Nusselt Number Estimation along a Wavy Wall in an Inclined Lid-driven Cavity using Adaptive Neuro-Fuzzy Inference System (ANFIS)

In this study, an adaptive neuro-fuzzy inference system (ANFIS) was developed to determine the Nusselt number (Nu) along a wavy wall in a lid-driven cavity under mixed convection regime. Firstly, the main data set of input/output vectors for training, checking and testing of the ANFIS was prepared based on the numerical results of the lattice Boltzmann method (LBM). Then, the ANFIS was developed and validated using the randomly selected data series for network testing. The applied ANFIS model has four inputs including Reynolds number (Re), Richardson number (Ri), wavy wall amplitude (A) and inclination angle (θ). Nusselt number (Nu) was the unique output of the ANFIS model. To select the best ANFIS model, the average errors of various architectures for three different data series of training, checking and testing of the main data set are calculated. Results indicated that the developed ANFIS has acceptable performance to predict the Nu number for the cited convection problem. This method can reduce computing time and cost considering acceptable accuracy of results.