CCD-ANOVA based RSM approach for heat transfer optimization in Cross-Hybrid (MoS2-SiO2) nanofluid flow

Response surface methodology (RSM) integrated with both central composite design (CCD) and analysis of variance (ANOVA) is used to analyse and attain optimal performance of relevant factors. This statistical technique is employed for flow data to create predictive correlations, measure the effects of parameter interactions, and identify optimal operational conditions. This work discussed the thermal performance of a Cross hybrid nanofluid with silicon dioxide (SiO 2 ) and molybdenum disulfide (MoS 2 ) nanoparticles dispersed in water, flowing over a stretching surface. The surface is embedded with a porous medium and an imposed Thompson-Troian outer slip velocity to account for the non-Newtonian characteristics of the fluid. To balance the stability and thermal enhancement of hybrid nanofluid’s composition, we considered a volume fraction of 0.2 and a size of 10-100 nm by following the lateral size of MoS 2 and spherical size of SiO 2 nanoparticles. The governing system of PDEs is converted into ODEs using the appropriate similarity transformation, and MATLAB ”bvp5c” is used to solve these ODEs numerically. The use of Response Surface Methodology (RSM) specifically for the skin-friction coefficient and Nusselt number, which enables the development of accurate regression models that connect the governing parameters with flow resistance, is a significant novelty of this work. A three-factor (CCD) with 20 experimental runs under RSM is used. This model’s validity is confirmed by ANOVA and R 2 statistics. The skin-friction correlation and Nusselt number achieve enhanced R 2 values of 99.96% and 99.99%, respectively, supporting the overall reliability of the created models, and the RSM regression model exhibits remarkable predictive accuracy. The outcome of this work applies to several thermal systems, engineering, and industrial processes that use nanofluid and hybrid nanofluid for heating and cooling procedures. The analysis may not be applicable under unsteady, three-dimensional, or more complex practical conditions.