Unsteady flow of a ternary nanofluid over a slow-rotating disk subject to uniform suction and backpropagated neural network - Varun Kumar, R. S.; Kumar, R. Naveen; Ben Ahmed, Samia; Madhu, J.; Verma, Anjali; Punith Gowda, R. J. (Numerical heat transfer. Part B, Fundamentals, 2024)
The purpose of the proposed research work is to explore the heat source or sink impact on the unsteady three-dimensional flow of ternary-hybrid nanofluid through a rotating disk. The magnetohydrodynamic flow of ternary-hybrid nanofluid under the impact of radiative heat transfer and uniform suction is also discussed in this study. The partial differential equations of the flow problem are reduced into ordinary differential equations by employing apt similarity transformation and solved numerically using the Runge-Kutta Fehlberg fourth-fifth order method. The various nondimensional parameters' effects on velocity and thermal profiles are illustrated using graphs. In addition, a Levenberg Marquardt backpropagated neural network is employed for determining the Nusselt number and skin friction model. The outcomes of the developed Levenberg Marquardt backpropagated neural network models are indicated through the performance metrics. Result reveals that a rise in the suction parameter decreases the velocity profiles. The thermal profile increases with higher values of thermal radiation and heat source/sink parameters. In addition, the presented Levenberg Marquardt backpropagated neural network models' scheme is found to be a perfect tool for estimating heat transfer and surface drag force models.