Statistical Box-Behnken driven response surface modeling for thermal performance enhancement of radiative tri hybrid nanofluid transport over a porous heated material

Abstract

In various industrial production processes, the system needs significant cooling so that it can produce several products without maximum damage. The superior heat conduction performance exhibited by the tri-nanofluid leads to its more prominent performance relative to single and bi-hybrid nanofluids. In conveying the same approach, the present investigation provides a meaningful role in governing the flow patterns of tri-hybrid nanofluid comprised of alumina (Al2O3), zinc oxide (ZnO), and magnetite (Fe3O4) in the base liquid water is considered. The flow of conducting fluid for the involvement of the applied magnetization through a permeable medium is carried out with the mutual influence of thermal radiation and an additional heat source. Additionally, the main focus of the study is the utility of multiple slip conditions and their impact on the flow phenomena. The mathematical model integrating with the aforesaid assumptions leads to presenting a set of nonlinear coupled systems, and then a suitable similarity rule is employed in transforming the model into its dimensional form. Moreover, a traditional numerical approach, such as fourth-order Runge-Kutta embedding with shooting, is utilized in handling the system with the characterizing factors. Further, it is observed that the tri-hybrid nanofluid exhibits a stronger ability in enhancing the fluid velocity and the heat transport properties. Again, radiative heat transfer using thermal radiation augments the temperature distribution. One of the interesting statistical approaches, i.e. Response surface methodology associated with Box-Behnken design (BBD) with face-centred approach and validation is obtained for the use of analysis of variance (ANOVA), a hypothetical test considering 5% of the significance level. The 99% of R-squared value presents that the model designed for the heat transfer rate for the involved factors shows a significant model. Clinical trial number: Not applicable.