Study the Affecting Factors on Free overfall Flow and Bed Roughness in Semi-Circular Channels by Artificial Neural Network

Abstract

One of the significant problems facing the water resource engineer is calculating the coefficient of roughness for subsequent design calculations of the discharge amount of a channel or river. In this study, experiments were conducted in a semi-circular, straight channel to investigate the factors affecting bed roughness and flow discharge using Artificial Neural Network (ANN). For this purpose, three semi-circular channel models with free overfall were constructed and installed in a 6-meter-long laboratory flume. The length of these models was 2.50 m with three different diameters (D= 150, 187, and 237mm) and three bed slopes (S=0.004, 0.008, and 0.012). Three sand particle sizes (ds) were used for each semi-circular channel to roughen the bed. The results showed that the Manning roughness coefficient obtained using a rough bed surface was higher than the channel with a smooth bed surface. Also, the results revealed that the Manning roughness coefficient and the Froude number were inversely related. (ANN) analysis showed a good agreement between the experimental and predicted results of flow and roughness. The bring depth (yb) had an 85.8% impact percentage on the free overfall discharge for semi-circular channels, while the bottom slope (S) had only 1.1%.

Keywords

• Artificial Neural Network
• Free overfall
• Manning Roughness Coefficient
• Semi-Circular Channels