Heat transfer in magnetohydrodynamic Jeffery-Hamel molybdenum disulfide/water hybrid nanofluid flow with thermal radiation: A neural networking analysis

The heat transfer aspects are considered in the Jeffery-Hamel hybrid nanofluid flow between non-parallel plates. In a flow regime, molybdenum disulfide nanoparticles are suspended with a viscous dissipation, magnetic field, and thermal radiation effects. The flow is theoretically modeled, and the differential transformed approach is used to solve the problem. The flow field of the hybrid nanofluid is investigated for different parameters. For the Nusselt number, a sample of 75 values is gathered and compared to five inputs. Fifty-three (70%) values are taken into account for training the neural network. For testing and validating the artificial neural network (ANN) model, 11 (15%) each is used. Using Levenberg-Marquardt backpropagation, the training is carried out. It is noticed that the temperature of MoS2-MWCNTs/H2O is higher in comparison with MoS2/H2O. Further, according to ANN prediction, Pr, Ec, and M have a growing relationship with the Nusselt number; however, Re and Nr have an opposite relationship.