This research investigates the dynamics of ZnO-SAE50 nanolubricant transport within a flexible convergent/divergent channel while considering the influence of thermal radiation. This comprehensive investigation linked two distinct realms of inquiry. Firstly, it analyzes the impact of thermal radiation on ZnO-SAE50 nanolubricant transport within a convergent/divergent channel featuring stretchable walls, specifically within the context of Jeffery-Hamel flow. Secondly, the research influences an artificial neural network (ANN) empowered by intelligent Bayesian regularization (IBR) to model the nanolubricant flow within the same channel configuration. The nonlinear boundary layer partial differential equations for momentum and thermal energy in convergent/divergent channels with stretchable walls were studied first. We employed similarity transformations to transform these partial differential equations into ordinary differential equations. We used numerical computation methods to create a reference dataset to implement ANN-IBR. Specifically, bvp4c, a built-in scheme in MATLAB software, is applied to generate a representative dataset for ANN-IBR. The ANN-IBR model is simultaneously trained, tested, and validated using numerical calculations. The model's efficiency is assessed using various techniques, such as regression analysis, histogram generation, transition state analysis, and mean square error. The findings are presented through tables and graphs based on physical parameters, providing clear and easily understandable insights into flow and temperature profiles.
