Closed-loop AI control of nanoparticle transport under mixed convection: application to electromagnetic propulsion power generation

Abstract

The efficient heat transfer and control of heat flow is essential to electromagnetic propulsion and MHD power generation systems. The present study deals with the effect of magnetic field, thermal radiation, Brownian motion and thermophoresis on the unsteady flow and heat transfer characteristics of a second grade viscoelastic nanofluid flowing over a stretching surface. A physics-informed, closed-loop artificial intelligence framework is developed, integrating a feed-forward artificial neural network for real-time flow field prediction with response surface methodology for global sensitivity analysis and parameter optimization. Results reveal that the second-grade viscoelastic parameter enhances the velocity boundary layer, while increasing magnetic field intensity suppresses fluid motion through Lorentz force retardation. Thermophoretic effects augment thermal energy distribution, whereas increasing Schmidt number progressively diminishes nanoparticle concentration profiles. The model was statistically reliable as confirmed by ANOVA-RSM analysis at 95% confidence level with the R² and Predicted-R² values of 100%. Residual diagnostics showed that the errors were random, and the sensitivity analysis showed that the parameter which has the greatest effect on the heat transfer is thermal radiation, followed by thermophoresis and Brownian motion (p < 0.05). Streamline contour analysis further demonstrates significant topological restructuring of flow patterns under variable magnetic flux densities. The integrated neuro-architecture provides an end-to-end platform for time-dependent optimization, closed-loop control, and deep physical understanding of magneto-plasmonic nanofluid transport in advanced energy systems.