Abstract
AbstractThis study applies advanced AI techniques, including machine learning algorithms, to explore the numerically unsteady laminar flow of viscous and incompressible fluids in coaxially swirled porous disks, with applications in engineering sciences. Our focus encompasses the effects of magnetic hybrid nanomaterials and the dynamic behaviors associated with expanding/contracting and injection/suction. Utilizing single‐phase simulations, we address nonlinear coupled ordinary differential equations set against appropriate boundary conditions. Key parameters of our study include permeability and relaxation, as well as the influence of chemical reactions and mixed convection on fluid behaviors. The thermophysical properties of Al2O3/Cu nanoparticles have been by varying their morphological aspects. For the hybrid nanofluids flow, the aggregation of nanoparticle volume fraction has been designed critically in conjunction with an energy and mass transfer equation. Because dimensionless ordinary differential equations are employed, the obtained expression is transmuted using the obliging transformation technique. The desired nonlinear system of ODEs is implemented using an accurate numerical method. Our findings reveal significant impacts of chemical reaction parameters on the Sherwood number and a marked increase in the skin friction coefficient, Nusselt number, and Sherwood number as nanoparticle volume fraction rises from 2% to 7%.
Published Version
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