Flow Variables Prediction Using Experimental, Computational Fluid Dynamic andArtificial Neural Network Models in a Sharp Bend

Bend existence causes changes in the flow pattern, velocity and the water surface profile. the ability to simulate three-dimensional flow pattern is an important and significant issues in curved channel. in the present study, using three-dimensional model of computational fluid dynamics (cfd) and artificial neural network (ann) model of multi-layer perceptron (mlp), two velocities and pressure variables on the channel bed with 90º sharp bend is predicted and compared. also extensive experimental work has been conducted to measure the flow variables in this bend. experimental results are used to train and test the neural network model accordingly. comparison of the numerical with experimental results show that cfd model with average root mean square error (rmse), 0.02 and 0.13 and ann model with r^2 (determination coefficient) value, 0.984 and 0.99 to predict velocity and pressure respectively, has reasonable accuracy. also, velocity pattern and flow pressure with both numerical (cfd and ann) models at any point of the field channel is predictable. comparison of the cfd and ann models show that the ann model with the average value of mean absolute error (mae), 0.048 to cfd model with the average mae, 0.06 in prediction of velocity and pressure has more accuracy. the present neural network with less time and cost in designing and implementation of curved channels than other expensive and time consuming experimental and computational models can be used.