ANALYSIS OF LAMINAR MIXED CONVECTION IN AN INCLINED SQUARE LID-DRIVEN CAVITY WITH A NANOFLUID BY USING AN ARTIFICIAL NEURAL NETWORK

Abstract

This study has focused on laminar mixed convection in an inclined square ventilated lid-driven cavity filled with a copper−water nanofluid. The governing equations in the two-dimensional space are discretized by using the finite volume method with the SIMPLER algorithm. The effects of independent parameters, including the Richardson number, Reynolds number, inclination angle, and the solid volume fraction of nanoparticles, on the streamlines, isotherm lines, and the average Nusselt number along the heat source have been studied. It is found that both the inclination angle and solid volume fraction, especially the second one, have remarkable effects on the fluid flow and heat transfer characteristics in the cavity. Artificial neural networks (ANN) used to extract a relation involve independent parameters for calculating the Nusselt number. The back propagation-learning algorithm with the tangent sigmoid transfer function is used to train the ANN. Finally, analytical relations for the nanofluid mixed convection in a lid-driven cavity are derived from the available ANN. It is found that the coefficient of multiple determinations (R2) between the real values and ANN results is equal to 0.9999, the maximum error being less than 0.5829 and the mean square error being equal to 5.37 × 10−5.