Statistical approach on optimizing heat transfer rate for Au/Fe3O4- blood nanofluid flow with entropy analysis used in drug delivery system

The current study focuses on utilizing an advanced statistical concept for optimizing rate of heat transfer in a micropolar nanofluid flow within a squeezing channel. The study employs RSM to plan experiments and analyze the role of distinct constraints on HT performance. Moreover, the inclusion of dissipative heat due to the interaction of applied magnetic field along with thermal radiation enriches the study. The proposed designed model is transformed to its non -dimensional form using appropriate similarity rules and then numerical simulation is presented to solve the set of formulated problem. However, the irreversibility process of the system is assessing by incorporating the entropy analysis. The validation along with the characteristic of the contributing factor is presented via the solution of the present design. The outcomes reveals that enhanced volume fraction generally increases viscosity of the fluid which resulted in a strong retardation in the fluid velocity. However, the entropy rate augments with an increasing Br. The results provide the optimal conditions for HTR for both of the nanofluids, which is relevant for applications in drug delivery systems. The advancement in optimizing HTR using RSM and the regression analysis using ANOVA (analysis of variance) may have potential implications for biomedical engineering.