Artificial Neural Network for the prediction of heat transfer of mixed convection in lid-driven cavity with double vertical or horizontal blocks.*

The capability of using Artificial Neural Network (ANN) to predict Nusselt number for laminar mixed convection, is established. Numerical simulations are carried out in lid-driven square cavity with two internal rectangular blocks, positioned in vertical or horizontal direction. The objective is to predict the optimum distance between the two rectangular blocks W/L that provides the maximum heat transfer coefficient. CFD results are used for training and testing the ANN to predict new cases; thus, saving effort and computation time and validate the obtained numerical results. A wide range of flow and heat transfer parameters are considered. The maximum Nu number obtained for the vertical blocks was at W/L $=$ 0.4 (Re $=$ 500 and Gr $=$ 5 \texttimes 10$^{\mathrm{4}})$ and for the horizontal blocks case was at W/L $=$ 0.3 (Re $=$ 500 and Gr $=$ 5 \texttimes 10$^{\mathrm{4}})$. These numerical results agree well with the ANN predictions; thus, the ANN may help reduce analysis-time and effort.