Azadeh Gholami - Ph.D. Student, Department of Civil Engineering, Razi University, Kermanshah, Iran.
Salma Ajeel - M.Sc. Student, Department of Civil Engineering, Razi University, Kermanshah, Iran
Hossein Bonakdari - Associate Prof, Department of Civil Engineering, Razi University, Kermanshah, Iran.
Ali Akbar akhtari - Assistant Prof, Department of Civil Engineering, Razi University, Kermanshah, Iran

This paper presents an experimental and numerical study of the flow patterns in a strongly-curved 60º open channel bend. Corresponding numerical model is based on the computational fluid dynamics (CFD) and artificial neural network (ANN) method. The use of artificial intelligence methods in different hydraulic sciences has become conventional in recent years. In this study, the Fluent CFD model with k-ε (RNG) turbulence model is used to simulate turbulent flow parameters and compared of flow pattern in a 60° sharp bend by using ANN Methods. The results show that, enjoying low error values, the Fluent model has an acceptable level of consistency with the available experimental results. ANN model can predict velocity pattern fairy accurately. The error values of Fluent and ANN models are smaller in the outer wall (contraction zones) in comparison with the inner wall (separation zone). It could therefore be said that the error value is greater in highvelocity areas (erosion- prone areas) than in low- velocity areas (sedimentationprone areas). The error value is very small in the cross sections located after the bend in two models. The models comparison shows that the ANN model with root mean square error (RMSE) equal 2.6 is more accurate than Fluent model with 4.43 error in velocity prediction at a 60° bend.

60° sharp bend, Fluent model, ANN model, flow pattern, velocity prediction