Sensitivity analysis of boundary layer flow and thermal optimization over a porous wedge using unsteady experimental nanofluid and Darcy-Brinkman model

This study investigates the boundary layer flow (BLF) of Al₂O₃-H₂O nanofluid (NF) across a moving permeable wedge. The unsteady NF contain containing 36 nm nanomaterials and H₂O as a base fluid. The goal of the study is to optimize an ideal hydraulic BLF and thermal behaviors with the deployment of experimental NF for advanced industrial turbomachinery and advanced electronic cooling devices. The Darcy-Brinkman model and the modified Buongiorno model are employed for this purpose. The thermophysical characteristics of Al₂O₃-H₂O NF is deployed using derived correlations of experimental data. The response surface methodology (RSM) is implemented to scrutinize the surface drag and to optimize the heat and mass transmission rate of NF. The statistical analysis of variance (ANOVA), sensitivity analysis, and RSM are implemented for surface fluxes optimization. The controlling system of the Falkner-Skan equations are numerically solved using Bvp4c methodology. The findings suggest that Al₂O₃ nanoparticle reduces the skin friction up to 87.86%. The thermophoresis parameter and volume fraction expand the thermal BLs. Furthermore, the porosity parameter uplifts the temperature of the NF, while an opposite behavior was seen for flow field. The heat transfer is optimized by an optimal combination of the wedge angle and nanoparticle concentration, while mass transfer is mostly controlled by the wedge geometry.