Artificial intelligence investigation of magneto radiated nanofluid under mixed convection

Abstract Thermal radiations study for heat transfer applications using AI based algorithm known as Levenberg Marquardt Back Propagation Method (LMBPM) is an effective area. Hence, the current problem describes the effective performance of nanofluid through the LMBPM. The flow scenario is considered for plate which placed horizontally in the cartesian system. The $$\:A{l}₂{O}₃$$ and water are the functional fluid components in the absence of chemical reactions. The model obtained using the thermophysical relations and transformative functions in the presence of dissipation effects, solar radiations and combined convection. Further, the results furnished using 10 neurons in hidden while 5 neurons in output layer and computed the physical outcomes. The study’s findings reveal that the gravity effect depreciated the thermal boundary layer while the Lorentz forces due to normally acting magnetic field opposes the nanofluid motion. Thermal radiations and Eckert number provided considerable improvement in the temperature. The accuracy and validity of the scheme is studied through validations checks, fitting functions, histogram and regression analysis and found excellent outcomes. The conducted study along with influential physical controls will help to manage the heat transfer with suggested parametric ranges. These will practically applicable in engineering systems particularly in heating and cooling, thermal transport in nano-devices and heat exchangers applications.