dynamics of radiative hybrid nanofluid flow across a permeable convective surface with simultaneous heat generation. Utilizing a similarity transformation, the model is simplified and subsequently solved using a MATLAB numerical solver. Dual solutions are identified, and their stability is confirmed through rigorous stability analysis. To optimize heat transfer enhancement, the study employs response surface methodology (RSM) to refine key parameters—specifically thermal radiation, heat generation, and the Biot number—with the goal of achieving maximum heat transfer efficiency. Findings indicate a notable increase in heat transfer efficiency when employing a 2% volume fraction of copper in the hybrid nanofluid compared to lower concentrations (1–1.5%). Optimal conditions for the skin friction coefficient and flow bifurcation delay are identified which demonstrates effective control over fluid dynamics. Additionally, strategic adjustments in heat generation and nanoparticle volume fractions lead to significant reductions in fluid temperature, thereby enhancing thermal management efficiency. This research significantly advances the understanding of the thermal performance of hybrid nanofluids under dynamic conditions and provides practical insights for optimizing heat transfer in industrial applications.
|
|---|