Response Surface Method-Based Optimization of Thermal Transfer Rate in Hybrid Nanofluid Flow Over a Porous Stretching Sheet with Multiple Slip Condition

Innovative progress in heat transfer mechanisms has driven the use of hybridized nanofluids, significantly improving thermal performance across a wide range of applications, from electronic cooling systems to the optimization of thermal energy storage technologies. The proposed study develops a statistical model for predicting thermal transfer rate determined in a hybrid nanofluid flow over a stretching sheet. Maximizing thermal efficiency presents a challenge due to the simultaneous influence of a non-uniform heat source, the presence of two different nanoparticles, and multiple slip conditions. Integrating with a porous medium enhances its real-world relevance for engineering applications, including renewable energy extraction, industrial processing, and thermal energy storage. The developed mathematical fluid flow phenomena are transformed into a dimensionless form using similarity transformation. Furthermore, the nonlinear equations are solved numerically to ensure high accuracy. In a novel way, a statistical model is formulated by integrating the Response Surface Method (RSM) with examination of variance (ANOVA) to predict and optimize the thermal transfer performance of key influencing factors. The predictive model has diverse applications in industrial processes, particularly in significant thermal regulation systems incorporating the effects of porosity. However, a significant finding is that the study utilizes RSM for simultaneous optimization of multiple parameters, identifying optimal conditions for enhancing heat transfer rate. The estimated R2 (97.62%) validates the heat transfer rate model's excellent predictive accuracy, which makes it reliable for predicting results.