Novel design of intelligent Bayesian networks to study the impact of magnetic field and Joule heating in hybrid nanomaterial flow with applications in medications for blood circulation

Abstract

In current study, the effects of magnetic field and Joule heating in flow of (Cu+CuO) hybrid nanomaterial model with blood as a base fluid is analyzed effectively by exploiting novel design of intelligent Bayesian regularization neural networks (IBRNN) for several important physical aspects. Mathematical models of the systems are derived by considering the small Eckert and Prandtl numbers with thermo-physical characteristics of Cu+CuO/blood hybrid nanoliquids while the heat emission/absorption is added in energy equation to maintain the temperature for blood flow. The effects of various parameters of interest like wall deformation rate, Joule heating parameter, magnetic parameter, volume fraction and permeability parameter on velocity and temperature distribution have been studied using viable numerical approach of Adams method as well as presented intelligent Bayesian networks. The reliability of the Bayesian networks is endorsed by convergence plots of MSEs, effective outputs of adaptive controlling parameters of optimization algorithm, error based histograms, and regression statistics for exhaustive simulations studies in case of sundry scenarios.