Prediction of flow fields and temperature distributions due to natural convection in a triangular enclosure using Adaptive-Network-Based Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN)

Artificial Neural Network (ANN) and Adaptive-Network-Based Fuzzy Inference System (ANFIS) were used to predict the natural convection thermal and flow variables in a triangular enclosure which is heated from below and cooled from sloping wall while vertical wall is maintained adiabatic. Governing equations of natural convection were solved using finite difference technique by writing a FORTRAN code to generate database for ANN and ANFIS in the range of Rayleigh number from Ra = 104 to Ra = 106 and aspect ratio of triangle AR = 0.5 and AR = 1. Thus, the results obtained from numerical solutions were used for training and testing the ANN and ANFIS. A comparison was performed among the soft programming and Computational Fluid Dynamic (CFD) codes. It is observed that although both ANN and ANFIS soft programming codes can be used to predict natural convection flow field in a triangular enclosure, ANFIS method gives more significant value to actual value than ANN.