Design of an adaptive neuro-fuzzy computing technique for predicting flow variables in a 90° sharp bend

Investigating flow patterns in sharp bends is more essential than in mild bends due to the complex behaviour exhibited by sharp bends. Flow variable prediction in bends is among several concerns of hydraulics scientists. In this study, the adaptive neuro-fuzzy inference system (ANFIS) is applied to predict axial velocity and flow depth in a 90 degrees sharp bend. The experimental velocity and flow depth data for five discharge rates of 5, 7.8, 13.6, 19.1 and 25.3 L/s are used for training and testing the models. In ANFIS training, the two algorithms employed are back propagation (BP) and a hybrid of BP and least squares. In model design, the grid partitioning (GP) and sub-clustering methods are used for fuzzy inference system generation. The results indicate that ANFIS-GP-Hybrid predicts velocity best followed by flow depth.