Bayesian regularization knack-based intelligent networks for thermo-physical analysis of 3D MHD nanofluidic flow model over an exponential stretching surface

Artificial intelligence (AI) knacks are exploited broadly by the research community in the field of engineering and technology to enhance efficacy of the outputs for rapid development of business and society. Therefore, it looks promising to explore or investigate in the novel AI-based integrated numerical computing to study the solution dynamic of computational fluid mechanics problem of utmost significance. Boundary layer methodology is invoked for the mathematical modeling of fluidic system to study the heat and mass transport improvement of nanofluid with base water 3D-MHD flow model with an exponentially stretched surface involving three types of nanoparticles Fe3O4 (magnetite), Ag (silver) and C140H42O20 (graphene oxide). The representative 3D PDEs for nanofluidic system are converted into equivalent nonlinear ODE-based system via suitable similarity transformation. The results of the three nanofluidic models are calculated by applying Adams numerical technique for influential outcomes of aggressing parameters including Hartmann number M, wall stretching ratio P, thermal radiation Rd and heat source/sink hs to observe the effecting profiles of temperature and velocity fields. The obtained numerical data are utilized for Bayesian regularization knack-based intelligent computing method to construct the networks for approximate solution dynamic of nanofluidic models. The worth and value of designed Bayesian regularization knack-based networks (BRKNs) is established by regression index measurements, error histogram studies and convergence curves with negligible level of mean square error (E-12 to E-10) for the exhaustive simulations.

Automated summary

Artificial intelligence techniques are widely used in engineering and technology to enhance efficiency in business and society. This study explores the application of AI-based numerical computing to investigate the heat and mass transport improvement of nanofluids. Mathematical modeling and numerical simulations are conducted to analyze the effect of various parameters on temperature and velocity fields. Bayesian regularization knack-based networks are designed and evaluated for their accuracy and performance in approximating the solution dynamic of nanofluidic models.