An Innovative Approach for Study of Thermal Behavior of an Unsteady Nanofluid Squeezing Flow between Two Parallel Plates Utilizing Artificial Neural Network

Abstract

This study reveals the thermal behavior of an unsteady nanofluid streaming between two parallel plates by using artificial neural network (ANN). Initially, a similarity solution is employed to simplify the partial differential equations (PDSs) and convert them into a system of coupled nonlinear ordinary differential equations (ODEs). Subsequently, a numerical analysis is undertaken to verify the predicted results applying forth order Runge Kutta method. ANN is utilized to provide a nonlinear map between the considered input parameters such as solid volume fraction (Φ), Eckert number (Ec) and a moving parameter which represents the movement of the parallel plates (S), and output parameters like Nusselt number (Nu). Considering the accuracy of the current results, it is concluded that ANN method can be a potential reliable approach for function approximation. Results indicate that an optimal network with 16 neurons exists in hidden layer for which the value of RMSE for testing data is found to be 0.001364.

Heat and mass transfer on MHD squeezing flow of Jeffrey nanofluid in horizontal channel through permeable medium

“…Sheikholeslami et al [36] initiated the research on time dependent squeeze flow of nanofluid within two surfaces with viscous dissipation using Tiwari and Das model. The similar problem was reported from Pourmehran et al [37] and Gorgani et al [38] by implementing different methods. Acharya et al [39] extended the previous problem with magnetic field impact.…”