Prediction of the velocity contours in triangular channel with non-uniform roughness distributions by Adaptive Neuro-Fuzzy Inference System.

Abstract

Triangular channels have different applications in many water and wastewater engineering problems. For this purpose investigating hydraulic characteristics of flow in these sections has great importance. Researchers have presented different prediction methods for the velocity contours in prismatic sections. Most proposed methods are not able to consider the effect of walls roughness, the roughness distribution and secondary flows. However, due to complexity and nonlinearity of velocity contours in open channel flow, there is no simple relationship that can be fully able to exactly draw the velocity contours. In this paper an efficient approach for modeling velocity contours in triangular open channels with non-uniform roughness distributions by Adaptive Neuro-Fuzzy Inference System (ANFIS) has been suggested. For training and testing model, the experimental data including 1703 data in triangular channels with geometric symmetry and non-uniform roughness distributions have been used. Comparing experimental results with predicted values by model indicates that ANFIS model is capable to be used in simulation of local velocity and determining velocity contours and the independent evaluation showed that the calculated values of discharge and depth-averaged velocity from model information are precisely in conformity with experimental values.