Exothermic thermosolutal convection in a nanofluid-filled square cavity with a rotating Z-Fin: ISPH and AI integration

This study explores the combined effects of exothermic chemical reactions and Cattaneo–Christov heat flux on thermosolutal convection within a nanofluid-filled square cavity containing a rotating Z-shaped fin. The incompressible smoothed particle hydrodynamics (ISPH) approach was employed, utilizing boundary particle renormalization to accurately model boundary conditions. An artificial neural network (ANN) model, trained on ISPH simulation data, predicted the average Nusselt number ($ {\text{Nu}}_{\text{avg}} $) and average Sherwood number ($ {\text{Sh}}_{\text{avg}} $) with high accuracy. A dataset comprising 56 data points was used, from which 40 data points were used for training, 8 for validation, and 8 for testing. The Z-shaped fin, centrally positioned, rotates at a fixed angular velocity, maintaining lower temperature and concentration levels, while the cavity's vertical walls exhibit elevated thermal and solutal conditions. Results indicate that the Z-shaped fin's geometry, exothermic reaction rates, and magnetic field strength significantly influence heat and mass transfer and fluid dynamics. For instance, increasing the Hartmann number ($ \text{Ha} $) from 0 to 50 decreased nanofluid velocity by 61.99%, while $ {\text{Nu}}_{\text{avg}} $ and $ {\text{Sh}}_{\text{avg}} $ were reduced by 16.87% and 11.81%, respectively. Additionally, increasing the nanoparticle volume fraction from 0 to 0.15 enhanced $ {\text{Nu}}_{\text{avg}} $ by 22.43% and $ {\text{Sh}}_{\text{avg}} $ by 116.3%. The ANN model, employing the Levenberg–Marquardt algorithm, achieved a coefficient of determination $ R = 0.99994 $ and a mean squared error $ \text{MSE} = 4.21\times {10}^{-6} $, demonstrating its reliability in predicting thermal performance. These findings underscore the study's relevance to applications such as energy systems, refrigeration, and heat exchangers.