A semi analytical simulation and Bayesian Regularization technique for thermophoretic particle deposition in radiative nanofluid flow with Brownian motion and Stefan blowing impacts.

By using the computational power of Bayesian Regularization Optimizer Supervised Neural Networks (BRO-SNNs), this semi-analytical model, which investigates thermophoretic particle deposition in a Boger nanofluid flow over an inclined sheet using Brownian motion and Stefan blowing, has important applications in advanced heat management and materials processing. It provides a key framework for enhancing heat exchanger designs, demonstrating how controlled nanoparticle deposition can improve heat transfer or avoid surface fouling. The findings are immediately applicable to coating and thin-film manufacturing processes, allowing for fine control over deposit uniformity and thickness by manipulating thermophoresis and Stefan blowing. Furthermore, the model can be used in environmental engineering to estimate particle deposition in scrubbers and filtration systems, as well as in aircraft to manage dust or ice formation on critical components. The viscoelastic character of Boger fluids makes it especially useful for creating next-generation smart cooling systems based on polymer-based nanofluids. The HAM solves the non-dimensional non-linear governing ODEs (ordinary differential equations) that are derived following appropriate similarity transformations. The velocity field upsurges as increase the values of nanoparticles solvent friction while thermal profile decline.