A statistically predictive heat transfer rate model in a radiative magnetized hybrid nanofluid flow with dissipation over a sheet elongating exponentially: Combined RSM & ANOVA

Innovative advancements in the mechanism of heat transfer require the utility of hybridised fluids have revolutionized thermal enhancement due to diversified applications from the cooling of electronic devices to energy systems. The proposed investigation introduces a predictive model for the heat transportation rate obtained statistically in a magnetized hybridised fluid flow via an exponentially elongating sheet. Optimizing thermal performance is a real challenge due to the combined involvement of radiant heat, dissipative heat, particle concentrations of the nanoparticle, suction/injection, etc. Incorporating permeable surface embedding within a porous medium adds enhancing the practical significance for engineering applications such as geothermal energy extraction, chemical processing, etc. The proposed mathematical model for the flow phenomena is re-constructed into dimensionless format due to the involvement of similarity functions further, the coupled set of nonlinear equations is handled numerically ensuring high accuracy. A statistical model is developed for the combined “Response surface methodology (RSM)” and “analysis of variance (ANOVA)” approach in predicting and optimizing the thermal performance of characterizing factors. The predictive model demonstrates several applications in various in industrial processes, including efficient thermal regulating systems utilizing magnetic and porosity. However, important findings are; fluid velocity augments with the enhanced variation of the linear and nonlinear thermal buoyancy and the heat transfer rate is controlled by the inclusion of the resistivity offered by the magnetization and the Darcy number but thermal radiation enhances it.