Artificial intelligence significantly enhances the application of computational fluid dynamics in solar aircraft design by enabling faster and more accurate simulations of airflow around the aircraft. Expert AI integration allows engineers to analyze complex aerodynamic patterns to improve the effective solar energy utilization and flights durations. This study focuses on a computational fluid dynamics problem examining the thermal analysis of a Prandtl–Eyring hybrid nanofluidic (PE-HNF) model arising in solar aircrafts. A dual-layered nonlinear autoregressive exogenous neural architecture enhanced with Bayesian regularization technique (D-NARX-BRT) is devised to exact the dynamics underlying the PE-HNF solar aircraft model. An Adams-based numerical scheme is deployed to exact the solutions for the PE-HNF, where parameters such as Prandtl–Eyring parameter, Biot number, magnetic parameter, mass transfer parameter, Eckert number, thermal radiation parameter and velocity slip parameters are systematically varied while maintaining fixed values for the Prandtl number. The predictive prowess of the D-NARX-BRT is evaluated against these reference numerical solutions through iterative convergence curves based on mean squared error (MSE), an analysis of adaptive controlling factors, statistical error histogram plots, regression analysis, autocorrelation plots and input-error correlation plots. The D-NARX-BRT technique shows consistent robustness through the 35 experiments as reflected by the low mean squared errors in the range of 10–11 to 10–14. The exhaustive comparative analysis showcases the adeptness of this innovative intelligent computing scheme for the intricate nonlinear differential aerodynamic and energy bottlenecks in solar aviation systems.
