Thermodynamic Analysis in Electromagnetic Hybrid Nanofluid Flow Over a Stretching Flat Sheet With Chemical Reaction Using Response Surface Methodology

초록

Hybrid nanofluids exhibit outstanding cooling capabilities in elevated temperature settings, rendering them ideal for numerous thermal applications. Their efficiency encompasses fields such as cooling for electronic devices, heat exchangers, the manufacturing and automotive sectors, heat pipes, and solar power systems. The novelty of this research lies in the entropy analysis of the unsteady 2D flow of an EMHD Casson (GO-MoS2/C2H6O2-H2O) HNF moving over a porous stretching surface while incorporating the effects of chemical reactions and electrical Joule heating. The nonlinear PDEs are transformed into a system of ODEs using similarity transformations and subsequently solved numerically via the KB method in MATLAB. Further, a statistical approach is applied to optimise HT using RSM with central composite design. The research assesses the effects of essential factors on the flow profiles and entropy generation, along with their relevance in engineering applications. The results found that an elevation in the magnetic field and Casson parameters diminishes the nanofluid velocity, while a robust electric field amplifies it in HNF. The temperature of HNF rises with elevated radiation and Eckert number, whereas the NF concentration diminishes as the chemical reaction parameter increases. The entropy rises significantly for the HNF as the Casson fluid, radiation parameters, and Brinkman number increase, whereas the Bejan number diminishes with the same influencing factors. An elevation in the Casson fluid parameter boosts skin friction by 3.52% more in HNFs, while an increase in the electric field parameter diminishes it by 2.25% further in conventional NFs. The Nusselt number experiences a further decrease of 3.4% in NFs as the radiation parameter rises, whereas it exhibits an increase of 2.1% in HNFs with an escalating Biot number. An elevation in the chemical reaction parameter results in a boost in the Sherwood number, with a 2.8% more significant impact noted in HNFs owing to their exceptional transport characteristics. Furthermore, the RSM incorporates the key factors influencing the Nusselt number into a quadratic regression model, which demonstrates excellent predictive capability, as evidenced by an R 2 value of 98.97%.

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