Estimation of nanostructured maxwell fluid transport via an elongated convective sheet with variable thermal conductivity: Artificial neural network-assisted modeling

The present study investigates radiative heat transfer and flow characteristics of Maxwell nanofluid transport of along a deformable sheet. The flow under examination incorporates the Tiwari-Das model, thermal radiation, variable thermal conductivity, convective heat, and slip boundary conditions. The governing set of equations is transformed via similarity techniques, and an Artificial Neural Network (ANN) is adopted to get the physical insight. The ANN model demonstrates high accuracy and computational efficiency in predicting the fluid flow and temperature fields under varying physical parameters. Results reveal that a stronger convective term enhanced the velocity and temperature field by 8–10 %, showing its role in a cooling mechanism through latent heat diffusion. A rise in (M) led to a 15–20 % decrease in velocity due to Lorentz force influences, while the temperature field is raised by 12 %, confirming the dual role of the magnetic field. Higher radiation values spurred the temperature to rise by 15–22 %, depicting the supremacy of radiative heat transport in high-temperature regions. Accelerating (ϕ) boosted thermal conductivity and raised the temperature profile by about 18 %, underscoring its effect in increasing heat transfer. An increase in the Maxwell term reduced the peak flow rate by approximately 8–12 %, while the equivalent thermal boundary film thickness increased closely to 10 %, establishing that viscoelastic relaxation time damps velocity but raises heat retention. The ANN model, trained through the Levenberg-Marquardt procedure, attained a regression coefficient R ≈ 0.9991R with a mean square error below 10−6, showing superior exactness compared to traditional computational solvers. The findings offer valuable insights for industrial processes involving polymer extrusion, thermal coatings, and materials processing, where thermal management is critical.