Thermal analysis in Darcy-Forchheimer hybrid nanofluid through a Riga plate: An ANN optimization

Abstract

In the present study, a double-hybrid nanofluid's Darcy-Forchheimer flow behavior over a Riga plate with the presence of radiation and heat source impacts is examined. Two distinct nanoparticles as Silicon Carbide (SIC), and Graphite (C) with a base fluid of Ethylene glycol (C2H6O2) are taken into consideration. The model of Cattaneo-Christov heat flux is discussed in the energy and mass equation. The symmetry variables are utilized to remodel the boundary conditions and governing flow models into non-linear ordinary differential equations. The redesigned ordinary differential equations are numerically computed using bvp4c in MATLAB. Graphical representations are used to study how distinct parameters create variations in the concentration, temperature, and velocity profile. The present study shows that the velocity profile decreases with an upsurge in the Hartmann number. The current analysis determined that the fluid velocity declines and the Forchhiemer number (Fr) increases. The fluid concentration increases when temperature temperature-dependent coefficient (B) rises. The significance of emerging parameters on skin friction, Nusselt number, and Sherwood number are computed in tabular shape. Artificial neural networking (ANN) is applied to optimize physical quantities of data, by training, validation, and testing, to check the accuracy of the data.